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Type:	Package
Title:	Historical and Ahistorical Population Projection Matrix Analysis
Version:	6.6.0
Date:	2025-10-28
Description:	Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices.
Encoding:	UTF-8
License:	GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/dormancy1/lefko3
Imports:	Rcpp (≥ 1.0.5), glmmTMB, lme4, MASS, Matrix, methods, MuMIn,pscl, rlang, stats, VGAM, grDevices
LinkingTo:	Rcpp, RcppArmadillo, BH
LazyData:	true
BugReports:	https://github.com/dormancy1/lefko3/issues
RoxygenNote:	7.3.3
Suggests:	knitr, popbio, rmarkdown, Rcompadre
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2025-10-27 21:07:52 UTC; richpshefferson
Author:	Richard P. Shefferson [aut, cre], Johan Ehrlen [aut]
Maintainer:	Richard P. Shefferson <cdorm@g.ecc.u-tokyo.ac.jp>
Depends:	R (≥ 3.5.0)
Repository:	CRAN
Date/Publication:	2025-10-28 06:10:32 UTC

Historical and Ahistorical Population Projection Matrix Analysis

Description

This package creates population matrix projection models (MPMs)for use in population ecological analyses. It presents a complete workingenvironment for the construction and analysis of ALL kinds of MPMs and IPMs,including age, stage, and age-by-stage versions. Its specialty is theestimation of historical MPMs, which are 2d matrices comprising 3 monitoringoccasions (2 time steps or periods) of demographic information. The packageconstructs both function-based and raw MPMs for both standard ahistorical(i.e. 2 occasions, 1 time step) and historical analyses, has functions forcomplex density-dependent and independent, and stochastic and cyclical,projections, and also includes the automatic calculation of quality controlmetrics throughout every step of analysis. It also includes powerfulfunctions to standardize demographic datasets.

Details

The lefko3 package provides seven categories of functions:

1. Data transformation and handling functions

2. Functions determining population characteristics from vertical data

3. Model building and selection

4. Matrix / integral projection model creation functions

5. Population dynamics analysis and projection functions

6. Functions describing, summarizing, or visualizing MPMs and derivedstructures

7. Extra functions used to illustrate core theory and ideas.

lefko3 also includes example datasets complete with sample code.

Author(s)

Maintainer: Richard P. Sheffersoncdorm@g.ecc.u-tokyo.ac.jp (ORCID)

Authors:

• Johan Ehrlen (ORCID)

Richard P. Shefferson <cdorm@g.ecc.u-tokyo.ac.jp>

Johan Ehrlén

References

Shefferson, R.P., J. Ehrlen, and S. Kurokawa. 2021.lefko3: analyzing individual history through size-classified matrix population models.Methods in Ecology and Evolution 12(2): 378-382.

See Also

Useful links:

• https://github.com/dormancy1/lefko3

• Report bugs athttps://github.com/dormancy1/lefko3/issues

Calculate Actual Stage, Age, Stage-Pair, or Age-Stage Distributions

Description

Functionactualstage3() shows the frequencies and proportions ofeach stage, stage pair, age-stage, or age in each year.

Usage

actualstage3(  data,  check_stage = TRUE,  check_age = FALSE,  historical = FALSE,  year2 = NULL,  indices = NULL,  stagecol = NULL,  agecol = NULL,  remove_stage = NULL,  t1_allow = NULL)

Arguments

Value

A data frame with the following variables:

Notes

This function produces frequencies and proportions of stages in hfv formatteddata using stage index variables rather than stage name variables, and sorequires the former. The latter is only required if the user wants to knowthe associated stage names.

Frequencies and proportions will be calculated for all times, including thelast time, which is generally found in thestage3 columns of the lastyear2 entry in objectdata. The default is to treat theyear2 entry for that time asmax(year2) + 1.

Ifcheck_stage = TRUE andcheck_age = FALSE, then this functionwill assess frequencies and proportions of stages or historical stage-pairs.If bothcheck_stage = TRUE andcheck_age = TRUE, then thisfunction will assess frequencies and proportions of age-stages. Ifcheck_stage = FALSE andcheck_age = TRUE, then the frequenciesand proportions of ages only will be assessed.

Note that no stageframe is required for this function to operate. Stagenames and their order are inferred directly from the objectdata.

Examples

sizevector <- c(0, 0, 0, 0, 0, 0, 1, 3, 6, 11, 19.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1.5, 1.5, 3.5, 5)comments <- c("Dormant seed", "1st yr protocorm", "2nd yr protocorm",  "3rd yr protocorm", "Seedling", "Dormant adult",  "Extra small adult (1 shoot)", "Small adult (2-4 shoots)",  "Medium adult (5-7 shoots)", "Large adult (8-14 shoots)",  "Extra large adult (>14 shoots)")cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,   binhalfwidth = binvec, comments = comments)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,   patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE, age_offset = 4)all_stage_props_ah <- actualstage3(cypraw_v1)all_stage_props_h <- actualstage3(cypraw_v1, historical = TRUE)all_stage_props_h_NANotAllow <- actualstage3(cypraw_v1, historical = TRUE,  t1_allow = "none")all_stage_props_as <- actualstage3(cypraw_v1, check_age = TRUE)all_age_props <- actualstage3(cypraw_v1, check_stage = FALSE,  check_age = TRUE)

Add Matrices to a lefkoMat or lefkoMatList Object

Description

Functionadd_lM() adds matrices tolefkoMat andlefkoMatList objects.

Usage

add_lM(  lM,  Amats = NA,  Umats = NA,  Fmats = NA,  UFdecomp = FALSE,  entrystage = 1,  pop = NA,  patch = NA,  year = NA)

Arguments

Value

AlefkoMat orlefkoMatList object incorporating the newmatrices within the object input inlM. Note that if alefkoMatList object is used as input, then ALL compositelefkoMat objects will have the same matrices added in exactly the sameway.

Notes

This function will not allow matrices of different dimension from those inputin objectlM to be added to that object.

Two ofAmats,Umats, andFmats must be provided for thisfunction to proceed. Also, ifAmats,Umats, andFmatsare all provided, then this function will default to replacingAmatswith the sum of the respectiveUmats andFmats.

See Also

create_lM()

delete_lM()

subset_lM()

Examples

# These matrices are of 9 populations of the plant species Anthyllis# vulneraria, and were originally published in Davison et al. (2010) Journal# of Ecology 98:255-267 (doi: 10.1111/j.1365-2745.2009.01611.x).sizevector <- c(1, 1, 2, 3) # These sizes are not from the original paperstagevector <- c("Sdl", "Veg", "SmFlo", "LFlo")repvector <- c(0, 0, 1, 1)obsvector <- c(1, 1, 1, 1)matvector <- c(0, 1, 1, 1)immvector <- c(1, 0, 0, 0)propvector <- c(0, 0, 0, 0)indataset <- c(1, 1, 1, 1)binvec <- c(0.5, 0.5, 0.5, 0.5)anthframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)# POPN C 2003-2004XC3 <- matrix(c(0, 0, 1.74, 1.74,0.208333333, 0, 0, 0.057142857,0.041666667, 0.076923077, 0, 0,0.083333333, 0.076923077, 0.066666667, 0.028571429), 4, 4, byrow = TRUE)# 2004-2005XC4 <- matrix(c(0, 0, 0.3, 0.6,0.32183908, 0.142857143, 0, 0,0.16091954, 0.285714286, 0, 0,0.252873563, 0.285714286, 0.5, 0.6), 4, 4, byrow = TRUE)# 2005-2006XC5 <- matrix(c(0, 0, 0.50625, 0.675,0, 0, 0, 0.035714286,0.1, 0.068965517, 0.0625, 0.107142857,0.3, 0.137931034, 0, 0.071428571), 4, 4, byrow = TRUE)# POPN E 2003-2004XE3 <- matrix(c(0, 0, 2.44, 6.569230769,0.196428571, 0, 0, 0,0.125, 0.5, 0, 0,0.160714286, 0.5, 0.133333333, 0.076923077), 4, 4, byrow = TRUE)XE4 <- matrix(c(0, 0, 0.45, 0.646153846,0.06557377, 0.090909091, 0.125, 0,0.032786885, 0, 0.125, 0.076923077,0.049180328, 0, 0.125, 0.230769231), 4, 4, byrow = TRUE)XE5 <- matrix(c(0, 0, 2.85, 3.99,0.083333333, 0, 0, 0,0, 0, 0, 0,0.416666667, 0.1, 0, 0.1), 4, 4, byrow = TRUE)mats_list <- list(XC3, XC4, XC5, XE3, XE4, XE5)yr_ord <- c(1, 2, 3, 1, 2, 3)pch_ord <- c(1, 1, 1, 2, 2, 2)anth_lefkoMat <- create_lM(mats_list, anthframe, hstages = NA,  historical = FALSE, poporder = 1, patchorder = pch_ord, yearorder = yr_ord)  XH3 <- matrix(c(0, 0, 0.1125, 1.05,0.2, 0, 0, 0,0, 0.5, 0, 0,0.2, 0.5, 0, 0), 4, 4, byrow = TRUE)XH3u <- matrix(c(0, 0, 0, 0,0.2, 0, 0, 0,0, 0.5, 0, 0,0.2, 0.5, 0, 0), 4, 4, byrow = TRUE)XH4 <- matrix(c(0, 0, 0, 0,0, 0, 0.5, 0,0.8, 0.5, 0.25, 0.25,0.2, 0, 0, 0.75), 4, 4, byrow = TRUE)XH4u <- matrix(c(0, 0, 0, 0,0, 0, 0.5, 0,0.8, 0.5, 0.25, 0.25,0.2, 0, 0, 0.75), 4, 4, byrow = TRUE)XH5 <- matrix(c(0, 0, 0.2, 1.05,0, 0, 0, 0,0.001, 0.001, 0.333333333, 0,0.001, 0, 0, 0), 4, 4, byrow = TRUE)XH5u <- matrix(c(0, 0, 0, 0,0, 0, 0, 0,0.001, 0.001, 0.333333333, 0,0.001, 0, 0, 0), 4, 4, byrow = TRUE)anth_lefkoMat <- add_lM(anth_lefkoMat, Amats = list(XH3, XH4, XH5),  Umats = list(XH3u, XH4u, XH5u), patch = c(3, 3, 3), year = c(1, 2, 3))

Add a New Stage to an Existing lefkoMat or lefkoMatList Object

Description

Functionadd_stage() adds a new stage to an existinglefkoMatorlefkoMatList object. In addition to altering theahstagesobject within the MPM, it alters thehstages andagestagesobjects and adds the appropriate number of new rows and columns depending onthe kind of MPM input. Note that, if entering alefkoMatList object,then a stage will be added to alllefkoMat objects contained therein.

Usage

add_stage(mpm, add_before = 0L, add_after = 0L, stage_name = NULL)

Arguments

Value

A new copy of the original MPM edited to include new rows andcolumns in the associated matrices, and withahstages,agestages, andhstages objects edited to include the newstage.

See Also

edit_lM()

Examples

data(cypdata)cyp_lesl_data <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,   patchidcol = "patch", individcol = "plantid", blocksize = 4,   sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",   repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",   stagesize = "sizeadded", NAas0 = TRUE, age_offset = 2)cyp_survival <- glm(alive3 ~ obsage + as.factor(year2), data = cyp_lesl_data,  family = "binomial")cyp_fecundity <- glm(feca2 ~ 1 + obsage + as.factor(year2),  data = cyp_lesl_data, family = "poisson")mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[22] <- "obsage"germination <- 0.08protocorm_to_seedling <- 0.10seeding_to_adult <- 0.20seeds_per_fruit <- 8000cyp_lesl_supp <- supplemental(historical = FALSE, stagebased = FALSE,  agebased = TRUE, age2 = c(1, 2), type = c(1, 1),  givenrate = c(protocorm_to_seedling, seeding_to_adult))cyp_lesl_fb_mpm <- fleslie(data = cyp_lesl_data, surv_model = cyp_survival,  fec_model = cyp_fecundity, paramnames = mod_params, last_age = 7,  fecage_min = 3, fecmod = (germination * seeds_per_fruit),  supplement = cyp_lesl_supp)altered1 <- add_stage(cyp_lesl_fb_mpm, add_before = 1, stage_name = "DS")

Create Function-based Ahistorical Age x Stage Matrix Projection Model

Description

Functionaflefko2() returns ahistorical age x stage MPMs correspondingto the patches and occasions given, including the associated componenttransition and fecundity matrices, data frames detailing the characteristicsof ahistorical stages and the exact age-stage combinations corresponding torows and columns in estimated matrices, and a data frame characterizing thepatch and occasion combinations corresponding to these matrices.

Usage

aflefko2(  year = "all",  patch = "all",  stageframe,  supplement = NULL,  repmatrix = NULL,  overwrite = NULL,  data = NULL,  modelsuite = NULL,  surv_model = NULL,  obs_model = NULL,  size_model = NULL,  sizeb_model = NULL,  sizec_model = NULL,  repst_model = NULL,  fec_model = NULL,  jsurv_model = NULL,  jobs_model = NULL,  jsize_model = NULL,  jsizeb_model = NULL,  jsizec_model = NULL,  jrepst_model = NULL,  jmatst_model = NULL,  paramnames = NULL,  inda = NULL,  indb = NULL,  indc = NULL,  annua = NULL,  annub = NULL,  annuc = NULL,  surv_dev = 0,  obs_dev = 0,  size_dev = 0,  sizeb_dev = 0,  sizec_dev = 0,  repst_dev = 0,  fec_dev = 0,  jsurv_dev = 0,  jobs_dev = 0,  jsize_dev = 0,  jsizeb_dev = 0,  jsizec_dev = 0,  jrepst_dev = 0,  jmatst_dev = 0,  density = NA,  fecmod = 1,  random.inda = FALSE,  random.indb = FALSE,  random.indc = FALSE,  final_age = NA,  continue = TRUE,  prebreeding = TRUE,  negfec = FALSE,  ipm_method = "CDF",  reduce = FALSE,  simple = FALSE,  err_check = FALSE,  exp_tol = 700,  theta_tol = 1e+08,  sparse_output = FALSE)

Arguments

Value

If the user inputs a standardlefkoMod orvrm_inputobject in argumentmodelsuite, or individual vital rate models areinput separately,then this function will return an object of classlefkoMat. If the user inputs an object of classlefkoModListin argumentmodelsuite, then the output will be an object of classlefkoMatList, in which each element is an object of classlefkoMat.

AlefkoMat object is a list that holds one full matrix projectionmodel and all of its metadata. The structure has the following elements:

Notes

Unlikerlefko2(),rlefko3(),arlefko2(), andrleslie(), this function does notcurrently distinguish populations. Users wishing to use the same vital ratemodels across populations should label them as patches (though we do notadvise this approach, as populations should typically be treated asstatistically independent).

This function will yield incorrect estimates if the models utilizedincorporate state in occasiont-1. Only use models developed testingfor ahistorical effects.

The default behavior of this function is to estimate fecundity with regardsto transitions specified via associated fecundity multipliers in thesupplement. If this field is left empty, then fecundity will beestimated at full for all transitions leading from reproductive stages toimmature and propagule stages.

Stageframes used in this function should include ages for minimum and maximumage for each stage.NAs are treated as0s in minimum age, andasfinal_age for maximum age.

Users may at times wish to estimate MPMs using a dataset incorporatingmultiple patches or subpopulations, but without discriminating between thosepatches or subpopulations. Should the aim of analysis be a general MPM thatdoes not distinguish these patches or subpopulations, themodelsearch() run should not include patch terms.

Input options including multiple variable names must be entered in the orderof variables in occasiont+1 andt. Rearranging the order willlead to erroneous calculations, and may lead to fatal errors.

Care should be taken to match the random status of year and patch to thestates of those variables within themodelsuite. If they do not match,then they will be treated as zeroes in vital rate estimation.

Theipm_method function gives the option of using two different meansof estimating the probability of size transition. The midpoint method("midpoint") refers to the method in which the probability isestimated by first estimating the probability associated with transition fromthe exact size at the midpoint of the size class using the correspondingprobability density function, and then multiplying that value by the binwidth of the size class. Doak et al. 2021 (Ecological Monographs) noted thatthis method can produce biased results, with total size transitionsassociated with a specific size not totaling to 1.0 and even specific sizetransition probabilities capable of being estimated at values greater than1.0. The alternative and default method,"CDF", uses the correspondingcumulative density function to estimate the probability of size transition asthe cumulative probability of size transition at the greater limit of thesize class minus the cumulative probability of size transition at the lowerlimit of the size class. The latter method avoids this bias. Note, however,that both methods are exact and unbiased for the Poisson and negativebinomial distributions.

Under the Gaussian and gamma size distributions, the number of estimatedparameters may differ between the twoipm_method settings. Becausethe midpoint method has a tendency to incorporate upward bias in theestimation of size transition probabilities, it is more likely to yield non-zero values when the true probability is extremely close to 0. This willresult in thesummary.lefkoMat function yielding higher numbers ofestimated parameters than theipm_method = "CDF" yields in some cases.

Using theerr_check option will produce a matrix of 7 columns, eachcharacterizing a different vital rate. The product of each row yields anelement in the associatedU matrix. The number and order of elementsin each column of this matrix matches the associated matrix in column vectorformat. Use of this option is generally for the purposes of debugging code.

Individual covariates are treated as categorical only if they are set asrandom terms. Fixed categorical individual covariates are currently notallowed. However, such terms may be supplied if themodelsuite optionis set to avrm_input object. In that case, the user should also setthe logical random switch for the individual covariate to be used toTRUE (e.g.,random.inda = TRUE).

See Also

mpm_create()

flefko3()

flefko2()

fleslie()

arlefko2()

rlefko3()

rlefko2()

rleslie()

Examples

data(lathyrus)sizevector <- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,  9)stagevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",  "Sz5nr", "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r",  "Sz4r", "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")repvector <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)obsvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)matvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0)indataset <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)minima <- c(1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)binvec <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,  0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)lathframeln <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector, minage = minima)lathvertln <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "Intactseed88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframeln,  stagesize = "sizea", censorcol = "Missing1988", censorkeep = NA,  NAas0 = TRUE, censor = TRUE)lathvertln$feca2 <- round(lathvertln$feca2)lathvertln$feca1 <- round(lathvertln$feca1)lathvertln$feca3 <- round(lathvertln$feca3)lathvertln_adults <- subset(lathvertln, stage2index > 2)surv_model <- glm(alive3 ~ obsage + sizea2 + as.factor(patchid) +  as.factor(year2), data = lathvertln_adults, family = "binomial")obs_data <- subset(lathvertln_adults, alive3 == 1)obs_model <- glm(obsstatus3 ~ obsage + as.factor(patchid) +  as.factor(year2), data = obs_data, family = "binomial")size_data <- subset(obs_data, obsstatus3 == 1)siz_model <- lm(sizea3 ~ sizea2 + repstatus2 + obsage + as.factor(patchid) +  as.factor(year2), data = size_data)reps_model <- glm(repstatus3 ~ sizea2 + as.factor(patchid) + as.factor(year2),  data = size_data, family = "binomial")fec_data <- subset(lathvertln_adults, repstatus2 == 1)fec_model <- glm(feca2 ~ sizea2 + obsage + as.factor(patchid) +  as.factor(year2), data = fec_data, family = "poisson")lathvertln_juvs <- subset(lathvertln, stage2index < 3)jsurv_model <- glm(alive3 ~ as.factor(patchid), data = lathvertln_juvs,  family = "binomial")jobs_data <- subset(lathvertln_juvs, alive3 == 1)jobs_model <- glm(obsstatus3 ~ 1, family = "binomial", data = jobs_data)jsize_data <- subset(jobs_data, obsstatus3 == 1)jsiz_model <- lm(sizea3 ~ as.factor(year2), data = jsize_data)jrepst_model <- 0jmatst_model <- 1lathsupp2 <- supplemental(stage3 = c("Sd", "Sdl", "mat", "Sd", "Sdl"),   stage2 = c("Sd", "Sd", "Sdl", "rep", "rep"),  eststage3 = c(NA, NA, "mat", NA, NA),  eststage2 = c(NA, NA, "Dorm", NA, NA),  givenrate = c(0.345, 0.054, NA, NA, NA),  multiplier = c(NA, NA, 0.8, 0.345, 0.054), type = c(1, 1, 1, 3, 3),  stageframe = lathframeln, historical = FALSE, agebased = TRUE)mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[3] <- "patchid"mod_params$modelparams[5] <- "obsstatus3"mod_params$modelparams[6] <- "sizea3"mod_params$modelparams[9] <- "repstatus3"mod_params$modelparams[11] <- "feca2"mod_params$modelparams[12] <- "sizea2"mod_params$modelparams[18] <- "repstatus2"mod_params$modelparams[22] <- "obsage"lathmat2age2 <- aflefko2(year = "all", patch = "all", data = lathvertln,  stageframe = lathframeln, supplement = lathsupp2, final_age = 3,  surv_model = surv_model, obs_model = obs_model, size_model = siz_model,  repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,  jobs_model = jobs_model, jsize_model = jsiz_model,  jrepst_model = jrepst_model, jmatst_model = jmatst_model,  paramnames = mod_params, continue = TRUE, reduce = FALSE)

Matrix Set ofAnthyllis vulneraria Populations in Belgium

Description

AlefkoMat object containing projection matrices developed fromdemographic data gathered on nineAnthyllis vulneraria populationsfrom 2003 to 2006 in southwestern Belgium.

