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Title:

Piece-Wise Exponential Additive Mixed Modeling Tools forSurvival Analysis

Version:

0.7.3

Date:

2025-03-23

Description:

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi:10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.

Depends:

R (≥ 4.1.0)

Imports:

mgcv, survival (≥ 2.39-5), checkmate, magrittr, rlang, tidyr(≥ 1.0.0), ggplot2 (≥ 3.2.2), dplyr (≥ 1.0.0), purrr (≥0.2.3), tibble, lazyeval, Formula, mvtnorm, pec, vctrs (≥0.3.0), scam

Suggests:

testthat, mstate

Config/Needs/website:

coxme, eha, etm, scam, msm, mvna, TBFmultinomial

License:

MIT + file LICENSE

LazyData:

true

URL:

https://adibender.github.io/pammtools/

BugReports:

https://github.com/adibender/pammtools/issues

RoxygenNote:

7.3.2

Encoding:

UTF-8

NeedsCompilation:

Packaged:

2025-03-24 14:04:35 UTC; ab

Author:

Andreas Bender

[aut, cre], Fabian Scheipl

[aut], Johannes Piller

[aut], Philipp Kopper

[aut], Lukas Burk

[ctb]

Maintainer:

Andreas Bender <andreas.bender@stat.uni-muenchen.de>

Repository:

CRAN

Date/Publication:

2025-03-24 15:20:02 UTC

pammtools: Piece-wise exponential Additive Mixed Modeling tools.

Description

pammtools provides functions and utilities that facilitate fittingPiece-wise Exponential Additive Mixed Models (PAMMs), including datatransformation and other convenience functions for pre- and post-processingas well as plotting.

Details

The best way to get an overview of the functionality provided and how tofit PAMMs is to view the vignettesavailable athttps://adibender.github.io/pammtools/articles/.A summary of the vignettes' content is given below:

basics:Introduction to PAMMs and basic modeling.
baseline:Shows how to estimate and visualize baseline model (without covariates) andcomparison to respective Cox-PH model.
convenience:Convenience functions for post-processing and plotting PAMMs.
data-transformation:Transforming data into a format suitable to fit PAMMs.
frailty:Specifying "frailty" terms, i.e., random effects for PAMMs.
splines:Specifying spline smooth terms for PAMMs.
strata:Specifying stratified models in which each level of a grouping variable has adifferent baseline hazard.
tdcovar:Dealing with time-dependent covariates.
tveffects:Specifying time-varying effects.
left-truncation:Estimation for left-truncated data.
competing-risks:Competing risks analysis.

Author(s)

Maintainer: Andreas Benderandreas.bender@stat.uni-muenchen.de (ORCID)

Authors:

Fabian Scheiplfabian.scheipl@stat.uni-muenchen.de (ORCID)
Johannes Pillerjohannes.piller@lmu.de (ORCID)
Philipp Kopperphilipp.kopper@stat.uni-muenchen.de (ORCID)

Other contributors:

Lukas Burkburk@leibniz-bips.de (ORCID) [contributor]

References

Bender, Andreas, Andreas Groll, and Fabian Scheipl. 2018.“A Generalized Additive Model Approach to Time-to-Event Analysis”Statistical Modelling, February. https://doi.org/10.1177/1471082X17748083.

Bender, Andreas, Fabian Scheipl, Wolfgang Hartl, Andrew G. Day, and Helmut Küchenhoff. 2019.“Penalized Estimation of Complex, Non-Linear Exposure-Lag-Response Associations.”Biostatistics 20 (2): 315–31. https://doi.org/10.1093/biostatistics/kxy003.

Bender, Andreas, and Fabian Scheipl. 2018.“pammtools: Piece-Wise Exponential Additive Mixed Modeling Tools.”ArXiv:1806.01042 Stat, June. https://arxiv.org/abs/1806.01042.Ramjith J, Bender A, Roes KCB, Jonker MA. Recurrent events analysiswith piece-wise exponential additive mixed models. 2022. StatisticalModelling., 2022

Pipe operator

Description

Seemagrittr::%>% for details.