Usage

data(anthyllis)

Format

AlefkoMat object holding 27 matrices. The structure of theobject is as below:

A

The 27 A matrices.

U

The 27 survival-transition matrices used to develop the Amatrices.

F

The 27 fecundity matrices used to develop the A matrices.

hstages

Not used, so set toNA.

agestages

Not used, so set toNA.

ahstages

The edited stageframe describing the life history of thestudy organism as interpreted in the original demographic study.

labels

The order of the matrices, where each population is treatedas a separate patch and each matrix corresponds to a different combinationof population and year in timet.

matrixqc

A vector of integers used in the quality control section oflefkoMat summary statements.

dataqc

Currently a vector with twoNA values.

Source

Davison, R. et al. 2010. Demographic effects of extreme weatherevents on a short-lived calcareous grassland species: stochastic life tableresponse experiments.Journal of Ecology 98(2):255-267.

Examples

data(anthyllis)lambda3(anthyllis)

Append Projections To Create New lefkoProj Object

Description

Functionappend_lP() combines two population projections. It takestwolefkoProj objects and appends them into a newlefkoProjobject.

Usage

append_lP(proj1 = NULL, proj2 = NULL)

Arguments

Value

A list of classlefkoProj, which always includes the firstthree elements of the following, and also includes the remaining elementsbelow when alefkoMat object is used as input:

Notes

lefkoProj objects resulting from previous appends can also beappended.

See Also

projection3()

Examples

data(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,   sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",   repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,   NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",    "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep", "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.1, 0.2, 0.2, 0.2, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type = c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3), stageframe = cypframe_raw,  historical = FALSE)cypmatrix2r_AB <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = c("A", "B"), stages = c("stage3", "stage2"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2",  patchcol = "patchid", indivcol = "individ")cypmatrix2r_AC <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = c("A", "C"), stages = c("stage3", "stage2"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2",  patchcol = "patchid", indivcol = "individ")cypproj1 <- projection3(cypmatrix2r_AB, nreps = 5, times = 15,  stochastic = TRUE)cypproj2 <- projection3(cypmatrix2r_AC, nreps = 10, times = 20,  stochastic = TRUE)cypproj3 <- append_lP(cypproj1, cypproj2)

Create Raw Ahistorical Age x Stage Matrix Projection Model

Description

Functionarlefko2() returns raw ahistorical age x stage MPMscorresponding to the patches and occasion times given, including theassociated component transition and fecundity matrices, data frames detailingthe characteristics of ahistorical stages and the exact age-stagecombinations corresponding to rows and columns in estimated matrices, and adata frame characterizing the patch and occasion time combinationscorresponding to these matrices.

Usage

arlefko2(  data,  stageframe,  year = "all",  pop = NULL,  patch = NULL,  censor = FALSE,  stages = NULL,  alive = c("alive3", "alive2"),  obsst = NULL,  size = c("sizea3", "sizea2"),  sizeb = NULL,  sizec = NULL,  repst = c("repstatus3", "repstatus2"),  matst = c("matstatus3", "matstatus2"),  fec = c("feca3", "feca2"),  supplement = NULL,  repmatrix = NULL,  overwrite = NULL,  agecol = "obsage",  yearcol = NULL,  popcol = NULL,  patchcol = NULL,  indivcol = NULL,  censorcol = NULL,  censorkeep = 0,  final_age = NA,  continue = TRUE,  prebreeding = TRUE,  NRasRep = FALSE,  reduce = FALSE,  simple = FALSE,  err_check = FALSE,  sparse_output = FALSE)

Arguments

Value

If the user inputs a standardhfv_data object in argumentdata, then this function will return an object of classlefkoMat. If the user inputs an object of classhfv_list inargumentdata, then the output will be an object of classlefkoMatList, in which each element is an object of classlefkoMat.

AlefkoMat object is a list that holds one full matrix projectionmodel and all of its metadata. The structure has the following elements:

Notes

The default behavior of this function is to estimate fecundity with regardsto transitions specified via associated fecundity multipliers in thesupplement. If this field is left empty, then fecundity will beestimated at full for all transitions leading from reproductive stages toimmature and propagule stages.

Users may at times wish to estimate MPMs using a dataset incorporatingmultiple patches or subpopulations. Should the aim of analysis be a generalMPM that does not distinguish these patches or subpopulations, thepatchcol variable should be left toNA, which is the default.Otherwise the variable identifying patch needs to be named.

Input options including multiple variable names must be entered in the orderof variables in occasiont+1 andt. Rearranging the order WILLlead to erroneous calculations, and may lead to fatal errors.

Although this function is capable of assigning stages given an inputstageframe, it lacks the power ofverticalize3() andhistoricalize3() in this regard. Users are stronglyencouraged to use the latter two functions for stage assignment.

See Also

mpm_create()

flefko3()

flefko2()

aflefko2()

fleslie()

rlefko3()

rlefko2()

rleslie()

Examples

# Cypripedium exampledata(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)minagevec <- c(1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5)maxagevec <- c(rep(NA, 11))cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec, minage = minagevec, maxage = maxagevec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE, age_offset = 4)# Here we use supplemental() to provide overwrite and reproductive infocypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE, agebased = TRUE)cyp_mats <- arlefko2(data = cypraw_v1, stageframe = cypframe_raw, year = "all",   patch = NA, censor = FALSE, stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), fec = c("feca3", "feca2"),  supplement = cypsupp2r, agecol = "obsage", yearcol = "year2",   patchcol = "patchid", indivcol = "individ", prebreeding = TRUE, final_age = NA,  continue = TRUE, reduce = FALSE)

Two-Parameter Beverton-Holt Function

Description

Functionbeverton3() creates a vector of values produced by the two-parameter Beverton-Holt function as applied with a user-specified time lag.The two-parameter Beverton-Holt function is given as\phi_{t+1} = \phi_t \alpha / (1 + \beta n_t). Here, if noseparate_N vector is provided, thenn_t = \phi_t.

Usage

beverton3(  start_value,  alpha,  beta,  time_steps = 100L,  time_lag = 1L,  pre0_subs = FALSE,  pre0_value = 0,  substoch = 0L,  separate_N = NULL)

Arguments

Value

A numeric vector of values showing values projected under the two-parameter Beverton-Holt function.

Examples

trial_run1 <- beverton3(1, alpha = 0.5, beta = 0.009)plot(trial_run1)trial_run2 <- beverton3(1, alpha = 0.5, beta = 0.9)plot(trial_run2)trial_run3 <- beverton3(1, alpha = 1, beta = 0.009)plot(trial_run3)trial_run4 <- beverton3(1, alpha = 1, beta = 0.9)plot(trial_run4)trial_run5 <- beverton3(1, alpha = 5, beta = 0.009)plot(trial_run5)trial_run6 <- beverton3(1, alpha = 5, beta = 0.9)plot(trial_run6)used_Ns <- c(10, 15, 12, 14, 14, 150, 15, 1, 5, 7, 9, 14, 13, 16, 17, 19,  25, 26)trial_run7 <- beverton3(1, alpha = 1, beta = 0.009, separate_N = used_Ns)plot(trial_run7)

Bootstrap Standardized hfv_data Datasets

Description

Functionbootstrap3() takes already standardizedhfvdatadatasets and bootstraps them by individual identity, or by row.

Usage

bootstrap3(  data,  by_pop = NULL,  by_patch = NULL,  by_indiv = NULL,  prop_size = NULL,  max_limit = NULL,  reps = NULL,  popcol = NULL,  patchcol = NULL,  indivcol = NULL)

Arguments

Value

A list of classhfvlist, which is composed of data frames ofclasshfvdata.

Examples

data(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathboot <- bootstrap3(lathvert, reps = 3)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl"),   stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep"),  stage1 = c("Sd", "rep", "Sd", "rep", "all", "all"),   givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054),  type = c(1, 1, 1, 1, 3, 3), type_t12 = c(1, 2, 1, 2, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3_boot <- rlefko3(data = lathboot, stageframe = lathframe,  year = c(1989, 1990), stages = c("stage3", "stage2", "stage1"),  supplement = lathsupp3, yearcol = "year2", indivcol = "individ")

Extract Conditional Ahistorical Difference Matrices

Description

Functioncond_diff() takes a set of historical difference matricesresulting from functiondiff_lM() and decomposes them into ahistorical difference matrices conditional upon stage in timet-1.

Usage

cond_diff(lDiff, ref = 1L, matchoice = NULL, err_check = NULL)

Arguments

Value

AlefkoCondDiff object, with the following elements:

Examples

sizevector <- c(0, 0, 0, 0, 0, 0, 1, 3, 6, 11, 19.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1.5, 1.5, 3.5, 5)comments <- c("Dormant seed", "1st yr protocorm", "2nd yr protocorm",  "3rd yr protocorm", "Seedling", "Dormant adult",  "Extra small adult (1 shoot)", "Small adult (2-4 shoots)",  "Medium adult (5-7 shoots)", "Large adult (8-14 shoots)",  "Extra large adult (>14 shoots)")cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,   binhalfwidth = binvec, comments = comments)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,   patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)seeds_per_pod <- 5000cypsupp2_raw <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "SL", "D",     "XSm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep", "rep"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "D", "XSm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, NA, "XSm", "XSm", NA, NA),  givenrate = c(0.03, 0.15, 0.1, 0.1, 0.1, 0.05, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, (0.5 * seeds_per_pod),    (0.5 * seeds_per_pod)),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypsupp3_raw <- supplemental(stage3 = c("SD", "SD", "P1", "P1", "P2", "P3", "SL",    "D", "XSm", "Sm", "D", "XSm", "Sm", "mat", "mat", "mat", "SD", "P1"),  stage2 = c("SD", "SD", "SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "SL",    "SL", "SL", "D", "XSm", "Sm", "rep", "rep"),  stage1 = c("SD", "rep", "SD", "rep", "SD", "P1", "P2", "P3", "P3", "P3",    "SL", "SL", "SL", "SL", "SL", "SL", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, NA, NA, NA, "D", "XSm", "Sm", "D", "XSm", "Sm",    "mat", "mat", "mat", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", "XSm", "XSm",    "XSm", "D", "XSm", "Sm", NA, NA),  eststage1 = c(NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", "XSm", "XSm",    "XSm", "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.25, NA, NA, NA, NA, NA, NA,    NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,    NA, 0.5, 0.5),  type = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  type_t12 = c(1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),  stageframe = cypframe_raw, historical = TRUE)cypmatrix2rp <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", patch = "all", stages = c("stage3", "stage2"),  size = c("size3added", "size2added"), supplement = cypsupp2_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", stages = c("stage3", "stage2"),  size = c("size3added", "size2added"), supplement = cypsupp2_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix3rp <- rlefko3(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),   size = c("size3added", "size2added", "size1added"), supplement = cypsupp3_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix3r <- rlefko3(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", stages = c("stage3", "stage2", "stage1"),   size = c("size3added", "size2added", "size1added"), supplement = cypsupp3_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix2r_3 <- hist_null(cypmatrix2r)cypmatrix2r_3 <- delete_lM(cypmatrix2r_3, year = 2004)diff_r <- diff_lM(cypmatrix3r, cypmatrix2r_3)cypmatrix2rp_3 <- hist_null(cypmatrix2rp)cypmatrix2rp_3 <- delete_lM(cypmatrix2rp_3, year = 2004)diff_rp <- diff_lM(cypmatrix3rp, cypmatrix2rp_3)condr1 <- cond_diff(diff_r, ref = 1)condr2 <- cond_diff(diff_r, ref = 2)condrp1 <- cond_diff(diff_rp, matchoice = "U", ref = 1)condrp2 <- cond_diff(diff_rp, matchoice = "F", ref = 2)

Extract Conditional Ahistorical Matrices from Historical MPM

Description

Functioncond_hmpm() takes historical MPMs and decomposes them into ahistorical matrices conditional upon stage in timet-1. In effect,the function takes each historical matrix within a lefkoMat object, andforms one ahistorical matrix for each stage in timet-1.

Usage

cond_hmpm(hmpm, matchoice = NULL, err_check = NULL)

Arguments

Value

AlefkoCondMat object, with the following elements:

Examples

data(cypdata) sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md",  "Lg", "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,   sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",   repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,   NRasRep = TRUE)cypsupp3r <- supplemental(stage3 = c("SD", "SD", "P1", "P1", "P2", "P3", "SL",    "D", "XSm", "Sm", "D", "XSm", "Sm", "mat", "mat", "mat", "SD", "P1"),  stage2 = c("SD", "SD", "SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "SL",    "SL", "SL", "D", "XSm", "Sm", "rep", "rep"),  stage1 = c("SD", "rep", "SD", "rep", "SD", "P1", "P2", "P3", "P3", "P3",    "SL", "SL", "SL", "SL", "SL", "SL", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, NA, NA, NA, "D", "XSm", "Sm", "D", "XSm", "Sm",    "mat", "mat", "mat", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", "XSm", "XSm",    "XSm", "D", "XSm", "Sm", NA, NA),  eststage1 = c(NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", "XSm", "XSm",    "XSm", "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.25, NA, NA, NA, NA, NA, NA,    NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,    NA, 0.5, 0.5),  type = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  type_t12 = c(1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),  stageframe = cypframe_raw, historical = TRUE)cypmatrix3r <- rlefko3(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added", "size1added"),  supplement = cypsupp3r, yearcol = "year2", patchcol = "patchid",  indivcol = "individ")cypcondmats <- cond_hmpm(cypmatrix3r)summary(cypcondmats)

Create lefkoMat Object from Given Input Matrices or an MPM Database

Description

Functioncreate_lM() creates lefkoMat objects from supplied matricesand extra information, or from a supplied MPM database such as COMPADRE orCOMADRE.

Usage

create_lM(  mats,  stageframe = NULL,  hstages = NA,  agestages = NA,  historical = FALSE,  agebystage = FALSE,  UFdecomp = TRUE,  entrystage = 1,  poporder = 1,  patchorder = 1,  yearorder = NA,  matrix_id = NULL,  add_FC = TRUE,  sparse_output = FALSE)

Arguments

Value

AlefkoMat object incorporating the matrices input in objectmats as objectA, their U and F decompositions in objectsU andF (if requested), the provided stageframe as objectahstages, the order of historical stages as objecthstages (ifhistorical = TRUE), the order of matrices as objectlabels, anda short quality control section used by thesummary.lefkoMat()function.

Notes for importing lists of matrices

Lists may be composed of a mix of matrices in standard R format (i.e. createdvia thematrix() function), and indgCMatrix sparse format(i.e. created via theMatrix::Matrix() function withsparse = TRUE.) All matrices will be forced to one or the other,depending on the value given for thesparse_output argument.

U and F decomposition assumes that elements holding fecundity values areto be interpreted solely as fecundity rates. Users wishing to split theseelements between fecundity and survival should do so manually after runningthis function.

Age-by-stage MPMs require anagestages data frame outlining the orderof age-stages. This data frame has 3 variables:stage_id, which is thenumber of the stage as labelled by the equivalently named variable in thestageframe;stage, which is the official name of the stage asgiven in the equivalently named variable in thestageframe; andage, which of course gives the age associated with the stage at thattime. The number of rows must be equal to the number of rows and columns ofeach entered matrix.

Users may edit thedataqc object, setting the firstNA to thenumber of individuals sampled, and the secondNA to the number ofrows in a vertical version of the demographic dataset. This is not required,however.

Notes for importing from COMPADRE or COMADRE

For this function to operate, users must have either the COMPADRE databaseor the COMADRE database loaded into the global environment. Note that thesample databases supplied within packageRcompadre will not work withthis function.

This function does not and cannot replace the wonderful tools offered toexplore the COMPADRE and COMADRE packages. Please see packageRcompadre to use those tools. Note that functionimport_Com()has no relationship to theRcompadre development team.

Functionimport_Com() requires that the dimensions of all matricesimported into a singlelefkoMat object be equal.

The reproductive and maturity status of each stage is determined by patternsassessed within theF matrices. Users should check that these valuesmake sense.

Stage names may be edited manually afterward.

Users may edit thedataqc object, setting the firstNA to thenumber of individuals sampled, and the secondNA to the number ofrows in a vertical version of the demographic dataset. This is not required,however.

See Also

add_lM()

delete_lM()

subset_lM()

Examples

# These matrices are of 9 populations of the plant species Anthyllis# vulneraria, and were originally published in Davison et al. (2010) Journal# of Ecology 98:255-267 (doi: 10.1111/j.1365-2745.2009.01611.x).sizevector <- c(1, 1, 2, 3) # These sizes are not from the original paperstagevector <- c("Sdl", "Veg", "SmFlo", "LFlo")repvector <- c(0, 0, 1, 1)obsvector <- c(1, 1, 1, 1)matvector <- c(0, 1, 1, 1)immvector <- c(1, 0, 0, 0)propvector <- c(0, 0, 0, 0)indataset <- c(1, 1, 1, 1)binvec <- c(0.5, 0.5, 0.5, 0.5)anthframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)# POPN C 2003-2004XC3 <- matrix(c(0, 0, 1.74, 1.74,0.208333333, 0, 0, 0.057142857,0.041666667, 0.076923077, 0, 0,0.083333333, 0.076923077, 0.066666667, 0.028571429), 4, 4, byrow = TRUE)# 2004-2005XC4 <- matrix(c(0, 0, 0.3, 0.6,0.32183908, 0.142857143, 0, 0,0.16091954, 0.285714286, 0, 0,0.252873563, 0.285714286, 0.5, 0.6), 4, 4, byrow = TRUE)# 2005-2006XC5 <- matrix(c(0, 0, 0.50625, 0.675,0, 0, 0, 0.035714286,0.1, 0.068965517, 0.0625, 0.107142857,0.3, 0.137931034, 0, 0.071428571), 4, 4, byrow = TRUE)# POPN E 2003-2004XE3 <- matrix(c(0, 0, 2.44, 6.569230769,0.196428571, 0, 0, 0,0.125, 0.5, 0, 0,0.160714286, 0.5, 0.133333333, 0.076923077), 4, 4, byrow = TRUE)XE4 <- matrix(c(0, 0, 0.45, 0.646153846,0.06557377, 0.090909091, 0.125, 0,0.032786885, 0, 0.125, 0.076923077,0.049180328, 0, 0.125, 0.230769231), 4, 4, byrow = TRUE)XE5 <- matrix(c(0, 0, 2.85, 3.99,0.083333333, 0, 0, 0,0, 0, 0, 0,0.416666667, 0.1, 0, 0.1), 4, 4, byrow = TRUE)mats_list <- list(XC3, XC4, XC5, XE3, XE4, XE5)yr_ord <- c(1, 2, 3, 1, 2, 3)pch_ord <- c(1, 1, 1, 2, 2, 2)anth_lefkoMat <- create_lM(mats_list, anthframe, hstages = NA,  historical = FALSE, poporder = 1, patchorder = pch_ord, yearorder = yr_ord)# A theoretical example showcasing historical matricessizevector <- c(1, 2, 3) # These sizes are not from the original paperstagevector <- c("Sdl", "Veg", "Flo")repvector <- c(0, 0, 1)obsvector <- c(1, 1, 1)matvector <- c(0, 1, 1)immvector <- c(1, 0, 0)propvector <- c(1, 0, 0)indataset <- c(1, 1, 1)binvec <- c(0.5, 0.5, 0.5)exframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)A1 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,  0, 0, 2.00, 0, 0, 3.00, 0, 0, 4.00,  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)A2 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,  0, 0, 5.00, 0, 0, 6.00, 0, 0, 7.00,  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)A3 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,  0, 0, 8.00, 0, 0, 9.00, 0, 0, 10.00,  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)B1 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,  0, 0, 11.00, 0, 0, 12.00, 0, 0, 13.00,  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)B2 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,  0, 0, 14.00, 0, 0, 15.00, 0, 0, 16.00,  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)B3 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,  0, 0, 17.00, 0, 0, 18.00, 0, 0, 19.00,  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)histmats <- list(A1, A2, A3, B1, B2, B3)stageframe <- exframepch_ord <- c("A", "A", "A", "B", "B", "B")yr_ord <- c(1, 2, 3, 1, 2, 3)hist_trial <- create_lM(histmats, exframe, historical = TRUE,  UFdecomp = TRUE, entrystage = 1, patchorder = pch_ord, yearorder = yr_ord)

Creates a Skeleton Paramnames Object for Use in Function-based Modeling

Description

Creates a simple skeletonparamnames object that can be entered asinput in functionsflefko2(),flefko3(), andaflefko2().

Usage

create_pm(name_terms = FALSE)

Arguments

Value

A three column data frame, of which the first describes theparameters in reasonably plain English, the second gives the name of theparameter within the MPM generating functions, and the third is to beedited with the names of the variables as they appear in the models.

Notes

The third column in the resulting object should be edited with the names onlyof those variables actually used in vital rate modeling. Thisparamnames object should apply to all models used in a single MPMbuilding exercise. So, for example, if the models used include random terms,then they should all have the same random terms. Fixed terms can vary,however.

Examples

our_pm <- create_pm()our_pm

Check Continuity of Life Cycle through Matrices in lefkoMat Objects

Description

Functioncycle_check() tests whether stages, stage-pairs, orage-stages connect in matrices withinlefkoMat objects.

Usage

cycle_check(mpm, quiet = NULL)

Arguments

Value

Returns a list with two elements, both of which are also lists.The first list,no_in, contains as many elements as matrices, witheach element containing an integer vector showing the identification numbersof stages, stage-pairs, or age-stages, in each matrix that do not show anytransitions leading to them. The second list,no_out, is structuredsimilarly to the first, but shows stages, stage-pairs, or age-stages fromwhich there are no transitions leading out.

Notes

This function tests whether stages, stage-pairs, and age-stages areconnected to others in matrices used for projection. Whether stages,stage-pairs, or age-stages are shown depends on whether the MPM isahistorical / age-based, historical stage-based, or age-by-stage,respectively. Checks are performed by testing whether each column in amatrix includes non-zero transitions to other columns, and by testingwhether any columns have no transitions to them from other columns. If anysuch columns are found, then functioncycle_checkwill export an integer vector giving the column numbers with problems.These column numbers may then be checked against thestage_id columnof the associated stageframe in the case of a ahistorical or age-based MPM,against the row number of the associatedhstages data frame in thecase of a historical MPM, or against the row number of the associatedagestages data frame in the case of an age-by-stage MPM.

Examples

data(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")cycle_check(cypmatrix2r)

Demographic Dataset ofCypripedium candidum Population, in HorizontalFormat

Description

A dataset containing the states and fates ofCypripedium candidum (white lady's slipper orchids), family Orchidaceae, from a population in Illinois, USA, resulting from monitoring that occurred annually between 2004 and 2009.

Usage

data(cypdata)

Format

A data frame with 77 individuals and 29 variables. Each row corresponds to an unique individual, and each variable fromsize.04 on refers to the state of the individual in a particular year.

plantid

A numeric variable giving a unique number to each individual.

patch

A variable refering to patch within the population.

X

An X coordinate for the plant within the population.

Y

A Y coordinate for the plant within the population.

censor

A variable coding for whether the data point is valid. Anentry of 1 means that it is so.

Inf2.04

Number of double inflorescences in 2004.

Inf.04

Number of inflorescences in 2004.

Veg.04

Number of stems without inflorescences in 2004.

Pod.04

Number of fruits in 2004.

Inf2.05

Number of double inflorescences in 2005.

Inf.05

Number of inflorescences in 2005.

Veg.05

Number of stems without inflorescences in 2005.

Pod.05

Number of fruits in 2005.

Inf2.06

Number of double inflorescences in 2006.

Inf.06

Number of inflorescences in 2006.

Veg.06

Number of stems without inflorescences in 2006.

Pod.06

Number of fruits in 2006.

Inf2.07

Number of double inflorescences in 2007.

Inf.07

Number of inflorescences in 2007.

Veg.07

Number of stems without inflorescences in 2007.

Pod.07

Number of fruits in 2007.

Inf2.08

Number of double inflorescences in 2008.

Inf.08

Number of inflorescences in 2008.

Veg.08

Number of stems without inflorescences in 2008.

Pod.08

Number of fruits in 2008.

Inf2.09

Number of double inflorescences in 2009.

Inf.09

Number of inflorescences in 2009.

Veg.09

Number of stems without inflorescences in 2009.

Pod.09

Number of fruits in 2009.

Source

Shefferson, R.P., R. Mizuta, and M.J. Hutchings. 2017. Predictingevolution in response to climate change: the example of sprouting probabilityin three dormancy-prone orchid species.Royal Society Open Science 4(1):160647.

Examples

data(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")                       lambda3(cypmatrix2r)

Demographic Dataset ofCypripedium candidum Population, in VerticalFormat

Description

A dataset containing the states and fates ofCypripedium candidum (white lady's slipper orchids), family Orchidaceae, from a population in Illinois, USA, resulting from monitoring that occurred annually between 2004 and 2009. Same dataset ascypdata, but arranged in an ahistoricalvertical format.

Usage

data(cypvert)

Format

A data frame with 77 individuals, 322 rows, and 14 variables. Eachrow corresponds to a specific two-year transition for a specific individual.Variable codes are similar to those forcypdata, but use.2 toidentify occasiont and.3 to identify occasiont+1.

plantid

A numeric variable giving a unique number to each individual.

patch

A variable refering to patch within the population.

X

An X coordinate for the plant within the population.

Y

A Y coordinate for the plant within the population.

censor

A variable coding for whether the data point is valid. Anentry of 1 means that it is so.

year2

Year in occasiont.

Inf2.2

Number of double inflorescences in occasiont.

Inf.2

Number of inflorescences in occasiont.

Veg.2

Number of stems without inflorescences in occasiont.

Pod.2

Number of fruits in occasiont.

Inf2.3

Number of double inflorescences in occasiont+1.

Inf.3

Number of inflorescences in occasiont+1.

Veg.3

Number of stems without inflorescences in occasiont+1.

Pod.3

Number of fruits in occasiont+1.

Source

Shefferson, R.P., R. Mizuta, and M.J. Hutchings. 2017. Predictingevolution in response to climate change: the example of sprouting probabilityin three dormancy-prone orchid species.Royal Society Open Science 4(1):160647.

Examples

data(cypvert)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v2 <- historicalize3(data = cypvert, patchidcol = "patch",   individcol = "plantid", year2col = "year2", sizea2col = "Inf2.2",   sizea3col = "Inf2.3", sizeb2col = "Inf.2", sizeb3col = "Inf.3",   sizec2col = "Veg.2", sizec3col = "Veg.3", repstra2col = "Inf2.2",   repstra3col = "Inf2.3", repstrb2col = "Inf.2", repstrb3col = "Inf.3",   feca2col = "Pod.2", feca3col = "Pod.3", repstrrel = 2,   stageassign = cypframe_raw, stagesize = "sizeadded", censorcol = "censor",  censor = FALSE, NAas0 = TRUE, NRasRep = TRUE, reduce = TRUE)  cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v2, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")  lambda3(cypmatrix2r)

Delete Matrices from lefkoMat or lefkoMatList Object

Description

Functiondelete_lM() deletes matrices fromlefkoMat andlefkoMatList objects.

Usage

delete_lM(lM, mat_num = NA, pop = NA, patch = NA, year = NA)

Arguments

Value

AlefkoMat orlefkoMatList object in which the matricesspecified inlM have been removed. Note that, if applying to alefkoMatList object, then matrices will be deleted from ALL compositelefkoMat objects.

Notes

Ifmat_num is not provided, then at least one ofpop,patch, oryear must be provided. If at least two ofpop,patch, andyear are provided, then functiondetele_lM()will identify matrices to remove as the intersection of provided inputs.

See Also

create_lM()

add_lM()

subset_lM()

Examples

# These matrices are of 9 populations of the plant species Anthyllis# vulneraria, and were originally published in Davison et al. (2010) Journal# of Ecology 98:255-267 (doi: 10.1111/j.1365-2745.2009.01611.x).sizevector <- c(1, 1, 2, 3) # These sizes are not from the original paperstagevector <- c("Sdl", "Veg", "SmFlo", "LFlo")repvector <- c(0, 0, 1, 1)obsvector <- c(1, 1, 1, 1)matvector <- c(0, 1, 1, 1)immvector <- c(1, 0, 0, 0)propvector <- c(0, 0, 0, 0)indataset <- c(1, 1, 1, 1)binvec <- c(0.5, 0.5, 0.5, 0.5)anthframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)# POPN C 2003-2004XC3 <- matrix(c(0, 0, 1.74, 1.74,0.208333333, 0, 0, 0.057142857,0.041666667, 0.076923077, 0, 0,0.083333333, 0.076923077, 0.066666667, 0.028571429), 4, 4, byrow = TRUE)# 2004-2005XC4 <- matrix(c(0, 0, 0.3, 0.6,0.32183908, 0.142857143, 0, 0,0.16091954, 0.285714286, 0, 0,0.252873563, 0.285714286, 0.5, 0.6), 4, 4, byrow = TRUE)# 2005-2006XC5 <- matrix(c(0, 0, 0.50625, 0.675,0, 0, 0, 0.035714286,0.1, 0.068965517, 0.0625, 0.107142857,0.3, 0.137931034, 0, 0.071428571), 4, 4, byrow = TRUE)# POPN E 2003-2004XE3 <- matrix(c(0, 0, 2.44, 6.569230769,0.196428571, 0, 0, 0,0.125, 0.5, 0, 0,0.160714286, 0.5, 0.133333333, 0.076923077), 4, 4, byrow = TRUE)XE4 <- matrix(c(0, 0, 0.45, 0.646153846,0.06557377, 0.090909091, 0.125, 0,0.032786885, 0, 0.125, 0.076923077,0.049180328, 0, 0.125, 0.230769231), 4, 4, byrow = TRUE)XE5 <- matrix(c(0, 0, 2.85, 3.99,0.083333333, 0, 0, 0,0, 0, 0, 0,0.416666667, 0.1, 0, 0.1), 4, 4, byrow = TRUE)mats_list <- list(XC3, XC4, XC5, XE3, XE4, XE5)yr_ord <- c(1, 2, 3, 1, 2, 3)pch_ord <- c(1, 1, 1, 2, 2, 2)anth_lefkoMat <- create_lM(mats_list, anthframe, hstages = NA,  historical = FALSE, poporder = 1, patchorder = pch_ord, yearorder = yr_ord)  smaller_anth_lM <- delete_lM(anth_lefkoMat, patch = 2)

Set Density Dependence Relationships in Matrix Elements

Description

Functiondensity_input() provides all necessary data to incorporatedensity dependence into alefkoMat object, a list of matrices, or asingle matrix. Four forms of density dependence are allowed, including theRicker function, the Beverton-Holt function, the Usher function, and thelogistic function. In each case, density must have an effect with a delay ofat least one time-step (see Notes). The resulting data frame provides aguide for otherlefko3 functions to modify matrix elements bydensity.

Usage

density_input(  mpm,  stage3 = NULL,  stage2 = NULL,  stage1 = NULL,  age2 = NULL,  style = NULL,  time_delay = NULL,  alpha = NULL,  beta = NULL,  type = NULL,  type_t12 = NULL)

Arguments

Value

A data frame of classlefkoDens. This object can be used asinput in functionprojection3().

Variables in this object include the following:

Notes

This function provides inputs when density dependence is operationalizeddirectly on matrix elements. It can be used in bothprojection3() andf_projection3(). Users wishing to modify vital rate functions bydensity dependence functions for use in function-based projections withfunctionf_projection3() should use functiondensity_vr() toprovide the correct inputs.

The parametersalpha andbeta are applied according to thetwo-parameter Ricker function, the two-parameter Beverton-Holt function, thetwo-parameter Usher function, or the one-parameter logistic function.Although the default is that a 1 time step delay is assumed, greater timedelays can be set through thetime_delay option.

Entries instage3,stage2, andstage1 can includeabbreviations for groups of stages. Userep if all reproductive stagesare to be used,nrep if all mature but non-reproductive stages are tobe used,mat if all mature stages are to be used,immat if allimmature stages are to be used,prop if all propagule stages are to beused,npr if all non-propagule stages are to be used,obs ifall observable stages are to be used,nobs if all unobservable stagesare to be used, and leave empty or useall if all stages in stageframeare to be used.

When using the logistic function, it is possible that the time delay used indensity dependent simulations will cause matrix elements to become negative.To prevent this behavior, set the associatedbeta term to1.0.Doing so will setK as the hard limit in the logistic equation,essentially setting a minimum limit at0 for all matrix elementsmodified.

See Also

start_input()

projection3()

Examples

data(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")ehrlen3mean <- lmean(ehrlen3)e3d <- density_input(ehrlen3mean, stage3 = c("Sd", "Sdl"),  stage2 = c("rep", "rep"), stage1 = c("all", "all"), style = 1,  time_delay = 1, alpha = 1, beta = 0, type = c(2, 2), type_t12 = c(1, 1))lathproj <- projection3(ehrlen3, nreps = 5, stochastic = TRUE, substoch = 2,  density = e3d)

Set Density Dependence Relationships in Vital Rates

Description

Functiondensity_vr() provides all necessary data to incorporatedensity dependence into the vital rate functions used to create matrices infunction-based projections using functionf_projection3(). Four formsof density dependence are allowed, including the Ricker function, theBeverton-Holt function, the Usher function, and the logistic function. Ineach case, density must have an effect with at least a one time-step delay(see Notes).

Usage

density_vr(  density_yn = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,    FALSE, FALSE, FALSE, FALSE),  style = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),  time_delay = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),  alpha = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),  beta = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))

Arguments

Value

A data frame of classlefkoDensVR with 14 rows, one for eachvital rate in the order of: survival probability, observation probability,primary size transition, secondary size transition, tertiary size transition,reproductive status probability, fecundity rate, juvenile survivalprobability, juvenile observation probability, juvenile primary sizetransition, juvenile secondary size transition, juvenile tertiary sizetransition, juvenile reproductive status probability, and juvenile maturitystatus probability. This object can be used as input in functionf_projection3().

Variables in this object include the following:

Notes

This function provides inputs when density dependence is operationalizeddirectly on vital rates. It can be used only in functionf_projection3(). Users wishing to modify matrix elements directly bydensity dependence functions for use in function-based or raw projectionswith functionsprojection3() andf_projection3() should usefunctiondensity_input() to provide the correct inputs.

The parametersalpha andbeta are applied according to thetwo-parameter Ricker function, the two-parameter Beverton-Holt function, thetwo-parameter Usher function, or the one-parameter logistic function.Although the default is that a 1 time step delay is assumed, greater timedelays can be set through thetime_delay option.

When using the logistic function, it is possible that the time delay used indensity dependent simulations will cause matrix elements to become negative.To prevent this behavior, set the associatedbeta term to1.0.Doing so will setK as the hard limit in the logistic equation,essentially setting a minimum limit at0 for all matrix elementsmodified.