Usage

lhs %>% rhs

Add cumulative incidence function to data

Description

Add cumulative incidence function to data

Usage

add_cif(newdata, object, ...)## Default S3 method:add_cif(  newdata,  object,  ci = TRUE,  overwrite = FALSE,  alpha = 0.05,  nsim = 500L,  cause_var = "cause",  time_var = NULL,  ...)

Arguments

newdata

A data frame or list containing the values of the model covariates at which predictionsare required. If this is not provided then predictions corresponding to theoriginal data are returned. Ifnewdata is provided thenit should contain all the variables needed for prediction: awarning is generated if not. See details for use withlink{linear.functional.terms}.

object

a fittedgam object as produced bygam().

...

Further arguments passed topredict.gam andget_hazard

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

overwrite

Should hazard columns be overwritten if already present inthe data set? Defaults toFALSE. IfTRUE, columns with namesc("hazard", "se", "lower", "upper") will be overwritten.

alpha

The alpha level for confidence/credible intervals.

nsim

Number of simulations (draws from posterior of estimated coefficients)on which estimation of CIFs and their confidence/credible intervals will bebased on.

cause_var

Character. Column name of the 'cause' variable.

time_var

Name of the variable used for the baseline hazard. Ifnot given, defaults to"tend" forgam fits, else"interval". The latter is assumed to be a factor, the formernumeric.

Add counterfactual observations for possible transitions

Description

If data only contains one row per transition that took place, this functionadds additional rows for each transition that was possible at that time(for each subject in the data).

Usage

add_counterfactual_transitions(  data,  from_to_pairs = list(),  from_col = "from",  to_col = "to",  transition_col = "transition")

Arguments

data

Data set that only contains rows for transitions that took place.

from_to_pairs

A list with one element for each possible initial state.The values of each list element indicate possible transitions from that state.Will be calculated from the data if unspecified.

from_col

Name of the column that stores initial state.

to_col

Name of the column that stores end state.

transition_col

Name of the column that contains the transition identifier (factor variable).

Add predicted (cumulative) hazard to data set

Description

Add (cumulative) hazard based on the provided data set and model.Ifci=TRUE confidence intervals (CI) are also added. Their width canbe controlled via these_mult argument. The method by which theCI are calculated can be specified byci_type.This is a wrapper aroundpredict.gam. Whenreference is specified, the(log-)hazard ratio is calculated.

Usage

add_hazard(newdata, object, ...)## Default S3 method:add_hazard(  newdata,  object,  reference = NULL,  type = c("response", "link"),  ci = TRUE,  se_mult = 2,  ci_type = c("default", "delta", "sim"),  overwrite = FALSE,  time_var = NULL,  ...)add_cumu_hazard(  newdata,  object,  ci = TRUE,  se_mult = 2,  overwrite = FALSE,  time_var = NULL,  interval_length = "intlen",  ...)

Arguments

newdata

object

a fittedgam object as produced bygam().

...

Further arguments passed topredict.gam andget_hazard

reference

A data frame with number of rows equal tonrow(newdata) orone, or a named list with (partial) covariate specifications. See examples.

type

Either"response" or"link". The former calculateshazard, the latter the log-hazard.

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

se_mult

Factor by which standard errors are multiplied for calculatingthe confidence intervals.

ci_type

The method by which standard errors/confidence intervalswill be calculated. Default transforms the linear predictor atrespective intervals."delta" calculates CIs based on the standarderror calculated by the Delta method."sim" draws theproperty of interest from its posterior based on the normal distribution ofthe estimated coefficients. Seeherefor details and empirical evaluation.

overwrite

Should hazard columns be overwritten if already present inthe data set? Defaults toFALSE. IfTRUE, columns with namesc("hazard", "se", "lower", "upper") will be overwritten.

time_var

Name of the variable used for the baseline hazard. Ifnot given, defaults to"tend" forgam fits, else"interval". The latter is assumed to be a factor, the formernumeric.

interval_length

The variable in newdata containing the interval lengths.Can be either bare unquoted variable name or character. Defaults to"intlen".