See Also

density_input()

f_projection3()

Examples

data(lathyrus)sizevector <- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,  9)stagevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",  "Sz5nr", "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r",   "Sz4r", "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")repvector <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)obsvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)matvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,   0)indataset <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)binvec <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,   0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)lathframeln <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   immstatus = immvector, indataset = indataset, binhalfwidth = binvec,   propstatus = propvector)lathvertln <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,   juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "Intactseed88",  fecacol = "Intactseed88", deadacol = "Dead1988",   nonobsacol = "Dormant1988", stageassign = lathframeln, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, NAas0 = TRUE, censor = TRUE)lathvertln$feca2 <- round(lathvertln$feca2)lathvertln$feca1 <- round(lathvertln$feca1)lathvertln$feca3 <- round(lathvertln$feca3)lathvertln_adults <- subset(lathvertln, stage2index > 2)surv_model <- glm(alive3 ~ sizea2 + sizea1 + as.factor(patchid) +  as.factor(year2), data = lathvertln_adults, family = "binomial")obs_data <- subset(lathvertln_adults, alive3 == 1)obs_model <- glm(obsstatus3 ~ as.factor(patchid), data = obs_data,  family = "binomial")size_data <- subset(obs_data, obsstatus3 == 1)siz_model <- lm(sizea3 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid) +  as.factor(year2), data = size_data)reps_model <- glm(repstatus3 ~ sizea2 + sizea1 + as.factor(patchid) +  as.factor(year2), data = size_data, family = "binomial")fec_data <- subset(lathvertln_adults, repstatus2 == 1)fec_model <- glm(feca2 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid),  data = fec_data, family = "poisson")lathvertln_juvs <- subset(lathvertln, stage2index < 3)jsurv_model <- glm(alive3 ~ as.factor(patchid), data = lathvertln_juvs,  family = "binomial")jobs_data <- subset(lathvertln_juvs, alive3 == 1)jobs_model <- glm(obsstatus3 ~ 1, family = "binomial", data = jobs_data)jsize_data <- subset(jobs_data, obsstatus3 == 1)jsiz_model <- lm(sizea3 ~ as.factor(year2), data = jsize_data)jrepst_model <- 0jmatst_model <- 1mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[3] <- "patchid"mod_params$modelparams[4] <- "alive3"mod_params$modelparams[5] <- "obsstatus3"mod_params$modelparams[6] <- "sizea3"mod_params$modelparams[9] <- "repstatus3"mod_params$modelparams[11] <- "feca2"mod_params$modelparams[12] <- "sizea2"mod_params$modelparams[13] <- "sizea1"mod_params$modelparams[18] <- "repstatus2"mod_params$modelparams[19] <- "repstatus1"lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "mat", "Sd", "Sdl"),   stage2 = c("Sd", "Sd", "Sd", "Sd", "Sdl", "rep", "rep"),  stage1 = c("Sd", "rep", "Sd", "rep", "Sd", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, "mat", NA, NA),  eststage2 = c(NA, NA, NA, NA, "Sdl", NA, NA),  eststage1 = c(NA, NA, NA, NA, "Sdl", NA, NA),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, 0.345, 0.054),  type = c(1, 1, 1, 1, 1, 3, 3), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframeln, historical = TRUE)# While we do not use MPMs to initialize f_projections3(), we do use MPMs to# initialize functions start_input() and density_input().lathmat3ln <- flefko3(year = "all", patch = "all", data = lathvertln,  stageframe = lathframeln, supplement = lathsupp3, paramnames = mod_params,  surv_model = surv_model, obs_model = obs_model, size_model = siz_model,  repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,  jobs_model = jobs_model, jsize_model = jsiz_model,  jrepst_model = jrepst_model, jmatst_model = jmatst_model, reduce = FALSE)e3m_sv <- start_input(lathmat3ln, stage2 = "Sd", stage1 = "Sd", value = 1000)dyn7 <- c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,  FALSE, FALSE, FALSE, FALSE, FALSE)dst7 <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)dal7 <- c(0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)dbe7 <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)e3d_vr <- density_vr(density_yn = dyn7, style = dst7, alpha = dal7,  beta = dbe7)trial7_dvr_1 <- f_projection3(format = 1, data = lathvertln, supplement = lathsupp3,  paramnames = mod_params, stageframe = lathframeln, nreps = 2,  surv_model = surv_model, obs_model = obs_model, size_model = siz_model,  repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,  jobs_model = jobs_model, jsize_model = jsiz_model,  jrepst_model = jrepst_model, jmatst_model = jmatst_model,  times = 100, stochastic = TRUE, standardize = FALSE, growthonly = TRUE,  integeronly = FALSE, substoch = 0, sp_density = 0, start_frame = e3m_sv,  density_vr = e3d_vr)

Calculate Difference Matrices Between lefkoMat Objects of Equal Dimensions

Description

Functiondiff_lM() takes twolefkoMat objects with completelyequal dimensions, including both the size and number of matrices, andgives the matrix differences between each corresponding set.

Usage

diff_lM(mpm1, mpm2)

Arguments

Value

An object of classlefkoDiff, which is a set ofA,U, andF matrices corresponding to the differences between eachset of matrices, followed by thehstages,ahstages, andlabels elements from each inputlefkoMat object. Elementslabelled with a1 at the end refer tompm1, while thoselabelled2 at the end refer tompm2.

Notes

The exact difference is calculated as the respective matrix inmpm1minus the corresponding matrix inmpm2.

This function first checks to see if the number of matrices is the same, andthen whether the matrix dimensions are the same. If the two sets differ in atleast one of these characteristics, then the function will yield a fatalerror.

If the lengths and dimensions of the inputlefkoMat objects are thesame, then this will check if thelabels element is essentially thesame. If not, then the function will yield a warning, but will still operate.

Examples

sizevector <- c(0, 0, 0, 0, 0, 0, 1, 3, 6, 11, 19.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1.5, 1.5, 3.5, 5)comments <- c("Dormant seed", "1st yr protocorm", "2nd yr protocorm",  "3rd yr protocorm", "Seedling", "Dormant adult",  "Extra small adult (1 shoot)", "Small adult (2-4 shoots)",  "Medium adult (5-7 shoots)", "Large adult (8-14 shoots)",  "Extra large adult (>14 shoots)")cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,   binhalfwidth = binvec, comments = comments)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,   patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)seeds_per_pod <- 5000cypsupp2_raw <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "SL", "D",     "XSm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep", "rep"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "D", "XSm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, NA, "XSm", "XSm", NA, NA),  givenrate = c(0.03, 0.15, 0.1, 0.1, 0.1, 0.05, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, (0.5 * seeds_per_pod),    (0.5 * seeds_per_pod)),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypsupp3_raw <- supplemental(stage3 = c("SD", "SD", "P1", "P1", "P2", "P3",    "SL", "SL", "SL", "D", "D", "SD", "P1"),  stage2 = c("SD", "SD", "SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "SL",    "rep", "rep"),  stage1 = c("SD", "rep", "SD", "rep", "SD", "P1", "P2", "P3", "SL", "P3",    "SL", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, "XSm", "D", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", NA, NA),  eststage1 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", NA, NA),  givenrate = c(0.01, 0.05, 0.10, 0.20, 0.1, 0.1, 0.05, 0.05, 0.05, NA, NA,    NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,    (0.5 * seeds_per_pod), (0.5 * seeds_per_pod)),  type = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  type_t12 = c(1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1),  stageframe = cypframe_raw, historical = TRUE)cypmatrix2rp <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", patch = "all", stages = c("stage3", "stage2"),  size = c("size3added", "size2added"), supplement = cypsupp2_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", stages = c("stage3", "stage2"),  size = c("size3added", "size2added"), supplement = cypsupp2_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix3rp <- rlefko3(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),   size = c("size3added", "size2added", "size1added"), supplement = cypsupp3_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix3r <- rlefko3(data = cypraw_v1, stageframe = cypframe_raw,  year = "all", stages = c("stage3", "stage2", "stage1"),   size = c("size3added", "size2added", "size1added"), supplement = cypsupp3_raw,   yearcol = "year2", patchcol = "patchid", indivcol = "individ")cypmatrix2r_3 <- hist_null(cypmatrix2r)cypmatrix2r_3 <- delete_lM(cypmatrix2r_3, year = 2004)diff_r <- diff_lM(cypmatrix3r, cypmatrix2r_3)cypmatrix2rp_3 <- hist_null(cypmatrix2rp)cypmatrix2rp_3 <- delete_lM(cypmatrix2rp_3, year = 2004)diff_rp <- diff_lM(cypmatrix3rp, cypmatrix2rp_3)

Edit lefkoMat or lefkoMatList Object based on Supplemental Data

Description

Functionedit_lM() edits existinglefkoMat andlefkoMatList objects with external data supplied by the user. Theeffects are similar to functionsupplemental(), thoughfunctionedit_lM() allows individuals matrices withinlefkoMatobjects to be edited after creation, whilesupplemental()provides external data that modifies all matrices within alefkoMatobject, or within all thelefkoMat objects within alefkoMatList object.

Usage

edit_lM(  mpm,  pop = NULL,  patch = NULL,  year2 = NULL,  stage3 = NULL,  stage2 = NULL,  stage1 = NULL,  age2 = NULL,  eststage3 = NULL,  eststage2 = NULL,  eststage1 = NULL,  estage2 = NULL,  givenrate = NULL,  offset = NULL,  multiplier = NULL,  type = NULL,  type_t12 = NULL,  target_mpm = NULL)

Arguments

Value

An edited copy of the original MPM is returned, also as alefkoMat object.

Notes

Entries instage3,stage2, andstage1 can includeabbreviations for groups of stages. Userep if all reproductivestages are to be used,nrep if all mature but non-reproductive stagesare to be used,mat if all mature stages are to be used,immatif all immature stages are to be used,prop if all propagule stagesare to be used,npr if all non-propagule stages are to be used,obs if all observable stages are to be used,nobs if allunobservable stages are to be used, and leave empty or useall if allstages in stageframe are to be used. Also usegroupX to denote allstages in group X (e.g.group1 will use all stages in the respectivestageframe's group 1).

See Also

supplemental()

Examples

data(cypdata)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  age_offset = 3, NAas0 = TRUE, NRasRep = TRUE)cyp_rl <- rleslie(data = cypraw_v1, start_age = 0, last_age = 6, continue = TRUE,  fecage_min = 3, year = "all", pop = NA, patch = "all", yearcol = "year2",  patchcol = "patchid", indivcol = "individ")ddd1 <- edit_lM(cyp_rl, age2 = c(0, 1, 2, 3, 4, 5, 6),  givenrate = c(0.25, 0.25, 0.4, 0.4, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 2000, 2000, 2000),  type = c(1, 1, 1, 1, 3, 3, 3))  ddd1 <- edit_lM(ddd1, age2 = 6, multiplier = 1.5, type = 3, patch = "B",  year2 = "2005")

Estimate Elasticity of Population Growth Rate to Matrix Elements

Description

elasticity3() is a generic function that returns the elasticity ofthe population growth rate to the elements of the matrices in a matrixpopulation model. Currently, this function estimates both deterministic andstochastic elasticities, where the growth rate is\lambda in the formercase and the log of the stochastic\lambda in the latter case. Thisfunction is made to handle very large and sparse matrices supplied aslefkoMat objects, as lists of matrices, and as individual matrices.

Usage

elasticity3(mats, ...)

Arguments

Value

The value returned depends on the class of themats argument.

See Also

elasticity3.lefkoMat()

elasticity3.matrix()

elasticity3.dgCMatrix()

elasticity3.list()

elasticity3.lefkoMatList()

summary.lefkoElas()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")ehrlen3mean <- lmean(ehrlen3)elasticity3(ehrlen3mean)# Cypripedium exampledata(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type = c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")elasticity3(cypmatrix2r)

Estimate Elasticity of Population Growth Rate of a Single Sparse Matrix

Description

elasticity3.dgCMatrix() returns the elasticities of lambda to elementsof a single matrix. Because this handles only one matrix, the elasticitiesare inherently deterministic and based on the dominant eigen value as thebest metric of the population growth rate.

Usage

## S3 method for class 'dgCMatrix'elasticity3(mats, sparse = "auto", ...)

Arguments

Value

This function returns a single elasticity matrix indgCMatrixformat.

See Also

elasticity3()

elasticity3.lefkoMat()

elasticity3.list()

elasticity3.matrix()

elasticity3.lefkoMatList()

summary.lefkoElas()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")ehrlen3mean <- lmean(ehrlen3)elasticity3(ehrlen3mean$A[[1]])

Estimate Elasticity of Population Growth Rate of a lefkoMat Object

Description

elasticity3.lefkoMat() returns the elasticities of population growthrate to elements of all$A matrices in an object of classlefkoMat. If deterministic, then\lambda is taken as thepopulation growth rate. If stochastic, then stochastic\lambda, orthe stochastic growth rate, is taken as the population growth rate. Thisfunction can handle large and sparse matrices, and so can be used with largehistorical matrices, IPMs, age x stage matrices, as well as smallerahistorical matrices.

Usage

## S3 method for class 'lefkoMat'elasticity3(  mats,  stochastic = FALSE,  times = 10000,  tweights = NA,  seed = NA,  sparse = "auto",  append_mats = FALSE,  ...)

Arguments

Value

This function returns an object of classlefkoElas, which is alist with 8 elements. The first,h_elasmats, is a list of historicalelasticity matrices (NULL if an ahMPM is used as input). The second,ah_elasmats, is a list of either ahistorical elasticity matrices if anahMPM is used as input, or, if an hMPM is used as input, then the result is alist of elasticity matrices in which historical elasticities have been summedby the stage in occasionst andt+1 to producehistorically-corrected elasticity matrices, which are equivalent in dimensionto ahistorical elasticity matrices but reflect the effects of stage inoccasiont-1. The third element,hstages, is a data frameshowing historical stage pairs (NULL if ahMPM used as input). The fourthelement,agestages, shows age-stage combinations in the order used inage-by-stage MPMs, if suppled. The fifth element,ahstages, is a dataframe showing the order of ahistorical stages. The last 3 elements are the A,U, and F portions of the input.

Notes

Deterministic elasticities are estimated as eqn. 9.72 in Caswell (2001,Matrix Population Models). Stochastic elasticities are estimated as eqn.14.99 in Caswell (2001). Note that stochastic elasticities are of thestochastic\lambda, while stochastic sensitivities are with regard tothe log of the stochastic\lambda.

Speed can sometimes be increased by shifting from automatic sparse matrixdetermination to forced dense or sparse matrix projection. This will mostlikely occur when matrices have between 30 and 300 rows and columns.Defaults work best when matrices are very small and dense, or very large andsparse.

Thetime_weights,steps, andforce_sparse arguments arenow deprecated. Instead, please use thetweights,times, andsparse arguments.

See Also

elasticity3()

elasticity3.dgCMatrix()

elasticity3.matrix()

elasticity3.list()

elasticity3.lefkoMatList()

summary.lefkoElas()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")elasticity3(ehrlen3, stochastic = TRUE)# Cypripedium exampledata(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")elasticity3(cypmatrix2r)

Estimate Elasticity of Population Growth Rate of a lefkoMatList Object

Description

elasticity3.lefkoMatList() returns the elasticities of population growthgrowth rate to elements of allA matrices in all bootstrappedlefkoMat objects within an entered object of classlefkoMatList. If deterministic, then\lambda is taken as thepopulation growth rate. If stochastic, then stochastic\lambda, orthe stochastic growth rate, is taken as the population growth rate. Thisfunction can handle large and sparse matrices, and so can be used with largehistorical matrices, IPMs, age x stage matrices, as well as smallerahistorical matrices.

Usage

## S3 method for class 'lefkoMatList'elasticity3(  mats,  stochastic = FALSE,  times = 10000,  tweights = NA,  seed = NA,  sparse = "auto",  append_mats = FALSE,  ...)

Arguments

Value

This function returns a list with two elements. The first is anobject of classlefkoElas that contains the mean elasticity matricesof the bootstrapped sensitivity matrices. The second is a list containinglefkoElas objects giving the elasticity matrices of all bootstrappedmatrices.

Within these lists are objects of classlefkoElas, which are comprisedof lists of 8 elements. The first,h_elasmats, is a list of historicalelasticity matrices (NULL if an ahMPM is used as input). The second,ah_elasmats, is a list of either ahistorical elasticity matrices if anahMPM is used as input, or, if an hMPM is used as input, then the result is alist of elasticity matrices in which historical elasticities have been summedby the stage in occasionst andt+1 to producehistorically-corrected elasticity matrices, which are equivalent in dimensionto ahistorical elasticity matrices but reflect the effects of stage inoccasiont-1. The third element,hstages, is a data frameshowing historical stage pairs (NULL if ahMPM used as input). The fourthelement,agestages, shows age-stage combinations in the order used inage-by-stage MPMs, if suppled. The fifth element,ahstages, is a dataframe showing the order of ahistorical stages. The last 3 elements are the A,U, and F portions of the input.

Notes

Deterministic elasticities are estimated as eqn. 9.72 in Caswell (2001,Matrix Population Models). Stochastic elasticities are estimated as eqn.14.99 in Caswell (2001). Note that stochastic elasticities are of thestochastic\lambda, while stochastic sensitivities are with regard tothe log of the stochastic\lambda.

Speed can sometimes be increased by shifting from automatic sparse matrixdetermination to forced dense or sparse matrix projection. This will mostlikely occur when matrices have between 30 and 300 rows and columns.Defaults work best when matrices are very small and dense, or very large andsparse.

Thetime_weights,steps, andforce_sparse arguments arenow deprecated. Instead, please use thetweights,times, andsparse arguments.

See Also

elasticity3()

elasticity3.lefkoMat()

elasticity3.dgCMatrix()

elasticity3.matrix()

elasticity3.list()

summary.lefkoElas()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathvert_boot <- bootstrap3(lathvert, reps = 3)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3_boot <- rlefko3(data = lathvert_boot, stageframe = lathframe,  year = "all", stages = c("stage3", "stage2", "stage1"),  supplement = lathsupp3, yearcol = "year2", indivcol = "individ")elasticity3(ehrlen3_boot, stochastic = TRUE)# Cypripedium exampledata(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypraw_v1_boot <- bootstrap3(cypraw_v1, reps = 3)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r_boot <- rlefko2(data = cypraw_v1_boot, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")elasticity3(cypmatrix2r_boot)

Estimate Elasticity of Population Growth Rate of a List of Matrices

Description

elasticity3.list() returns the elasticities of lambda to elementsof a single matrix. This function can handle large and sparse matrices, and so can be used with large historical matrices, IPMs, age x stage matrices,as well as smaller ahistorical matrices.

Usage

## S3 method for class 'list'elasticity3(  mats,  stochastic = FALSE,  times = 10000,  tweights = NA,  historical = FALSE,  seed = NA,  sparse = "auto",  append_mats = FALSE,  ...)

Arguments

Value

This function returns an object of classlefkoElas, which is alist with 8 elements. The first,h_elasmats, is a list of historicalelasticity matrices, though in the standard list case it returns a NULLvalue. The second,ah_elasmats, is a list of ahistorical elasticitymatrices. The third element,hstages, the fourth element,agestages, and the fifth element,ahstages, are set to NULL.The last 3 elements are the original A matrices in element A, followed byNULL values for the U and F elements.

Notes

Deterministic elasticities are estimated as eqn. 9.72 in Caswell (2001,Matrix Population Models). Stochastic elasticities are estimated as eqn.14.99 in Caswell (2001). Note that stochastic elasticities are of stochastic\lambda, while stochastic sensitivities are with regard to the log ofthe stochastic\lambda.

Speed can sometimes be increased by shifting from automatic sparse matrixdetermination to forced dense or sparse matrix projection. This will mostlikely occur when matrices have between 30 and 300 rows and columns.Defaults work best when matrices are very small and dense, or very large andsparse.

Thetime_weights,steps, andforce_sparse arguments arenow deprecated. Instead, please use thetweights,times, andsparse arguments.

See Also

elasticity3()

elasticity3.lefkoMat()

elasticity3.matrix()

elasticity3.dgCMatrix()

elasticity3.lefkoMatList()

summary.lefkoElas()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")elasticity3(ehrlen3$A, stochastic = TRUE)# Cypripedium exampledata(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")elasticity3(cypmatrix2r$A)

Estimate Elasticity of Population Growth Rate of a Single Matrix

Description

elasticity3.matrix() returns the elasticities of lambda to elementsof a single matrix. Because this handles only one matrix, the elasticitiesare inherently deterministic and based on the dominant eigen value as thebest metric of the population growth rate. This function can handle large andsparse matrices, and so can be used with large historical matrices, IPMs,age x stage matrices, as well as smaller ahistorical matrices.

Usage

## S3 method for class 'matrix'elasticity3(mats, sparse = "auto", ...)

Arguments

Value

This function returns a single elasticity matrix.

Notes

Speed can sometimes be increased by shifting from automatic sparse matrixdetermination to forced dense or sparse matrix projection. This will mostlikely occur when matrices have between 30 and 300 rows and columns.Defaults work best when matrices are very small and dense, or very large andsparse.

Theforce_sparse argument is now deprecated. Please usesparseinstead.

See Also

elasticity3()

elasticity3.lefkoMat()

elasticity3.list()

elasticity3.dgCMatrix()

elasticity3.lefkoMatList()

summary.lefkoElas()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")ehrlen3mean <- lmean(ehrlen3)elasticity3(ehrlen3mean$A[[1]])

Project Function-based Matrix Projection Model

Description

Functionf_projection3() develops and projects function-based matrixmodels. Unlikeprojection3(), which uses matrices provided asinput via already createdlefkoMat objects, functionf_projection3() creates matrices at each time step from vital ratemodels and parameter inputs provided. Projections may be stochastic or not,and may be density dependent in either case. Also handles replication.

Usage

f_projection3(  format,  prebreeding = TRUE,  start_age = NA_integer_,  last_age = NA_integer_,  fecage_min = NA_integer_,  fecage_max = NA_integer_,  cont = TRUE,  stochastic = FALSE,  standardize = FALSE,  growthonly = TRUE,  repvalue = FALSE,  integeronly = FALSE,  substoch = 0L,  ipm_cdf = TRUE,  nreps = 1L,  times = 10000L,  repmod = 1,  exp_tol = 700,  theta_tol = 1e+08,  random_inda = FALSE,  random_indb = FALSE,  random_indc = FALSE,  err_check = FALSE,  quiet = FALSE,  data = NULL,  stageframe = NULL,  supplement = NULL,  repmatrix = NULL,  overwrite = NULL,  modelsuite = NULL,  paramnames = NULL,  year = NULL,  patch = NULL,  sp_density = NULL,  ind_terms = NULL,  ann_terms = NULL,  dev_terms = NULL,  surv_model = NULL,  obs_model = NULL,  size_model = NULL,  sizeb_model = NULL,  sizec_model = NULL,  repst_model = NULL,  fec_model = NULL,  jsurv_model = NULL,  jobs_model = NULL,  jsize_model = NULL,  jsizeb_model = NULL,  jsizec_model = NULL,  jrepst_model = NULL,  jmatst_model = NULL,  start_vec = NULL,  start_frame = NULL,  tweights = NULL,  density = NULL,  density_vr = NULL,  stage_weights = NULL,  sparse = NULL)

Arguments

Value

If running a single (including replicated) projection, then theoutput is a list of classlefkoProj, which always includes the firstthree elements of the following, and also includes the remaining elementsbelow when alefkoMat object is used as input:

If running bootstrapped projections (i.e. usinghfvlist andlefkoModList input), then will output an object of classlefkoProjList, which is just a simple list oflefkoProjobjects.

Notes

Population projection can be a very time-consuming activity, and it is mosttime-consuming when matrices need to be created at each time step. We havecreated this function to work as quickly as possible, but some options willslow analysis. First, theerr_check option should always be set toFALSE, as the added created output will not only slow the analysisdown but also potentially crash the memory if matrices are large enough.Second, therepvalue option should be set toFALSE unlessreproductive values are genuinely needed, since this step requiresconcurrent backward projection and so in some cases may double total runtime. Next, if the only needed data is the total population size andage/stage structure at each time step, then settinggrowthonly = TRUEwill yield the quickest possible run time. Finally, the default behavior ofthe function is to round down fractional values of individuals, and to stoprunning projections (replicates) when the population drops to 0. Settingintegeronly = FALSE will have the impact of increasing runtime,potentially dramatically, since the population can reach a point in whichthe population size is extremely small but not equal to 0.

Projections with large matrices may take a long time to run. To assess thelikely running time, try using a low number of iterations on a singlereplicate first. For example, setnreps = 1 andtimes = 10 fora trial run. If a full run is set and takes too long, press the STOP buttonin RStudio to cancel the projection run, or clickesc.

This function currently allows three forms of density dependence. The firstmodifies matrix elements on the basis of the input provided in optiondensity, and so alters matrix elements once the matrix has alreadybeen created. The second form alters the vital rates estimated, and soestimates matrix elements using vital rate values already modified bydensity. This second form uses the input provided in optiondensity_vr. These two forms of density dependence utilize theprojected population size at some time to make these alterations. The thirdform of density dependence also alters the vital rates, but using spatialdensity supplied via optionsp_density and only in vital rates inwhich spatial density is included as a fixed factor in the associatedvital rate model.

When running density dependent simulations involving user-set exponents,such as the beta term in the Ricker function and both the alpha and betaterms in the Usher function, values above or below the computer limits maycause unpredictable behavior. Noted odd behavior includes sudden shifts inpopulation size to negative values. This function produces warnings whensuch values are used, and the values used for warnings may be reset with theexp_tol term. In addition, this function resets beta values for theRicker function automatically to positive or negativeexp_tol, givinga warning when doing so.

Consistently positive population growth can quickly lead to population sizenumbers larger than can be handled computationally. In that circumstance, acontinuously rising population size will suddenly becomeNaN for theremainder of the projection.

This function does not reduce the dimensionality of matrices developed forprojection.

Speed can sometimes be increased by shifting from automatic sparse matrixdetermination to forced dense or sparse matrix projection. This will mostlikely occur when matrices have between 30 and 300 rows and columns.Defaults work best when matrices are very small and dense, or very large andsparse.

Some issues may arise in first-order Markovian stochastic projections iftheyear argument is used. Use the matrix input in thetweights argument to eliminate any years from consideration that arenot needed.

See Also

start_input()

density_input()

density_vr()

projection3()

flefko3()

flefko2()

aflefko2()

fleslie()

append_lP()

summary.lefkoProj()

plot.lefkoProj()

Examples

data(lathyrus)sizevector <- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,  9)stagevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",  "Sz5nr", "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r",   "Sz4r", "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")repvector <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)obsvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)matvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,   0)indataset <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)binvec <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,   0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)lathframeln <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   immstatus = immvector, indataset = indataset, binhalfwidth = binvec,   propstatus = propvector)lathvertln <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,   juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "Intactseed88",  fecacol = "Intactseed88", deadacol = "Dead1988",   nonobsacol = "Dormant1988", stageassign = lathframeln, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, NAas0 = TRUE, censor = TRUE)lathvertln$feca2 <- round(lathvertln$feca2)lathvertln$feca1 <- round(lathvertln$feca1)lathvertln$feca3 <- round(lathvertln$feca3)lathvertln_adults <- subset(lathvertln, stage2index > 2)surv_model <- glm(alive3 ~ sizea2 + sizea1 + as.factor(patchid) +  as.factor(year2), data = lathvertln_adults, family = "binomial")obs_data <- subset(lathvertln_adults, alive3 == 1)obs_model <- glm(obsstatus3 ~ as.factor(patchid), data = obs_data,  family = "binomial")size_data <- subset(obs_data, obsstatus3 == 1)siz_model <- lm(sizea3 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid) +  as.factor(year2), data = size_data)reps_model <- glm(repstatus3 ~ sizea2 + sizea1 + as.factor(patchid) +  as.factor(year2), data = size_data, family = "binomial")fec_data <- subset(lathvertln_adults, repstatus2 == 1)fec_model <- glm(feca2 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid),  data = fec_data, family = "poisson")lathvertln_juvs <- subset(lathvertln, stage2index < 3)jsurv_model <- glm(alive3 ~ as.factor(patchid), data = lathvertln_juvs,  family = "binomial")jobs_data <- subset(lathvertln_juvs, alive3 == 1)jobs_model <- glm(obsstatus3 ~ 1, family = "binomial", data = jobs_data)jsize_data <- subset(jobs_data, obsstatus3 == 1)jsiz_model <- lm(sizea3 ~ as.factor(year2), data = jsize_data)jrepst_model <- 0jmatst_model <- 1mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[3] <- "patchid"mod_params$modelparams[4] <- "alive3"mod_params$modelparams[5] <- "obsstatus3"mod_params$modelparams[6] <- "sizea3"mod_params$modelparams[9] <- "repstatus3"mod_params$modelparams[11] <- "feca2"mod_params$modelparams[12] <- "sizea2"mod_params$modelparams[13] <- "sizea1"mod_params$modelparams[18] <- "repstatus2"mod_params$modelparams[19] <- "repstatus1"lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "mat", "Sd", "Sdl"),   stage2 = c("Sd", "Sd", "Sd", "Sd", "Sdl", "rep", "rep"),  stage1 = c("Sd", "rep", "Sd", "rep", "Sd", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, "mat", NA, NA),  eststage2 = c(NA, NA, NA, NA, "Sdl", NA, NA),  eststage1 = c(NA, NA, NA, NA, "Sdl", NA, NA),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, 0.345, 0.054),  type = c(1, 1, 1, 1, 1, 3, 3), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframeln, historical = TRUE)# While we do not use MPMs to initialize f_projections3(), we do use MPMs to# initialize functions start_input() and density_input().lathmat3ln <- flefko3(year = "all", patch = "all", data = lathvertln,  stageframe = lathframeln, supplement = lathsupp3, paramnames = mod_params,  surv_model = surv_model, obs_model = obs_model, size_model = siz_model,  repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,  jobs_model = jobs_model, jsize_model = jsiz_model,  jrepst_model = jrepst_model, jmatst_model = jmatst_model, reduce = FALSE)e3m_sv <- start_input(lathmat3ln, stage2 = "Sd", stage1 = "Sd", value = 1000)dyn7 <- c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,  FALSE, FALSE, FALSE, FALSE, FALSE)dst7 <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)dal7 <- c(0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)dbe7 <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)e3d_vr <- density_vr(density_yn = dyn7, style = dst7, alpha = dal7,  beta = dbe7)trial7_dvr_1 <- f_projection3(format = 1, data = lathvertln, supplement = lathsupp3,  paramnames = mod_params, stageframe = lathframeln, nreps = 2,  surv_model = surv_model, obs_model = obs_model, size_model = siz_model,  repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,  jobs_model = jobs_model, jsize_model = jsiz_model,  jrepst_model = jrepst_model, jmatst_model = jmatst_model,  times = 100, stochastic = TRUE, standardize = FALSE, growthonly = TRUE,  substoch = 0, sp_density = 0, start_frame = e3m_sv, density_vr = e3d_vr)

Create Function-based Ahistorical Matrix Projection Model

Description

Functionflefko2() returns ahistorical MPMs corresponding to thepatches and occasions given, including the associated component transitionand fecundity matrices, a data frame detailing the characteristics of theahistorical stages used, and a data frame characterizing the patch andoccasion combinations corresponding to these matrices.

Usage

flefko2(  year = "all",  patch = "all",  stageframe,  supplement = NULL,  repmatrix = NULL,  overwrite = NULL,  data = NULL,  modelsuite = NULL,  surv_model = NULL,  obs_model = NULL,  size_model = NULL,  sizeb_model = NULL,  sizec_model = NULL,  repst_model = NULL,  fec_model = NULL,  jsurv_model = NULL,  jobs_model = NULL,  jsize_model = NULL,  jsizeb_model = NULL,  jsizec_model = NULL,  jrepst_model = NULL,  jmatst_model = NULL,  paramnames = NULL,  inda = NULL,  indb = NULL,  indc = NULL,  annua = NULL,  annub = NULL,  annuc = NULL,  surv_dev = 0,  obs_dev = 0,  size_dev = 0,  sizeb_dev = 0,  sizec_dev = 0,  repst_dev = 0,  fec_dev = 0,  jsurv_dev = 0,  jobs_dev = 0,  jsize_dev = 0,  jsizeb_dev = 0,  jsizec_dev = 0,  jrepst_dev = 0,  jmatst_dev = 0,  density = NA,  fecmod = 1,  random.inda = FALSE,  random.indb = FALSE,  random.indc = FALSE,  negfec = FALSE,  ipm_method = "CDF",  reduce = FALSE,  simple = FALSE,  err_check = FALSE,  exp_tol = 700,  theta_tol = 1e+08,  sparse_output = FALSE)

Arguments

Value

If the user inputs a standardlefkoMod orvrm_inputobject in argumentmodelsuite, or individual vital rate models areinput separately,then this function will return an object of classlefkoMat. If the user inputs an object of classlefkoModListin argumentmodelsuite, then the output will be an object of classlefkoMatList, in which each element is an object of classlefkoMat.

AlefkoMat object is a list that holds one full matrix projectionmodel and all of its metadata. The structure has the following elements:

Notes

Unlikerlefko2(),rlefko3(),arlefko2(), andrleslie(), this function does notcurrently distinguish populations. Users wishing to use the same vital ratemodels across populations should label them as patches (though we do notadvise this approach, as populations should typically be treated asstatistically independent).

This function will yield incorrect estimates if the models utilizedincorporate state in occasiont-1. Only use models developed testingfor ahistorical effects.

The default behavior of this function is to estimate fecundity with regardsto transitions specified via associated fecundity multipliers in thesupplement. If this field is left empty, then fecundity will beestimated at full for all transitions leading from reproductive stages toimmature and propagule stages.

Users may at times wish to estimate MPMs using a dataset incorporatingmultiple patches or subpopulations, but without discriminating between thosepatches or subpopulations. Should the aim of analysis be a general MPM thatdoes not distinguish these patches or subpopulations, themodelsearch() run should not include patch terms.

Input options including multiple variable names must be entered in the orderof variables in occasiont+1 andt. Rearranging the order willlead to erroneous calculations, and may lead to fatal errors.

Care should be taken to match the random status of year and patch to thestates of those variables within themodelsuite. If they do not match,then they will be treated as zeroes in vital rate estimation.

Theipm_method function gives the option of using two different meansof estimating the probability of size transition. The midpoint method("midpoint") refers to the method in which the probability isestimated by first estimating the probability associated with transition fromthe exact size at the midpoint of the size class using the correspondingprobability density function, and then multiplying that value by the binwidth of the size class. Doak et al. 2021 (Ecological Monographs) noted thatthis method can produce biased results, with total size transitionsassociated with a specific size not totaling to 1.0 and even specific sizetransition probabilities capable of being estimated at values greater than1.0. The alternative and default method,"CDF", uses the correspondingcumulative density function to estimate the probability of size transition asthe cumulative probability of size transition at the greater limit of thesize class minus the cumulative probability of size transition at the lowerlimit of the size class. The latter method avoids this bias. Note, however,that both methods are exact and unbiased for negative binomial and Poissondistributions.

Under the Gaussian and gamma size distributions, the number of estimatedparameters may differ between the twoipm_method settings. Becausethe midpoint method has a tendency to incorporate upward bias in theestimation of size transition probabilities, it is more likely to yield non-zero values when the true probability is extremely close to 0. This willresult in thesummary.lefkoMat function yielding higher numbers ofestimated parameters than theipm_method = "CDF" yields in some cases.

Using theerr_check option will produce a matrix of 7 columns, eachcharacterizing a different vital rate. The product of each row yields anelement in the associatedU matrix. The number and order of elementsin each column of this matrix matches the associated matrix in column vectorformat. Use of this option is generally for the purposes of debugging code.

Individual covariates are treated as categorical only if they are set asrandom terms. Fixed categorical individual covariates are currently notallowed. However, such terms may be supplied if themodelsuite optionis set to avrm_input object. In that case, the user should also setthe logical random switch for the individual covariate to be used toTRUE (e.g.,random.inda = TRUE).

See Also

mpm_create()

flefko3()

aflefko2()

arlefko2()

fleslie()

rlefko3()

rlefko2()