Examples

ped <- tumor[1:50,] %>% as_ped(Surv(days, status)~ age)pam <- mgcv::gam(ped_status ~ s(tend)+age, data = ped, family=poisson(), offset=offset)ped_info(ped) %>% add_hazard(pam, type="link")ped_info(ped) %>% add_hazard(pam, type = "response")ped_info(ped) %>% add_cumu_hazard(pam)

Add survival probability estimates

Description

Given suitable data (i.e. data with all columns used for estimation of the model),this functions adds a columnsurv_prob containing survival probabilitiesfor the specified covariate and follow-up information (and CIssurv_lower,surv_upper ifci=TRUE).

Usage

add_surv_prob(  newdata,  object,  ci = TRUE,  se_mult = 2,  overwrite = FALSE,  time_var = NULL,  interval_length = "intlen",  ...)

Arguments

newdata

object

a fittedgam object as produced bygam().

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

se_mult

Factor by which standard errors are multiplied for calculatingthe confidence intervals.

overwrite

Should hazard columns be overwritten if already present inthe data set? Defaults toFALSE. IfTRUE, columns with namesc("hazard", "se", "lower", "upper") will be overwritten.

time_var

Name of the variable used for the baseline hazard. Ifnot given, defaults to"tend" forgam fits, else"interval". The latter is assumed to be a factor, the formernumeric.

interval_length

The variable in newdata containing the interval lengths.Can be either bare unquoted variable name or character. Defaults to"intlen".

...

Further arguments passed topredict.gam andget_hazard

Examples

ped <- tumor[1:50,] %>% as_ped(Surv(days, status)~ age)pam <- mgcv::gam(ped_status ~ s(tend)+age, data=ped, family=poisson(), offset=offset)ped_info(ped) %>% add_surv_prob(pam, ci=TRUE)

Add time-dependent covariate to a data set

Description

Given a data set in standard format (with one row per subject/observation),this function adds a column with the specified exposure time pointsand a column with respective exposures, created fromrng_fun.This function should usually only be used to create data sets passedtosim_pexp.

Usage

add_tdc(data, tz, rng_fun, ...)

Arguments

data

A data set with variables specified informula.

tz

A numeric vector of exposure times (relative to thebeginning of the follow-up timet)

rng_fun

A random number generating function that createsthe time-dependent covariates at time pointstz.First argument of the function should ben, the number ofrandom numbers to generate. Withinadd_tdc,n will be settolength(tz).

...

Currently not used.

Embeds the data set with the specified (relative) term contribution

Description

Adds the contribution of a specific term to thelinear predictor to the data specified bynewdata.Essentially a wrapper topredict.gam, withtype="terms".Thus most arguments and their documentation below is frompredict.gam.

Usage

add_term(newdata, object, term, reference = NULL, ci = TRUE, se_mult = 2, ...)

Arguments

newdata

object

a fittedgam object as produced bygam().

term

A character (vector) or regular expression indicating forwhich term(s) information should be extracted and added to data set.

reference

A data frame with number of rows equal tonrow(newdata) orone, or a named list with (partial) covariate specifications. See examples.

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

se_mult

The factor by which standard errors are multiplied to formconfidence intervals.

...

Further arguments passed topredict.gam

Examples

library(ggplot2)ped <- as_ped(tumor, Surv(days, status)~ age, cut = seq(0, 2000, by = 100))pam <- mgcv::gam(ped_status ~ s(tend) + s(age), family = poisson(),  offset = offset, data = ped)#term contribution for sequence of agess_age <- ped %>% make_newdata(age = seq_range(age, 50)) %>%  add_term(pam, term = "age")ggplot(s_age, aes(x = age, y = fit)) + geom_line() +  geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = .3)# term contribution relative to mean ages_age2 <- ped %>% make_newdata(age = seq_range(age, 50)) %>%  add_term(pam, term = "age", reference = list(age = mean(.$age)))ggplot(s_age2, aes(x = age, y = fit)) + geom_line() +  geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = .3)

Add transition probabilities confidence intervals

Description

Add transition probabilities confidence intervals

Usage

add_trans_ci(newdata, object, nsim = 100L, alpha = 0.05, ...)

Add transition probabilities

Description

Usage

add_trans_prob(  newdata,  object,  overwrite = FALSE,  ci = FALSE,  alpha = 0.05,  nsim = 100L,  time_var = NULL,  interval_length = "intlen",  ...)