rleslie()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,  9)stagevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",   "Sz5nr", "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r",   "Sz4r", "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")repvector <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)obsvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)matvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0)indataset <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)binvec <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,  0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)lathframeln <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvertln <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,   patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "Intactseed88",  fecacol = "Intactseed88", deadacol = "Dead1988",   nonobsacol = "Dormant1988", stageassign = lathframeln,  stagesize = "sizea", censorcol = "Missing1988", censorkeep = NA,  NAas0 = TRUE, censor = TRUE)lathvertln$feca2 <- round(lathvertln$feca2)lathvertln$feca1 <- round(lathvertln$feca1)lathvertln$feca3 <- round(lathvertln$feca3)lathvertln_adults <- subset(lathvertln, stage2index > 2)surv_model <- glm(alive3 ~ sizea2 + as.factor(patchid),  data = lathvertln_adults, family = "binomial")obs_data <- subset(lathvertln_adults, alive3 == 1)obs_model <- glm(obsstatus3 ~ as.factor(patchid), data = obs_data,  family = "binomial")size_data <- subset(obs_data, obsstatus3 == 1)siz_model <- lm(sizea3 ~ sizea2 + repstatus2 + as.factor(patchid) +   as.factor(year2), data = size_data)reps_model <- glm(repstatus3 ~ sizea2 + as.factor(patchid) + as.factor(year2),  data = size_data, family = "binomial")fec_data <- subset(lathvertln_adults, repstatus2 == 1)fec_model <- glm(feca2 ~ sizea2 + as.factor(patchid) + as.factor(year2),  data = fec_data, family = "poisson")lathvertln_juvs <- subset(lathvertln, stage2index < 3)jsurv_model <- glm(alive3 ~ as.factor(patchid), data = lathvertln_juvs,  family = "binomial")jobs_data <- subset(lathvertln_juvs, alive3 == 1)jobs_model <- glm(obsstatus3 ~ 1, family = "binomial", data = jobs_data)jsize_data <- subset(jobs_data, obsstatus3 == 1)jsiz_model <- lm(sizea3 ~ as.factor(year2), data = jsize_data)jrepst_model <- 0jmatst_model <- 1mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[3] <- "patchid"mod_params$modelparams[5] <- "obsstatus3"mod_params$modelparams[6] <- "sizea3"mod_params$modelparams[9] <- "repstatus3"mod_params$modelparams[11] <- "feca2"mod_params$modelparams[12] <- "sizea2"mod_params$modelparams[18] <- "repstatus2"lathsupp2 <- supplemental(stage3 = c("Sd", "Sdl", "Sd", "Sdl"),   stage2 = c("Sd", "Sd", "rep", "rep"),  givenrate = c(0.345, 0.054, NA, NA),  multiplier = c(NA, NA, 0.345, 0.054),  type = c(1, 1, 3, 3), stageframe = lathframeln, historical = FALSE)lathmat2ln <- flefko2(year = "all", patch = "all", data = lathvertln,  stageframe = lathframeln, supplement = lathsupp2, paramnames = mod_params,  surv_model = surv_model, obs_model = obs_model, size_model = siz_model,  repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,  jobs_model = jobs_model, jsize_model = jsiz_model,  jrepst_model = jrepst_model, jmatst_model = jmatst_model, reduce = FALSE)# Cypripedium example using three size metrics for classificationdata(cypdata)sizevector_f <- c(0, 0, 0, 0, 0, 0, seq(1, 12, by = 1), seq(0, 9, by = 1),  seq(0, 8, by = 1), seq(0, 7, by = 1), seq(0, 6, by = 1), seq(0, 5, by = 1),  seq(0, 4, by = 1), seq(0, 3, by = 1), 0, 1, 2, 0, 1, 0,   0, 0, 1, 0)sizebvector_f <- c(0, 0, 0, 0, 0, 0, rep(0, 12), rep(1, 10), rep(2, 9),  rep(3, 8), rep(4, 7), rep(5, 6), rep(6, 5), rep(7, 4), rep(8, 3), 9, 9, 10,   0, 1, 1, 2)sizecvector_f <- c(0, 0, 0, 0, 0, 0, rep(0, 12), rep(0, 10), rep(0, 9),  rep(0, 8), rep(0, 7), rep(0, 6), rep(0, 5), rep(0, 4), 0, 0, 0, 0, 0, 0,   1, 1, 1, 1)stagevector_f <- c("DS", "P1", "P2", "P3", "Sdl", "Dorm", "V1 I0 D0",  "V2 I0 D0", "V3 I0 D0", "V4 I0 D0", "V5 I0 D0", "V6 I0 D0", "V7 I0 D0",  "V8 I0 D0", "V9 I0 D0", "V10 I0 D0", "V11 I0 D0", "V12 I0 D0", "V0 I1 D0",  "V1 I1 D0", "V2 I1 D0", "V3 I1 D0", "V4 I1 D0", "V5 I1 D0", "V6 I1 D0",  "V7 I1 D0", "V8 I1 D0", "V9 I1 D0", "V0 I2 D0", "V1 I2 D0", "V2 I2 D0",  "V3 I2 D0", "V4 I2 D0", "V5 I2 D0", "V6 I2 D0", "V7 I2 D0", "V8 I2 D0",  "V0 I3 D0", "V1 I3 D0", "V2 I3 D0", "V3 I3 D0", "V4 I3 D0", "V5 I3 D0",  "V6 I3 D0", "V7 I3 D0", "V0 I4 D0", "V1 I4 D0", "V2 I4 D0", "V3 I4 D0",  "V4 I4 D0", "V5 I4 D0", "V6 I4 D0", "V0 I5 D0", "V1 I5 D0", "V2 I5 D0",  "V3 I5 D0", "V4 I5 D0", "V5 I5 D0", "V0 I6 D0", "V1 I6 D0", "V2 I6 D0",  "V3 I6 D0", "V4 I6 D0", "V0 I7 D0", "V1 I7 D0", "V2 I7 D0", "V3 I7 D0",  "V0 I8 D0", "V1 I8 D0", "V2 I8 D0", "V0 I9 D0", "V1 I9 D0", "V0 I10 D0",  "V0 I0 D1", "V0 I1 D1", "V1 I1 D1", "V0 I2 D1")repvector_f <- c(0, 0, 0, 0, 0, rep(0, 13), rep(1, 59))obsvector_f <- c(0, 0, 0, 0, 0, 0, rep(1, 71))matvector_f <- c(0, 0, 0, 0, 0, rep(1, 72))immvector_f <- c(0, 1, 1, 1, 1, rep(0, 72))propvector_f <- c(1, rep(0, 76))indataset_f <- c(0, 0, 0, 0, 0, rep(1, 72))binvec_f <- c(0, 0, 0, 0, 0, rep(0.5, 72))binbvec_f <- c(0, 0, 0, 0, 0, rep(0.5, 72))bincvec_f <- c(0, 0, 0, 0, 0, rep(0.5, 72))vertframe_f <- sf_create(sizes = sizevector_f, sizesb = sizebvector_f,  sizesc = sizecvector_f, stagenames = stagevector_f, repstatus = repvector_f,  obsstatus = obsvector_f, propstatus = propvector_f, immstatus = immvector_f,  matstatus = matvector_f, indataset = indataset_f, binhalfwidth = binvec_f,  binhalfwidthb = binbvec_f, binhalfwidthc = bincvec_f)vert_data_f <- verticalize3(cypdata, noyears = 6, firstyear = 2004,  individcol = "plantid", blocksize = 4, sizeacol = "Veg.04",  sizebcol = "Inf.04", sizeccol = "Inf2.04", repstracol = "Inf.04",  repstrbcol = "Inf2.04", fecacol = "Pod.04", censorcol = "censor",  censorkeep = 1, censorRepeat = FALSE, stageassign = vertframe_f,  stagesize = "sizeabc", NAas0 = TRUE, censor = FALSE)surv_model <- glm(alive3 ~ sizea2 + sizeb2, data = vert_data_f,  family = "binomial")obs_data <- subset(vert_data_f, alive3 == 1)obs_model <- glm(obsstatus3 ~ sizeb2 + as.factor(year2), data = obs_data,  family = "binomial")size_data <- subset(obs_data, obsstatus3 == 1)siz_model <- MASS::glm.nb(sizea3 ~ sizea2 + sizeb2 + as.factor(year2),  data = size_data)sizb_model <- glm(sizeb3 ~ sizea2 + sizeb2 + repstatus2 + as.factor(year2),  data = size_data, family = "poisson")sizc_model <- glm(sizec3 ~ repstatus2, data = size_data, family = "poisson")reps_model <- glm(repstatus3 ~ sizea2 + sizeb2 + repstatus2 + as.factor(year2),  data = size_data, family = "binomial")fec_data <- subset(vert_data_f, repstatus2 == 1)fec_model <- glm(feca2 ~ sizeb2 + as.factor(year2), data = fec_data,  family = "poisson")mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[3] <- "patchid"mod_params$modelparams[4] <- "alive3"mod_params$modelparams[5] <- "obsstatus3"mod_params$modelparams[6] <- "sizea3"mod_params$modelparams[9] <- "repstatus3"mod_params$modelparams[11] <- "feca2"mod_params$modelparams[12] <- "sizea2"mod_params$modelparams[18] <- "repstatus2"vertsupp2f <- supplemental(stage3 = c("DS", "P1", "P2", "P3", "Sdl", "Sdl",    "Dorm", "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", "DS", "P1"),  stage2 = c("DS", "DS", "P1", "P2", "P3", "Sdl", "Sdl", "Sdl", "Sdl", "Sdl",    "rep", "rep"),   eststage3 = c(NA, NA, NA, NA, NA, NA, "Dorm", "V1 I0 D0", "V2 I0 D0",    "V3 I0 D0", NA, NA),   eststage2 = c(NA, NA, NA, NA, NA, NA, "V1 I0 D0", "V1 I0 D0", "V1 I0 D0",    "V1 I0 D0", NA, NA),   givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, 0.40, NA, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.5 * 5000,    0.5 * 5000),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3), stageframe = vertframe_f,  historical = FALSE)vert_mats_f2 <- flefko2(stageframe = vertframe_f, supplement = vertsupp2f,  data = vert_data_f, surv_model = surv_model, obs_model = obs_model,  size_model = siz_model, sizeb_model = sizb_model, sizec_model = sizc_model,  repst_model = reps_model, fec_model = fec_model, paramnames = mod_params)

Create Function-based Historical Matrix Projection Model

Description

Functionflefko3() returns function-based historical MPMscorresponding to the patches and occasions given, including the associatedcomponent transition and fecundity matrices, data frames detailing thecharacteristics of the ahistorical stages used and historical stage pairscreated, and a data frame characterizing the patch and occasion combinationscorresponding to these matrices.

Usage

flefko3(  year = "all",  patch = "all",  stageframe,  supplement = NULL,  repmatrix = NULL,  overwrite = NULL,  data = NULL,  modelsuite = NULL,  surv_model = NULL,  obs_model = NULL,  size_model = NULL,  sizeb_model = NULL,  sizec_model = NULL,  repst_model = NULL,  fec_model = NULL,  jsurv_model = NULL,  jobs_model = NULL,  jsize_model = NULL,  jsizeb_model = NULL,  jsizec_model = NULL,  jrepst_model = NULL,  jmatst_model = NULL,  paramnames = NULL,  inda = NULL,  indb = NULL,  indc = NULL,  annua = NULL,  annub = NULL,  annuc = NULL,  surv_dev = 0,  obs_dev = 0,  size_dev = 0,  sizeb_dev = 0,  sizec_dev = 0,  repst_dev = 0,  fec_dev = 0,  jsurv_dev = 0,  jobs_dev = 0,  jsize_dev = 0,  jsizeb_dev = 0,  jsizec_dev = 0,  jrepst_dev = 0,  jmatst_dev = 0,  density = NA,  fecmod = 1,  random.inda = FALSE,  random.indb = FALSE,  random.indc = FALSE,  negfec = FALSE,  format = "ehrlen",  ipm_method = "CDF",  reduce = FALSE,  simple = FALSE,  err_check = FALSE,  exp_tol = 700,  theta_tol = 1e+08,  sparse_output = FALSE)

Arguments

Value

If the user inputs a standardlefkoMod orvrm_inputobject in argumentmodelsuite, or individual vital rate models areinput separately,then this function will return an object of classlefkoMat. If the user inputs an object of classlefkoModListin argumentmodelsuite, then the output will be an object of classlefkoMatList, in which each element is an object of classlefkoMat.

AlefkoMat object is a list that holds one full matrix projectionmodel and all of its metadata. The structure has the following elements:

Notes

Unlikerlefko2(),rlefko3(),arlefko2(), andrleslie(), this function does notcurrently distinguish populations. Users wishing to use the same vital ratemodels across populations should label them as patches (though we do notadvise this approach, as populations should typically be treated asstatistically independent).

The default behavior of this function is to estimate fecundity with regardsto transitions specified via associated fecundity multipliers in thesupplement. If this field is left empty, then fecundity will beestimated at full for all transitions leading from reproductive stages toimmature and propagule stages.

Users may at times wish to estimate MPMs using a dataset incorporatingmultiple patches or subpopulations, but without discriminating between thosepatches or subpopulations. Should the aim of analysis be a general MPM thatdoes not distinguish these patches or subpopulations, themodelsearch() run should not include patch terms.

Input options including multiple variable names must be entered in the orderof variables in occasiont+1,t, andt-1. Rearrangingthe order will lead to erroneous calculations, and will may lead to fatalerrors.

Theipm_method function gives the option of using two different meansof estimating the probability of size transition. The midpoint method("midpoint") refers to the method in which the probability isestimated by first estimating the probability associated with transition fromthe exact size at the midpoint of the size class using the correspondingprobability density function, and then multiplying that value by the binwidth of the size class. Doak et al. 2021 (Ecological Monographs) noted thatthis method can produce biased results, with total size transitionsassociated with a specific size not totaling to 1.0 and even specific sizetransition probabilities capable of being estimated at values greater than1.0. The alternative and default method,"CDF", uses the correspondingcumulative density function to estimate the probability of size transition asthe cumulative probability of size transition at the greater limit of thesize class minus the cumulative probability of size transition at the lowerlimit of the size class. This latter method avoids this bias. Note, however,that both methods are exact and unbiased for negative binomial and Poissondistributions.

Under the Gaussian and gamma size distributions, the number of estimatedparameters may differ between the twoipm_method settings. Becausethe midpoint method has a tendency to incorporate upward bias in theestimation of size transition probabilities, it is more likely to yield non-zero values when the true probability is extremely close to 0. This willresult in thesummary.lefkoMat function yielding higher numbers ofestimated parameters than theipm_method = "CDF" yields in some cases.

Using theerr_check option will produce a matrix of 7 columns, eachcharacterizing a different vital rate. The product of each row yields anelement in the associatedU matrix. The number and order of elementsin each column of this matrix matches the associated matrix in column vectorformat. Use of this option is generally for the purposes of debugging code.'Individual covariates are treated as categorical only if they are set asrandom terms. Fixed categorical individual covariates are currently notallowed. However, such terms may be supplied if themodelsuite optionis set to avrm_input object. In that case, the user should also setthe logical random switch for the individual covariate to be used toTRUE (e.g.,random.inda = TRUE).

See Also

mpm_create()

flefko2()

aflefko2()

arlefko2()

fleslie()

rlefko3()

rlefko2()

rleslie()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,  9)stagevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",  "Sz5nr", "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r",   "Sz4r", "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")repvector <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)obsvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)matvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,   0)indataset <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)binvec <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,   0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)lathframeln <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   immstatus = immvector, indataset = indataset, binhalfwidth = binvec,   propstatus = propvector)lathvertln <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,   juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "Intactseed88",  fecacol = "Intactseed88", deadacol = "Dead1988",   nonobsacol = "Dormant1988", stageassign = lathframeln, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, NAas0 = TRUE, censor = TRUE)lathvertln$feca2 <- round(lathvertln$feca2)lathvertln$feca1 <- round(lathvertln$feca1)lathvertln$feca3 <- round(lathvertln$feca3)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "mat", "Sd", "Sdl"),   stage2 = c("Sd", "Sd", "Sd", "Sd", "Sdl", "rep", "rep"),  stage1 = c("Sd", "rep", "Sd", "rep", "Sd", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, "mat", NA, NA),  eststage2 = c(NA, NA, NA, NA, "Sdl", NA, NA),  eststage1 = c(NA, NA, NA, NA, "Sdl", NA, NA),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, 0.345, 0.054),  type = c(1, 1, 1, 1, 1, 3, 3), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframeln, historical = TRUE)lathvertln_adults <- subset(lathvertln, stage2index > 2)surv_model <- glm(alive3 ~ sizea2 + sizea1 + as.factor(patchid) +  as.factor(year2), data = lathvertln_adults, family = "binomial")obs_data <- subset(lathvertln_adults, alive3 == 1)obs_model <- glm(obsstatus3 ~ as.factor(patchid), data = obs_data,  family = "binomial")size_data <- subset(obs_data, obsstatus3 == 1)siz_model <- lm(sizea3 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid) +  as.factor(year2), data = size_data)reps_model <- glm(repstatus3 ~ sizea2 + sizea1 + as.factor(patchid) +  as.factor(year2), data = size_data, family = "binomial")fec_data <- subset(lathvertln_adults, repstatus2 == 1)fec_model <- glm(feca2 ~ sizea2 + sizea1 + repstatus1 + as.factor(patchid),  data = fec_data, family = "poisson")lathvertln_juvs <- subset(lathvertln, stage2index < 3)jsurv_model <- glm(alive3 ~ as.factor(patchid), data = lathvertln_juvs,  family = "binomial")jobs_data <- subset(lathvertln_juvs, alive3 == 1)jobs_model <- glm(obsstatus3 ~ 1, family = "binomial", data = jobs_data)jsize_data <- subset(jobs_data, obsstatus3 == 1)jsiz_model <- lm(sizea3 ~ as.factor(year2), data = jsize_data)jrepst_model <- 0jmatst_model <- 1mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[3] <- "patchid"mod_params$modelparams[4] <- "alive3"mod_params$modelparams[5] <- "obsstatus3"mod_params$modelparams[6] <- "sizea3"mod_params$modelparams[9] <- "repstatus3"mod_params$modelparams[11] <- "feca2"mod_params$modelparams[12] <- "sizea2"mod_params$modelparams[13] <- "sizea1"mod_params$modelparams[18] <- "repstatus2"mod_params$modelparams[19] <- "repstatus1"lathmat3ln <- flefko3(year = "all", patch = "all", data = lathvertln,  stageframe = lathframeln, supplement = lathsupp3, paramnames = mod_params,  surv_model = surv_model, obs_model = obs_model, size_model = siz_model,  repst_model = reps_model, fec_model = fec_model, jsurv_model = jsurv_model,  jobs_model = jobs_model, jsize_model = jsiz_model,  jrepst_model = jrepst_model, jmatst_model = jmatst_model, reduce = FALSE)# Cypripedium example using three size metrics for classificationdata(cypdata)sizevector_f <- c(0, 0, 0, 0, 0, 0, seq(1, 12, by = 1), seq(0, 9, by = 1),  seq(0, 8, by = 1), seq(0, 7, by = 1), seq(0, 6, by = 1), seq(0, 5, by = 1),  seq(0, 4, by = 1), seq(0, 3, by = 1), 0, 1, 2, 0, 1, 0,   0, 0, 1, 0)sizebvector_f <- c(0, 0, 0, 0, 0, 0, rep(0, 12), rep(1, 10), rep(2, 9),  rep(3, 8), rep(4, 7), rep(5, 6), rep(6, 5), rep(7, 4), rep(8, 3), 9, 9, 10,   0, 1, 1, 2)sizecvector_f <- c(0, 0, 0, 0, 0, 0, rep(0, 12), rep(0, 10), rep(0, 9),  rep(0, 8), rep(0, 7), rep(0, 6), rep(0, 5), rep(0, 4), 0, 0, 0, 0, 0, 0,   1, 1, 1, 1)stagevector_f <- c("DS", "P1", "P2", "P3", "Sdl", "Dorm", "V1 I0 D0",  "V2 I0 D0", "V3 I0 D0", "V4 I0 D0", "V5 I0 D0", "V6 I0 D0", "V7 I0 D0",  "V8 I0 D0", "V9 I0 D0", "V10 I0 D0", "V11 I0 D0", "V12 I0 D0", "V0 I1 D0",  "V1 I1 D0", "V2 I1 D0", "V3 I1 D0", "V4 I1 D0", "V5 I1 D0", "V6 I1 D0",  "V7 I1 D0", "V8 I1 D0", "V9 I1 D0", "V0 I2 D0", "V1 I2 D0", "V2 I2 D0",  "V3 I2 D0", "V4 I2 D0", "V5 I2 D0", "V6 I2 D0", "V7 I2 D0", "V8 I2 D0",  "V0 I3 D0", "V1 I3 D0", "V2 I3 D0", "V3 I3 D0", "V4 I3 D0", "V5 I3 D0",  "V6 I3 D0", "V7 I3 D0", "V0 I4 D0", "V1 I4 D0", "V2 I4 D0", "V3 I4 D0",  "V4 I4 D0", "V5 I4 D0", "V6 I4 D0", "V0 I5 D0", "V1 I5 D0", "V2 I5 D0",  "V3 I5 D0", "V4 I5 D0", "V5 I5 D0", "V0 I6 D0", "V1 I6 D0", "V2 I6 D0",  "V3 I6 D0", "V4 I6 D0", "V0 I7 D0", "V1 I7 D0", "V2 I7 D0", "V3 I7 D0",  "V0 I8 D0", "V1 I8 D0", "V2 I8 D0", "V0 I9 D0", "V1 I9 D0", "V0 I10 D0",  "V0 I0 D1", "V0 I1 D1", "V1 I1 D1", "V0 I2 D1")repvector_f <- c(0, 0, 0, 0, 0, rep(0, 13), rep(1, 59))obsvector_f <- c(0, 0, 0, 0, 0, 0, rep(1, 71))matvector_f <- c(0, 0, 0, 0, 0, rep(1, 72))immvector_f <- c(0, 1, 1, 1, 1, rep(0, 72))propvector_f <- c(1, rep(0, 76))indataset_f <- c(0, 0, 0, 0, 0, rep(1, 72))binvec_f <- c(0, 0, 0, 0, 0, rep(0.5, 72))binbvec_f <- c(0, 0, 0, 0, 0, rep(0.5, 72))bincvec_f <- c(0, 0, 0, 0, 0, rep(0.5, 72))vertframe_f <- sf_create(sizes = sizevector_f, sizesb = sizebvector_f,  sizesc = sizecvector_f, stagenames = stagevector_f, repstatus = repvector_f,  obsstatus = obsvector_f, propstatus = propvector_f, immstatus = immvector_f,  matstatus = matvector_f, indataset = indataset_f, binhalfwidth = binvec_f,  binhalfwidthb = binbvec_f, binhalfwidthc = bincvec_f)vert_data_f <- verticalize3(cypdata, noyears = 6, firstyear = 2004,  individcol = "plantid", blocksize = 4, sizeacol = "Veg.04",  sizebcol = "Inf.04", sizeccol = "Inf2.04", repstracol = "Inf.04",  repstrbcol = "Inf2.04", fecacol = "Pod.04", censorcol = "censor",  censorkeep = 1, censorRepeat = FALSE, stageassign = vertframe_f,  stagesize = "sizeabc", NAas0 = TRUE, censor = FALSE)vertsupp3f <- supplemental(stage3 = c("DS", "P1", "DS", "P1", "P2", "P2", "P3",    "Sdl", "Sdl", "Sdl", "Dorm", "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", "Dorm",    "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", "mat", "mat", "mat", "mat", "DS", "P1"),  stage2 = c("DS", "DS", "DS", "DS", "P1", "P1", "P2", "P3", "Sdl", "Sdl", "Sdl",    "Sdl", "Sdl", "Sdl", "Sdl", "Sdl", "Sdl", "Sdl", "Dorm", "V1 I0 D0",    "V2 I0 D0", "V3 I0 D0", "rep", "rep"),  stage1 = c("DS", "DS", "rep", "rep", "DS", "rep", "P1", "P2", "P3", "Sdl",    "Sdl", "Sdl", "Sdl", "Sdl", "P3", "P3", "P3", "P3", "Sdl", "Sdl", "Sdl",    "Sdl", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Dorm", "V1 I0 D0",    "V2 I0 D0", "V3 I0 D0", "Dorm", "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", "mat",    "mat", "mat", "mat", NA, NA),   eststage2 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "V1 I0 D0", "V1 I0 D0",    "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0",    "Dorm", "V1 I0 D0", "V2 I0 D0", "V3 I0 D0", NA, NA),  eststage1 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "V1 I0 D0", "V1 I0 D0",    "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0",    "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", "V1 I0 D0", NA, NA),  givenrate = c(0.10, 0.20, 0.10, 0.20, 0.20, 0.20, 0.20, 0.25, 0.40, 0.40, NA,    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,    NA, NA, NA, NA, NA, NA, NA, 0.5 * 5000, 0.5 * 5000),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,    3, 3),  type_t12 = c(1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,    1, 1, 1),  stageframe = vertframe_f, historical = TRUE)surv_model <- glm(alive3 ~ sizea2 + sizeb2, data = vert_data_f,  family = "binomial")obs_data <- subset(vert_data_f, alive3 == 1)obs_model <- glm(obsstatus3 ~ sizeb2 + sizec1 + as.factor(year2),  data = obs_data, family = "binomial")size_data <- subset(obs_data, obsstatus3 == 1)siz_model <- MASS::glm.nb(sizea3 ~ sizea2 + sizea1 + sizeb1, data = size_data)sizb_model <- glm(sizeb3 ~ sizea2 + sizeb2 + sizec1 + repstatus2 + repstatus1 +  as.factor(year2), data = size_data, family = "poisson")sizc_model <- glm(sizec3 ~ sizea1 + repstatus2, data = size_data,  family = "poisson")reps_model <- glm(repstatus3 ~ sizea2 + sizeb2 + repstatus2 + as.factor(year2),  data = size_data, family = "binomial")fec_data <- subset(vert_data_f, repstatus2 == 1)fec_model <- glm(feca2 ~ sizeb2 + as.factor(year2), data = fec_data,  family = "poisson")mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[3] <- "patchid"mod_params$modelparams[4] <- "alive3"mod_params$modelparams[5] <- "obsstatus3"mod_params$modelparams[6] <- "sizea3"mod_params$modelparams[9] <- "repstatus3"mod_params$modelparams[11] <- "feca2"mod_params$modelparams[12] <- "sizea2"mod_params$modelparams[13] <- "sizea1"mod_params$modelparams[18] <- "repstatus2"mod_params$modelparams[19] <- "repstatus1"vert_mats_f3 <- flefko3(stageframe = vertframe_f, supplement = vertsupp3f,  data = vert_data_f, surv_model = surv_model, obs_model = obs_model,  size_model = siz_model, sizeb_model = sizb_model, sizec_model = sizc_model,  repst_model = reps_model, fec_model = fec_model, paramnames = mod_params,  sparse_output = TRUE)