Arguments

newdata

object

a fittedgam object as produced bygam().

overwrite

Should hazard columns be overwritten if already present inthe data set? Defaults toFALSE. IfTRUE, columns with namesc("hazard", "se", "lower", "upper") will be overwritten.

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

alpha

The alpha level for confidence/credible intervals.

nsim

Number of simulations (draws from posterior of estimated coefficients)on which estimation of CIFs and their confidence/credible intervals will bebased on.

time_var

Name of the variable used for the baseline hazard. Ifnot given, defaults to"tend" forgam fits, else"interval". The latter is assumed to be a factor, the formernumeric.

interval_length

The variable in newdata containing the interval lengths.Can be either bare unquoted variable name or character. Defaults to"intlen".

...

Further arguments passed topredict.gam andget_hazard

Examples

ped <- tumor[1:50,] %>% as_ped(Surv(days, status)~ age)pam <- mgcv::gam(ped_status ~ s(tend)+age, data = ped, family=poisson(), offset=offset)ped_info(ped) %>% add_hazard(pam, type="link")ped_info(ped) %>% add_hazard(pam, type = "response")ped_info(ped) %>% add_cumu_hazard(pam)

Transform crps object to data.frame

Description

Aas.data.frame S3 method for objects of classcrps.

Usage

## S3 method for class 'crps'as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x

An object of classcrps. Seecrps.

row.names

NULL or a character vector giving the rownames for the data frame. Missing values are not allowed.

optional

logical. IfTRUE, setting row names andconverting column names (to syntactic names: seemake.names) is optional. Note that all ofR'sbase packageas.data.frame() methods useoptional only for column names treatment, basically with themeaning ofdata.frame(*, check.names = !optional).See also themake.names argument of thematrix method.

...

additional arguments to be passed to or from methods.

Transform data to Piece-wise Exponential Data (PED)

Description

This is the general data transformation function provided by thepammtools package. Two main applications must be distinguished:

Transformation of standard time-to-event data.
Transformation of left-truncated time-to-event data.
Transformation of time-to-event data with time-dependent covariates (TDC).

For the latter, the type of effect one wants to estimate is alsoimportant for the data transformation step.In any case, the data transformation is specified by a two sided formula.In case of TDCs, the right-hand-side of the formula can contain formula specialsconcurrent andcumulative.See thedata-transformationvignette for details.

Usage

as_ped(data, ...)## S3 method for class 'data.frame'as_ped(  data,  formula,  cut = NULL,  max_time = NULL,  tdc_specials = c("concurrent", "cumulative"),  censor_code = 0L,  transition = character(),  timescale = c("gap", "calendar"),  min_events = 1L,  ...)## S3 method for class 'nested_fdf'as_ped(data, formula, ...)## S3 method for class 'list'as_ped(  data,  formula,  tdc_specials = c("concurrent", "cumulative"),  censor_code = 0L,  ...)is.ped(x)## S3 method for class 'ped'as_ped(data, newdata, ...)## S3 method for class 'pamm'as_ped(data, newdata, ...)as_ped_multistate(  data,  formula,  cut = NULL,  max_time = NULL,  tdc_specials = c("concurrent", "cumulative"),  censor_code = 0L,  transition = character(),  timescale = c("gap", "calendar"),  min_events = 1L,  ...)

Arguments

data

Either an object inheriting from data frame or in case oftime-dependent covariates a list of data frames (of length 2), where the first data framecontains the time-to-event information and static covariates while the second(and potentially further data frames) contain information on time-dependentcovariates and the times at which they have been observed.

...

Further arguments passed to thedata.frame method andeventually tosurvSplit

formula

A two sided formula with aSurv objecton the left-hand-side and covariate specification on the right-hand-side (RHS).The RHS can be an extended formula, which specifies how TDCs should be transformedusing specialsconcurrent andcumulative. The left hand-side canbe in start-stop-notation. This, however, is only used to create left-truncateddata and does not support the full functionality.

cut

Split points, used to partition the follow up into intervals.If unspecified, all unique event times will be used.

max_time

Ifcut is unspecified, this will be the lastpossible event time. All event times aftermax_timewill be administratively censored atmax_time.

tdc_specials

A character vector. Names of potential specials informula for concurrent and or cumulative effects.

censor_code

Specifies the value of the status variable that indicates censoring.Often this will be0, which is the default.

x

any R object.

newdata

A new data set (data.frame) that contains the samevariables that were used to create the PED object (data).

Value

A data frame classped in piece-wise exponential data format.

Examples

tumor[1:3, ]tumor[1:3, ] %>% as_ped(Surv(days, status)~ age + sex, cut = c(0, 500, 1000))tumor[1:3, ] %>% as_ped(Surv(days, status)~ age + sex)## Not run: data("cgd", package = "frailtyHL")cgd2 <- cgd %>% select(id, tstart, tstop, enum, status, age) %>% filter(enum %in% c(1:2))ped_re <- as_ped_multistate(  formula = Surv(tstart, tstop, status) ~ age + enum,  data = cgd2, transition = "enum", timescale = "calendar")## End(Not run)

Competing risks trafo

Description

This is the general data transformation function provided by thepammtools package. Two main applications must be distinguished:

Transformation of standard time-to-event data.
Transformation of left-truncated time-to-event data.
Transformation of time-to-event data with time-dependent covariates (TDC).

Usage

as_ped_cr(  data,  formula,  cut = NULL,  max_time = NULL,  tdc_specials = c("concurrent", "cumulative"),  censor_code = 0L,  combine = TRUE,  ...)

Arguments

data

formula

cut

Split points, used to partition the follow up into intervals.If unspecified, all unique event times will be used.

max_time

Ifcut is unspecified, this will be the lastpossible event time. All event times aftermax_timewill be administratively censored atmax_time.

tdc_specials

A character vector. Names of potential specials informula for concurrent and or cumulative effects.

censor_code

Specifies the value of the status variable that indicates censoring.Often this will be0, which is the default.

...

Further arguments passed to thedata.frame method andeventually tosurvSplit

Value

A data frame classped in piece-wise exponential data format.

Examples

tumor[1:3, ]tumor[1:3, ] %>% as_ped(Surv(days, status)~ age + sex, cut = c(0, 500, 1000))tumor[1:3, ] %>% as_ped(Surv(days, status)~ age + sex)## Not run: data("cgd", package = "frailtyHL")cgd2 <- cgd %>% select(id, tstart, tstop, enum, status, age) %>% filter(enum %in% c(1:2))ped_re <- as_ped_multistate(  formula = Surv(tstart, tstop, status) ~ age + enum,  data = cgd2, transition = "enum", timescale = "calendar")## End(Not run)

Calculate confidence intervals

Description

Given 2 column matrix or data frame, returns 3 column data.framewith coefficient estimate plus lower and upper borders of the95% confidence intervals.

Usage

calc_ci(ftab)

Arguments

ftab

A table with two columns, containing coefficients in the firstcolumn and standard-errors in the second column.

Create a data frame from all combinations of data frames

Description

Works likeexpand.grid but for data frames.

Usage

combine_df(...)

Arguments

...

Data frames that should be combined to one data frame.Elements of first df vary fastest, elements of last df vary slowest.

Examples

combine_df(  data.frame(x=1:3, y=3:1),  data.frame(x1=c("a", "b"), x2=c("c", "d")),  data.frame(z=c(0, 1)))

Calculate difference in cumulative hazards and respective standard errors

Description

CIs are calculated by sampling coefficients from their posterior andcalculating the cumulative hazard differencensim times. The CIare obtained by the 2.5\

Usage

compute_cumu_diff(d1, d2, model, alpha = 0.05, nsim = 100L)

Arguments

d1

A data set used asnewdata inpredict.gam

d2

Seed1

model

A model object for which a predict method is implemented whichreturns the design matrix (e.g.,mgcv::gam).

Formula specials for defining time-dependent covariates

Description

So far, two specials are implemented.concurrent is used whenthe goal is to estimate a concurrent effect of the TDC.cumulativeis used when the goal is to estimate a cumulative effect of the TDC. Theseshould usually not be called directly but rather as part of theformulaargument toas_ped.See thevignette on data transformationfor details.

Usage

cumulative(..., tz_var, ll_fun = function(t, tz) t >= tz, suffix = NULL)concurrent(..., tz_var, lag = 0, suffix = NULL)has_special(formula, special = "cumulative")

Arguments

...