Create Function-based Age-based (Leslie) Matrix Projection Model

Description

Functionfleslie() returns age-based (Leslie) MPMs corresponding tothe patches and occasions given, including the associated componenttransition and fecundity matrices, data frames detailing the characteristicsof the exact ages corresponding to rows and columns in estimated matrices,and a data frame characterizing the patch and occasion combinationscorresponding to these matrices.

Usage

fleslie(  year = "all",  patch = NULL,  prebreeding = TRUE,  data = NULL,  modelsuite = NULL,  surv_model = NULL,  fec_model = NULL,  paramnames = NULL,  supplement = NULL,  start_age = NA,  last_age = NA,  fecage_min = NA,  fecage_max = NA,  continue = TRUE,  inda = NULL,  indb = NULL,  indc = NULL,  annua = NULL,  annub = NULL,  annuc = NULL,  surv_dev = 0,  fec_dev = 0,  density = NA,  fecmod = 1,  random.inda = FALSE,  random.indb = FALSE,  random.indc = FALSE,  negfec = FALSE,  reduce = FALSE,  simple = FALSE,  err_check = FALSE,  exp_tol = 700,  theta_tol = 1e+08,  sparse_output = FALSE)

Arguments

Value

If the user inputs a standardlefkoMod orvrm_inputobject in argumentmodelsuite, or individual vital rate models areinput separately,then this function will return an object of classlefkoMat. If the user inputs an object of classlefkoModListin argumentmodelsuite, then the output will be an object of classlefkoMatList, in which each element is an object of classlefkoMat.

AlefkoMat object is a list that holds one full matrix projectionmodel and all of its metadata. The structure has the following elements:

Notes

Unlikerlefko2(),rlefko3(),arlefko2(), andrleslie(), this function does notcurrently distinguish populations.

This function will yield incorrect estimates if the models utilizedincorporate state in occasiont-1, or any size or reproductive statusterms.

Users may at times wish to estimate MPMs using a dataset incorporatingmultiple patches or subpopulations, but without discriminating between thosepatches or subpopulations. Should the aim of analysis be a general MPM thatdoes not distinguish these patches or subpopulations, themodelsearch() run should not include patch terms.

Input options including multiple variable names must be entered in the orderof variables in occasiont+1 andt. Rearranging the order willlead to erroneous calculations, and may lead to fatal errors.

Care should be taken to match the random status of year and patch to thestates of those variables within the modelsuite. If they do not match, thenthey will be treated as zeroes in vital rate estimation.

Individual covariates are treated as categorical only if they are set asrandom terms. Fixed categorical individual covariates are currently notallowed. However, such terms may be supplied if themodelsuite optionis set to avrm_input object. In that case, the user should also setthe logical random switch for the individual covariate to be used toTRUE (e.g.,random.inda = TRUE).

See Also

mpm_create()

flefko3()

flefko2()

aflefko2()

arlefko2()

rlefko3()

rlefko2()

rleslie()

Examples

data(lathyrus)lathvert_base <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  sizeacol = "Volume88", repstracol = "FCODE88", fecacol = "Intactseed88",  deadacol = "Dead1988", censorcol = "Missing1988", censorkeep = NA,  censor = TRUE, NAas0 = TRUE, NRasRep = TRUE, NOasObs = TRUE)lathvert_base$feca3 <- round(lathvert_base$feca3)lathvert_base$feca2 <- round(lathvert_base$feca2)lathvert_base$feca1 <- round(lathvert_base$feca1)lathvert_age <- subset(lathvert_base, firstseen > 1988)lath_survival <- glm(alive3 ~ obsage + as.factor(year2), data = lathvert_age,  family = "binomial")lath_fecundity <- glm(feca2 ~ obsage + as.factor(year2), data = lathvert_age,  family = "poisson")mod_params <- create_pm(name_terms = TRUE)mod_params$modelparams[22] <- "obsage"lathmat2fleslie <- fleslie(year = "all", data = lathvert_age,  surv_model = lath_survival, fec_model = lath_fecundity,  paramnames = mod_params, fecage_min = 1)

Assess Quality of hfv Datasets

Description

Functionhfv_qc() tests the overall quality of hfv datasets, and alsoruns a series of tests to assess which statistical distributions match thevariables within these datasets. The input format is equivalent to the inputformat of functionmodelsearch(), allowing users to assessvital rate variable distributions assuming the same internal datasetsubsetting used by the latter function and simply copy and pasting theparameter options from one function to the other.

Usage

hfv_qc(  data,  stageframe = NULL,  historical = TRUE,  suite = "size",  vitalrates = c("surv", "size", "fec"),  surv = c("alive3", "alive2", "alive1"),  obs = c("obsstatus3", "obsstatus2", "obsstatus1"),  size = c("sizea3", "sizea2", "sizea1"),  sizeb = c(NA, NA, NA),  sizec = c(NA, NA, NA),  repst = c("repstatus3", "repstatus2", "repstatus1"),  fec = c("feca3", "feca2", "feca1"),  stage = c("stage3", "stage2", "stage1"),  matstat = c("matstatus3", "matstatus2", "matstatus1"),  indiv = "individ",  patch = NA,  year = "year2",  density = NA,  patch.as.random = TRUE,  year.as.random = TRUE,  juvestimate = NA,  juvsize = FALSE,  fectime = 2,  censor = NA,  age = NA,  indcova = NA,  indcovb = NA,  indcovc = NA,  random.indcova = FALSE,  random.indcovb = FALSE,  random.indcovc = FALSE,  test.group = FALSE,  ...)

Arguments

Value

This function yields text output describing the subsets to be used inlinear vital rate modeling. No value or object is returned.

Notes

This function is meant to handle input as would be supplied to functionmodelsearch(). To use most easily, users may copy all input parametersfrom a call to functionmodelsearch(), and paste directly within thisfunction. The exact subsets used in themodelsearch() run will also becreated here.

Tests of Gaussian normality are conducted as Shapiro-Wilk tests via base R'sshapiro.test() function. If datasets with more than 5000 rows aresupplied, functionhfv_qc() will sample 5000 rows from the dataset andconduct the Shapiro-Wilk test on the data sample.

Random factor variables are also tested for the presence of singletoncategories, which are factor values that occur only once in the used datasubset. Singleton categories may cause problems with estimation under mixedmodeling.

Examples

data(lathyrus)sizevector <- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,  9)stagevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr",  "Sz5nr", "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r",   "Sz4r", "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")repvector <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)obsvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)matvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,   0)indataset <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)binvec <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,   0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)lathframeln <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   immstatus = immvector, indataset = indataset, binhalfwidth = binvec,   propstatus = propvector)lathvertln <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,   juvcol = "Seedling1988", sizeacol = "lnVol88", repstracol = "Intactseed88",  fecacol = "Intactseed88", deadacol = "Dead1988",   nonobsacol = "Dormant1988", stageassign = lathframeln, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, NAas0 = TRUE, censor = TRUE)lathvertln$feca2 <- round(lathvertln$feca2)lathvertln$feca1 <- round(lathvertln$feca1)lathvertln$feca3 <- round(lathvertln$feca3)hfv_qc(lathvertln, historical = TRUE, suite = "main",   vitalrates = c("surv", "obs", "size", "repst", "fec"), juvestimate = "Sdl",  indiv = "individ", patch = "patchid", year = "year2",year.as.random = TRUE,  patch.as.random = TRUE)

Create Historical MPMs Assuming No Influence of Individual History

Description

Functionhist_null() uses ahistorical MPMs to create the equivalentMPMs in the structure of historical MPMs. These MPMs have the samedimensions and stage structure of hMPMs but assume no influence ofindividual history, and so can be compared to actual hMPMs.

Usage

hist_null(mpm, format = 1L, err_check = FALSE)

Arguments

Value

An object of classlefkoMat, with the same list structure asthe input object, but withA,U, andF elementsreplaced with lists of historically-structured matrices, and with elementhstages changed fromNA to an index of stage pairscorresponding to the rows and columns of the new matrices. Iferr_check = TRUE, then a list of three data frames showing the valuesused to determine matrix element index values is also exported.

Notes

This function does not currently identify biologically impossibletransitions. Ahistorical transition values are placed in all theoreticallypossible historical transitions.

Examples

sizevector <- c(1, 1, 2, 3)stagevector <- c("Sdl", "Veg", "SmFlo", "LFlo")repvector <- c(0, 0, 1, 1)obsvector <- c(1, 1, 1, 1)matvector <- c(0, 1, 1, 1)immvector <- c(1, 0, 0, 0)propvector <- c(0, 0, 0, 0)indataset <- c(1, 1, 1, 1)binvec <- c(0.5, 0.5, 0.5, 0.5)anthframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)# POPN C 2003-2004XC3 <- matrix(c(0, 0, 1.74, 1.74,0.208333333, 0, 0, 0.057142857,0.041666667, 0.076923077, 0, 0,0.083333333, 0.076923077, 0.066666667, 0.028571429), 4, 4, byrow = TRUE)# 2004-2005XC4 <- matrix(c(0, 0, 0.3, 0.6,0.32183908, 0.142857143, 0, 0,0.16091954, 0.285714286, 0, 0,0.252873563, 0.285714286, 0.5, 0.6), 4, 4, byrow = TRUE)mats_list <- list(XC3, XC4)yr_ord <- c(1, 2)pch_ord <- c(1, 1)anth_lefkoMat <- create_lM(mats_list, anthframe, hstages = NA, historical = FALSE,  poporder = 1, patchorder = pch_ord, yearorder = yr_ord)  nullmodel1 <- hist_null(anth_lefkoMat, 1) # Ehrlen formatnullmodel2 <- hist_null(anth_lefkoMat, 2) # deVries format

Create Historical Vertical Data Frame from Ahistorical Vertical Data Frame

Description

Functionhistoricalize3() returns a vertically formatted demographicdata frame organized to create historical projection matrices, given avertically but ahistorically formatted data frame. This data frame is instandardhfvdata format and can be used in all functions in thepackage.

Usage

historicalize3(  data,  popidcol = 0,  patchidcol = 0,  individcol,  year2col = 0,  year3col = 0,  xcol = 0,  ycol = 0,  sizea2col = 0,  sizea3col = 0,  sizeb2col = 0,  sizeb3col = 0,  sizec2col = 0,  sizec3col = 0,  repstra2col = 0,  repstra3col = 0,  repstrb2col = 0,  repstrb3col = 0,  feca2col = 0,  feca3col = 0,  fecb2col = 0,  fecb3col = 0,  indcova2col = 0,  indcova3col = 0,  indcovb2col = 0,  indcovb3col = 0,  indcovc2col = 0,  indcovc3col = 0,  alive2col = 0,  alive3col = 0,  dead2col = 0,  dead3col = 0,  obs2col = 0,  obs3col = 0,  nonobs2col = 0,  nonobs3col = 0,  repstrrel = 1,  fecrel = 1,  stage2col = 0,  stage3col = 0,  juv2col = 0,  juv3col = 0,  stageassign = NA,  stagesize = NA,  censor = FALSE,  censorcol = 0,  censorkeep = 0,  spacing = NA,  NAas0 = FALSE,  NRasRep = FALSE,  NOasObs = FALSE,  prebreeding = TRUE,  age_offset = 0,  reduce = TRUE,  a2check = FALSE,  quiet = FALSE)

Arguments

Value

If all inputs are properly formatted, then this function will outputa historical vertical data frame (classhfvdata), meaning that theoutput data frame will have three consecutive years of size and reproductivedata per individual per row. This data frame is in standard format for allfunctions used inlefko3, and so can be used without further modification. Note that determination of state in occasionst-1 andt+1 gives preference to condition in occasiont within theinput dataset. Conflicts in condition in input datasets that have bothoccasionst andt+1 listed per row are resolved by usingcondition in occasiont.

Variables in this data frame include the following:

Notes

Warnings that some individuals occur in state combinations that do not matchany stages in the stageframe used to assign stages, and that some individualsmatch characteristics of several stages in the stageframe, are common whenfirst working with a dataset. Typically, these situations can be identified asNoMatch entries instage3, although such entries may crop up instage1 andstage2, as well. In some cases, these warnings willarise with no concurrentNoMatch entries. These are important warningsand suggest that there is likely a problem with the stageframe. The mostcommon such problems are: 1) stages have significant overlap incharacteristics, with the most common being overlapping size bins caused byerroneous definitions of size bin halfwidths; and 2) some individuals existin states not defined within the stageframe.

In some datasets with unobservable stages, observation status(obsstatus) might not be inferred properly if a single size variableis used that does not yield sizes greater than 0 in all cases in whichindividuals were observed. Such situations may arise, for example, in plantswhen leaf number is the dominant size variable used, but individualsoccasionally occur with inflorescences but no leaves. In this instances,it helps to mark related variables assizeb andsizec, becauseobservation status will be interpreted in relation to all 3 size variables.Alternatively, observation status may be input viaobs2col andobs3col to force computation with given values (although this requiresall instances of observation and non-observation to be known and coded aheadof time). Further analysis can then utilize only a single size variable, ofthe user's choosing. Similar issues can arise in reproductive status(repstatus).

Juvenile designation should only be used when juveniles fall outside of thesize classification scheme used in determining stages. If juveniles are to besize classified along the size spectrum that adults also fall on, thenit is best to treat juveniles as mature but not reproductive.

Care should be taken to avoid variables with negative values indicating size,fecundity, or reproductive or observation status. Negative values can beinterpreted in different ways, typically reflecting estimation through otheralgorithms rather than actual measured data. Variables holding negativevalues can conflict with data management algorithms in ways that aredifficult to predict.

Unusual errors (e.g."Error in pjf...") may occur in cases where thevariables are improperly passed, or where seemingly numeric variables includetext and so get automatically converted to string variables.

Density estimation is performed as a count of individuals alive and withinthe radius specified inspacing of the respective individual at somepoint in time.

Examples

data(cypvert)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,   repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v2 <- historicalize3(data = cypvert, patchidcol = "patch",   individcol = "plantid", year2col = "year2", sizea2col = "Inf2.2",   sizea3col = "Inf2.3", sizeb2col = "Inf.2", sizeb3col = "Inf.3",   sizec2col = "Veg.2", sizec3col = "Veg.3", repstra2col = "Inf2.2",   repstra3col = "Inf2.3", repstrb2col = "Inf.2", repstrb3col = "Inf.3",   feca2col = "Pod.2", feca3col = "Pod.3", repstrrel = 2,   stageassign = cypframe_raw, stagesize = "sizeadded", censorcol = "censor",  censor = FALSE, NAas0 = TRUE, NRasRep = TRUE, reduce = TRUE)

Create Matrix Image

Description

Functionimage3() is a generic function that creates matrix plots.

Usage

image3(mats, ...)

Arguments

Value

Produces a single matrix image, or a series of images, depending onthe input. Non-zero elements appear as red space, while zero elements appearas white space.

See Also

image3.lefkoMat()

image3.matrix()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")image3(ehrlen3, used = 1, type = "U")

Create a Matrix Image for a Single Sparse Matrix

Description

Functionimage3.dgCMatrix plots a matrix image for a single sparsematrix.

Usage

## S3 method for class 'dgCMatrix'image3(mats, ...)

Arguments

Value

Plots a matrix image, or series of matrix images, denoting non-zeroelements as red space and zero elements as white space.

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ", sparse_output = TRUE)image3(ehrlen3$U[[1]])

Create Matrix Image(s) for lefkoElas Object

Description

Functionimage3.lefkoElas plots matrix images for elasticity matricessupplied withinlefkoElas objects.

Usage

## S3 method for class 'lefkoElas'image3(mats, used = "all", type = "a", ...)

Arguments

Value

Plots a matrix image, or series of matrix images, denoting non-zeroelements as red space and zero elements as white space.

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe,  year = c(1989, 1990), stages = c("stage3", "stage2", "stage1"),  supplement = lathsupp3, yearcol = "year2", indivcol = "individ")ehrlen_elas <- elasticity3(ehrlen3)image3(ehrlen_elas, used = 1, type = "h")

Create Matrix Image(s) for lefkoMat Object

Description

Functionimage3.lefkoMat plots matrix images for matrices suppliedwithinlefkoMat objects.

Usage

## S3 method for class 'lefkoMat'image3(mats, used = "all", type = "A", ...)

Arguments

Value

Plots a matrix image, or series of matrix images, denoting non-zeroelements as red space and zero elements as white space.

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")image3(ehrlen3, used = 1, type = "U")

Create Matrix Image(s) for lefkoSens Object

Description

Functionimage3.lefkoSens plots matrix images for sensitivity matricessupplied withinlefkoSens objects.

Usage

## S3 method for class 'lefkoSens'image3(mats, used = "all", type = "a", ...)

Arguments

Value

Plots a matrix image, or series of matrix images, denoting non-zeroelements as red space and zero elements as white space.

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe,  year = c(1989, 1990), stages = c("stage3", "stage2", "stage1"),  supplement = lathsupp3, yearcol = "year2", indivcol = "individ")ehrlen_sens <- sensitivity3(ehrlen3)image3(ehrlen_sens, used = 1, type = "h")

Create Matrix Images for Matrices in a List

Description

Functionimage3.list plots matrix images for matrices contained in alist of matrices.

Usage

## S3 method for class 'list'image3(mats, used = "all", ...)

Arguments

Value

Plots a matrix image, or series of matrix images, denoting non-zeroelements as red space and zero elements as white space.

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl"),   stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep"),  stage1 = c("Sd", "rep", "Sd", "rep", "all", "all"),   givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054),  type = c(1, 1, 1, 1, 3, 3), type_t12 = c(1, 2, 1, 2, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")image3(ehrlen3$A, used = 1)

Create a Matrix Image for a Single Matrix

Description

Functionimage3.matrix plots a matrix image for a single matrix.

Usage

## S3 method for class 'matrix'image3(mats, ...)

Arguments

Value

Plots a matrix image, or series of matrix images, denoting non-zeroelements as red space and zero elements as white space.

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ", sparse_output = FALSE)image3(ehrlen3$U[[1]])

Estimate Actual or Deterministic Population Growth Rate

Description

Functionlambda3() is a generic function that returns the dominanteigenvalue of a matrix, set of dominant eigenvalues of a set of matrices,set of dominant eigenvalues for alefkoMat object, or actual\lambda in each year in alefkoProj object. It can handlelarge and sparse matrices supplied aslefkoMat objects or asindividual matrices, and can be used with large historical matrices, IPMs, age x stage matrices, as well as smaller ahistorical matrices, and generalprojetions.

Usage

lambda3(mpm, force_sparse = NULL)

Arguments

Value

The value returned depends on the class of thempm argument.If alefkoMat object is provided, then this function will return thelabels data frame with a new column namedlambda showing thedominant eigenvalues for each matrix. If alefkoMatList object isprovided, then a two element list in which the first element is a vectorcomposed of the mean lambda values of eachlefkoMat element withinthe list is provided, and the second element is a list oflefkoMatlambda summaries as previously described. If a list of matrices is provided,then this function will produce a numeric vector with the dominanteigenvalues provided in order of matrix. If a single matrix is provided,then this function will return the dominant eigenvalue of that matrix. Onlythe largest real parts of the eigenvalues are returned.

If alefkoProj object is provided, then the output consists of a listwith three elements. The second and third elements are lists of matriceswith each lower-level list elements corresponding tolabels rows,and matrices within these lists showing the actual\lambda andlog\lambda for each consecutive year or time index (columns)within each replicate (row).

See Also

slambda3()

Examples

# Lathyrus exampledata(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",  fecacol = "Intactseed88", deadacol = "Dead1988",  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")ehrlen3mean <- lmean(ehrlen3)lambda3(ehrlen3mean)# Cypripedium exampledata(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)# Here we use supplemental() to provide overwrite and reproductive infocypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")lambda3(cypmatrix2r)

Demographic Dataset ofLathyrus vernus Population

Description

A dataset containing the states and fates ofLathyrus vernus (springvetch), family Fabaceae, from a population in Sweden monitored annuallyfrom 1988 to 1991 in six study plots.

Usage

data(lathyrus)

Format

A data frame with 1119 individuals and 34 variables. Each rowcorresponds to a unique individual, and each variable fromVolume88on refers to the state of the individual in a given year.

SUBPLOT

A variable refering to patch within the population.

GENET

A numeric variable giving a unique number to each individual.

Volume88

Aboveground volume in cubic mm in 1988.

lnVol88

Natural logarithm ofVolume88.

FCODE88

Equals 1 if flowering and 0 if not flowering in 1988.

Flow88

Number of flowers in 1988.

Intactseed88

Number of intact mature seeds produced in 1988.Not always an integer, as in some cases seed number was estimated via linear modeling.

Dead1988

Marked as 1 if known to be dead in 1988.

Dormant1988

Marked as 1 if known to be alive but vegetatively dormant in 1988.

Missing1988

Marked as 1 if not found in 1988.

Seedling1988

Marked as 1, 2, or 3 if observed as a seedling in yeart. Numbers refer to certainty of assignment: 1 = certain that plantis a seedling in 1988, 2 = likely that plant is a seedling in 1988,3 = probable that plant is a seedling in 1988.

Volume89

Aboveground volume in cubic mm in 1989.

lnVol89

Natural logarithm ofVolume89.

FCODE89

Equals 1 if flowering and 0 if not flowering in 1989.

Flow89

Number of flowers in 1989.

Intactseed89

NZumber of intact mature seeds produced in 1989.Not always an integer, as in some cases seed number was estimated vialinear modeling.

Dead1989

Marked as 1 if known to be dead in 1989.

Dormant1989

Marked as 1 if known to be alive but vegetatively dormant in 1989.

Missing1989

Marked as 1 if not found in 1989.

Seedling1989

Marked as 1, 2, or 3 if observed as a seedling inyeart. Numbers refer to certainty of assignment: 1 = certain that plant is a seedling in 1989, 2 = likely that plant is a seedling in 1989, 3 = probable that plant is a seedling in 1989.

Volume90

Aboveground volume in mm³ in 1990.

lnVol90

Natural logarithm ofVolume90.

FCODE90

Equals 1 if flowering and 0 if not flowering in 1990.

Flow90

Number of flowers in 1990.

Intactseed90

NZumber of intact mature seeds produced in 1990.Not always an integer, as in some cases seed number was estimated via linear modeling.

Dead1990

Marked as 1 if known to be dead in 1990.

Dormant1990

Marked as 1 if known to be alive but vegetatively dormant in 1990.

Missing1990

Marked as 1 if not found in 1990.

Seedling1990

Marked as 1, 2, or 3 if observed as a seedling inyeart. Numbers refer to certainty of assignment: 1 = certain that plant is a seedling in 1990, 2 = likely that plant is a seedlingin 1990, 3 = probable that plant is a seedling in 1990.

Volume91

Aboveground volume in mm³ in 1991.

lnVol91

Natural logarithm ofVolume91.

FCODE91

Equals 1 if flowering and 0 if not flowering in 1991.

Flow91

Number of flowers in 1991.

Intactseed91

NZumber of intact mature seeds produced in 1991.Not always an integer, as in some cases seed number was estimated vialinear modeling.

Dead1991

Marked as 1 if known to be dead in 1991.

Dormant1991

Marked as 1 if known to be alive but vegetatively dormant in 1991.

Missing1991

Marked as 1 if not found in 1991.

Seedling1991

Marked as 1, 2, or 3 if observed as a seedling in yeart. Numbers refer to certainty of assignment: 1 = certain that plant is a seedling in 1991, 2 = likely that plant is a seedling in 1991, 3 = probable that plant is a seedling in 1991.

Source

Ehrlen, J. 2000. The dynamics of plant populations: does the history of individuals matter?Ecology 81(6):1675-1684.

Examples

data(lathyrus)sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")repvector <- c(0, 0, 0, 0, 0, 1, 0)obsvector <- c(0, 1, 1, 1, 1, 1, 0)matvector <- c(0, 0, 1, 1, 1, 1, 1)immvector <- c(1, 1, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0)indataset <- c(0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,  propstatus = propvector)lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,  patchidcol = "SUBPLOT", individcol = "GENET",  juvcol = c("Seedling1988", "Seedling1989", "Seedling1990", "Seedling1991"),  sizeacol = c("Volume88", "Volume89", "Volume90", "Volume91"),  repstracol = c("FCODE88", "FCODE89", "FCODE90", "FCODE91"),  fecacol = c("Intactseed88", "Intactseed89", "Intactseed90", "Intactseed91"),  deadacol = c("Dead1988", "Dead1989", "Dead1990", "Dead1991"),  nonobsacol = c("Dormant1988", "Dormant1989", "Dormant1990", "Dormant1991"),  censorcol = c("Missing1988", "Missing1989", "Missing1990", "Missing1991"),   stageassign = lathframe, stagesize = "sizea",  censorkeep = NA, censor = TRUE)lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),  stageframe = lathframe, historical = TRUE)ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all",   stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,  yearcol = "year2", indivcol = "individ")ehrlen3mean <- lmean(ehrlen3)ehrlen3mean$A[[1]]lambda3(ehrlen3mean)

Estimate Mean Projection Matrices

Description

Functionlmean() estimates mean projection matrices as element-wisearithmetic means. It produceslefkoMat objects if provided with them,or single matrices in a simple one-element list if provided a list ofmatrices. Will produce alefkoMatList object, in which each elementis alefkoMat object, if provided with alefkoMatList object.

Usage

lmean(mats, matsout = NULL, force_sparse = FALSE)

Arguments

Value

Yields alefkoMat object, alefkoMatList object, ora list of matrices. If alefkoMat object. then will have thefollowing characteristics:

Examples

data(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")cyp2mean <- lmean(cypmatrix2r)

Two-Parameter logistic Function

Description

Functionlogistic3() creates a vector of values produced by thelogistic function as applied with a user-specified time lag. The logisticfunction is given as\phi_{t+1} = \phi_t \lambda (1 - n_t / K). Here,if noseparate_N vector is provided, thenn_t = \phi_t. If\lambda is not provided, then it defaults to1.0.

Usage

logistic3(  start_value,  alpha,  beta = 0,  lambda = 1,  time_steps = 100L,  time_lag = 1L,  pre0_subs = FALSE,  pre0_value = 0,  substoch = 0L,  separate_N = NULL)

Arguments

Value

A numeric vector of values showing values projected under the-logistic function.

Examples

trial_run1 <- logistic3(1, alpha = 5)plot(trial_run1)trial_run2 <- logistic3(1, alpha = 5, beta = 5)plot(trial_run2)trial_run3 <- logistic3(1, alpha = 100)plot(trial_run3)trial_run4 <- logistic3(1, alpha = 100, beta = 50)plot(trial_run4)trial_run5 <- logistic3(1, alpha = 500)plot(trial_run5)trial_run6 <- logistic3(1, alpha = 500, beta = 501)plot(trial_run6)used_Ns <- c(10, 15, 12, 14, 14, 150, 15, 1, 5, 7, 9, 14, 13, 16, 17, 19,  25, 26)trial_run7 <- logistic3(1, alpha = 500, beta = 501, separate_N = used_Ns)plot(trial_run7)

Conduct a Life Table Response Experiment

Description

ltre3() returns a set of matrices of one-way LTRE (life table responseexperiment), stochastic LTRE (sLTRE) matrices, or small noise approximationLTRE (sna-LTRE) contributions.

Usage

ltre3(  mats,  refmats = NA,  ref = NA,  stochastic = FALSE,  times = 10000,  burnin = 3000,  tweights = NA,  sparse = "auto",  seed = NA,  append_mats = FALSE,  sna_ltre = FALSE,  tol = 1e-30,  ...)

Arguments

Value

This function returns an object of classlefkoLTRE. Thisincludes a list of LTRE matrices as objectcont_mean if adeterministic LTRE is called for, or a list of mean-value LTRE matrices asobjectcont_mean and a list of SD-value LTRE matrices as objectcont_sd if a stochastic LTRE is called for. If a small-noiseapproximation LTRE (SNA-LTRE) is performed, then the output includes sixobjects:cont_mean, which provides the contributions of shifts in meanmatrix elements;cont_elas, which provides the contributions of shiftsin the elasticities of matrix elements;cont_cv, which provides thecontributions of temporal variation in matrix elements;cont_corr,which provides the contributions of temporal correlations in matrix elements;r_values_m, which provides a vector of log deterministic lambda valuesfor treatment populations; andr_values_ref, which provides the logdeterministic lambda of the mean reference matrix.This is followed by thestageframe as objectahstages, the order of historical stages asobjecthstages, the age-by-stage order as objectagestages, theorder of matrices as objectlabels, and, if requested, the original A,U, and F matrices.

Notes

Functionltre3() cannot handlelefkoMatList inputs, althoughindividuallefkoMat elements within these lists can be used.

Deterministic LTRE is one-way, fixed, and based on the sensitivities of thematrix midway between each input matrix and the reference matrix, per Caswell(2001, Matrix Population Models, Sinauer Associates, MA, USA). StochasticLTRE is performed via two methods. The stochastic LTRE approximation issimulated per Davison et al. (2010) Journal of Ecology 98:255-267(doi: 10.1111/j.1365-2745.2009.01611.x). The small noise approximation(sna-LTRE) is analyzed per Davison et al. (2019) Ecological Modelling 408:108760 (doi: 10.1016/j.ecolmodel.2019.108760).

All stochastic and small noise approximation LTREs conducted withoutreference matrices are performed as spatial tests of the population dynamicsamong patches.

Default behavior for stochastic LTRE uses the full population provided inmats as the reference if norefmats andref is provided.If norefmats is provided butref is, then the matrices notedinref are used as the reference matrix set. Year and patch order isutilized from objectmats, but not from objectrefmats, inwhich each matrix is assumed to represent a different year from onepopulation. This function cannot currently handle multiple populations withinthe samemats object (although such analysis is possible if thesepopulations are designated as patches instead).

Ifsparse = "auto", the default, then sparse matrix encodingwill be used if the size of the input matrices is at least 50 columns by 50rows for deterministic and stochastic LTREs and 10 columns by 10 rows forsmall noise approximation LTREs, in all cases as long as 50% of the elementsin the first matrix are non-zero.

Stochastic LTREs do not test for the impact of temporal change in vitalrates. An MPM with a single population, a single patch, and only annualmatrices will produce contributions of 0 to stochastic\lambda.

Speed can sometimes be increased by shifting from automatic sparse matrixdetermination to forced dense or sparse matrix projection. This will mostlikely occur when matrices have between 10 and 300 rows and columns.Defaults work best when matrices are very small and dense, or very large andsparse.

SNA-LTRE analysis cannot test the impact of first-order Markovianenvironments. However, different random weightings of annual matrices areallowed if given in vector format.

Thetime_weights,steps,force_sparse, andrseedarguments are now deprecated. Instead, please use thetweights,times,sparse, andseed arguments.

See Also

summary.lefkoLTRE()

Examples

data(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")ltre3(cypmatrix2r, sna_ltre = TRUE)

Creates Vector of Times Based on First-Order Markov Transition Matrix

Description

Creates a vector of randomly sampled years / times to be used in projection.Random sampling requires a 1st order Markovian transition matrix, showingthe probability of transitioning to each time from each time. Note that thisfunction is not required if the probability of transitioning to a particulartime does not vary with time.

Usage

markov_run(main_times, mat, times = 10000L, start = NULL)

Arguments

Value

An integer vector giving the order of times / years to use inprojection. This can be used as input in theyear option infunctionsprojection3() andf_projection3().

See Also

projection3()

f_projection3()

Examples

# Cypripedium exampledata(cypdata) sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,   propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,   sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",   repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,   NRasRep = TRUE)cypsupp3r <- supplemental(stage3 = c("SD", "SD", "P1", "P1", "P2", "P3", "SL",    "D", "XSm", "Sm", "D", "XSm", "Sm", "mat", "mat", "mat", "SD", "P1"),  stage2 = c("SD", "SD", "SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "SL",    "SL", "SL", "D", "XSm", "Sm", "rep", "rep"),  stage1 = c("SD", "rep", "SD", "rep", "SD", "P1", "P2", "P3", "P3", "P3",    "SL", "SL", "SL", "SL", "SL", "SL", "mat", "mat"),  eststage3 = c(NA, NA, NA, NA, NA, NA, NA, "D", "XSm", "Sm", "D", "XSm", "Sm",    "mat", "mat", "mat", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", "XSm", "XSm",    "XSm", "D", "XSm", "Sm", NA, NA),  eststage1 = c(NA, NA, NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", "XSm", "XSm",    "XSm", "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.25, NA, NA, NA, NA, NA, NA,    NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,    NA, 0.5, 0.5),  type = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  type_t12 = c(1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),  stageframe = cypframe_raw, historical = TRUE)cypmatrix3r <- rlefko3(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added", "size1added"),   supplement = cypsupp3r, yearcol = "year2", indivcol = "individ")used_years <-c(2005, 2006, 2007, 2008)yr_tx_vec <- c(0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.2,  0.2, 0.2, 0.2, 0.4)yr_tx_mat <- matrix(yr_tx_vec, 4, 4)set.seed(1)cyp_markov_vec_1 <- markov_run(main_times = used_years, mat = yr_tx_mat,  times = 100)set.seed(2)cyp_markov_vec_2 <- markov_run(main_times = used_years, mat = yr_tx_mat,  times = 100)set.seed(3)cyp_markov_vec_3 <- markov_run(main_times = used_years, mat = yr_tx_mat,  times = 100)cypstoch_1 <- projection3(cypmatrix3r, nreps = 1, times = 100,  year = cyp_markov_vec_1)cypstoch_2 <- projection3(cypmatrix3r, nreps = 1, times = 100,  year = cyp_markov_vec_2)cypstoch_3 <- projection3(cypmatrix3r, nreps = 1, times = 100,  year = cyp_markov_vec_3)

Arranges Matrix Elements in Order of Magnitude for Interpretation

Description

Functionmatrix_interp summarizes matrices fromlefkoMat,lefkoSens,lefkoElas, andlefkoLTRE objects in termsof the magnitudes of their elements. It can also create ordered summaries ofstandard matrices and sparse matrices.

Usage

matrix_interp(object, mat_chosen = 1L, part = 1L, type = 3L)

Arguments

Value

A data frame arranging all elements in the matrix chosen fromgreatest and smallest. This can be a data frame of only positive elements,of only negative elements, or all elements in order of absolute magnitude.

Notes

Argumentmat_chosen refers to the number of the matrix within thelist that it is held in. For example, if the function is applied to thecont_sd portion of a stochastic LTRE, and there are four LTREmatrices within that list element corresponding to three patch LTRE matricesand one overall population-level LTRE matrix, then setting this value to4 would focus the function on the overall population-level LTREmatrix associated with contributions of the standard deviations of elements.This argument should be left blank if a standard matrix or sparse matrix isinput.

Huge sparse matrices may take more time to process than small, densematrices.

Examples

data(cypdata)sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",  "XLg")repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,  propstatus = propvector, immstatus = immvector, indataset = indataset,  binhalfwidth = binvec)cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,  patchidcol = "patch", individcol = "plantid", blocksize = 4,  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,  NRasRep = TRUE)cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",     "XSm", "Sm", "SD", "P1"),  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",    "rep"),  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),  stageframe = cypframe_raw, historical = FALSE)cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,   year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),  size = c("size3added", "size2added"), supplement = cypsupp2r,  yearcol = "year2", patchcol = "patchid", indivcol = "individ")aaa <- ltre3(cypmatrix2r, stochastic = TRUE)matrix_interp(aaa, mat_chosen = 1, part = 2, type = 3)

Minimize lefkoMod Object by Conversion to vrm_input Object

Description

This function takes alefkoMod object, which consists of vital ratemodels, their associateddredge model tables, and related metadata,and converts them to minimal data frame lists useable in MPM creation andprojection. The main advantage to using this approach is in memory savings.

Usage

miniMod(  lMod,  hfv_data = NULL,  stageframe = NULL,  all_years = NULL,  all_patches = NULL,  all_groups = NULL,  all_indcova = NULL,  all_indcovb = NULL,  all_indcovc = NULL)

Arguments