Forconcurrent variables that will be transformed tocovariate matrices. The number of columns of each covariate depends ontz.Usually, elements that will be specified here aretime (which should bethe name of the time-variable used on the LHS of the formula argument toas_ped),tz which is the variable containing information onthe times at which the TDC was observed (can be wrapped inlatency) andthe TDCs that share the sametz and Lag-lead window (ll_fun).

tz_var

The name of the variable that stores information on thetimes at which the TDCs specified in this term where observed.

ll_fun

Function that specifies how the lag-lead matrixshould be constructed. First argument is the follow up timesecond argument is the time of exposure.

lag

a single positive number giving the time lag between fora concurrent effect to occur (i.e., the TDC at time of exposuret-lagaffects the hazard in the interval containing follow-up timet).Defaults to 0.

formula

special

The name of the special whose existence in theformula should be checked

Time-dependent covariates of the`patient` data set.

Description

This data set contains the time-dependent covariates (TDCs) for thepatientdata set. Note that nutrition was protocoled for at most 12 days afterICU admission. The data set includes:

CombinedID: Unique patient identifier. Can be used to merge withpatient data
Study_Day: The calendar (!) day at which calories (or proteins) wereadministered
caloriesPercentage: The percentage of target calories supplied to thepatient by the ICU staff
proteinGproKG: The amount of protein supplied to the patient by theICU staff

Usage

daily

Format

An object of classtbl_df (inherits fromtbl,data.frame) with 18797 rows and 4 columns.

`dplyr` Verbs for`ped`-Objects

Description

Seedplyr documentation of the respective functions fordescription and examples.

Usage

## S3 method for class 'ped'arrange(.data, ...)## S3 method for class 'ped'group_by(.data, ..., .add = FALSE)## S3 method for class 'ped'ungroup(x, ...)## S3 method for class 'ped'distinct(.data, ..., .keep_all = FALSE)## S3 method for class 'ped'filter(.data, ...)## S3 method for class 'ped'sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...)## S3 method for class 'ped'sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...)## S3 method for class 'ped'slice(.data, ...)## S3 method for class 'ped'select(.data, ...)## S3 method for class 'ped'mutate(.data, ...)## S3 method for class 'ped'rename(.data, ...)## S3 method for class 'ped'summarise(.data, ...)## S3 method for class 'ped'summarize(.data, ...)## S3 method for class 'ped'transmute(.data, ...)## S3 method for class 'ped'inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)## S3 method for class 'ped'full_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)## S3 method for class 'ped'left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)## S3 method for class 'ped'right_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

Arguments

.data

an object of classped, seeas_ped.

...

seedplyr documentation

x

an object of classped, seeas_ped.

tbl

an object of classped, seeas_ped.

size

<tidy-select>Forsample_n(), the number of rows to select.Forsample_frac(), the fraction of rows to select.Iftbl is grouped,size applies to each group.

replace

Sample with or without replacement?

weight

<tidy-select> Sampling weights.This must evaluate to a vector of non-negative numbers the same length asthe input. Weights are automatically standardised to sum to 1.

.env

DEPRECATED.

by

A join specification created withjoin_by(), or a charactervector of variables to join by.

IfNULL, the default,⁠*_join()⁠ will perform a natural join, using allvariables in common acrossx andy. A message lists the variables sothat you can check they're correct; suppress the message by supplyingbyexplicitly.

To join on different variables betweenx andy, use ajoin_by()specification. For example,join_by(a == b) will matchx$a toy$b.

To join by multiple variables, use ajoin_by() specification withmultiple expressions. For example,join_by(a == b, c == d) will matchx$a toy$b andx$c toy$d. If the column names are the same betweenx andy, you can shorten this by listing only the variable names, likejoin_by(a, c).

join_by() can also be used to perform inequality, rolling, and overlapjoins. See the documentation at?join_by for details onthese types of joins.

For simple equality joins, you can alternatively specify a character vectorof variable names to join by. For example,by = c("a", "b") joinsx$atoy$a andx$b toy$b. If variable names differ betweenx andy,use a named character vector likeby = c("x_a" = "y_a", "x_b" = "y_b").

To perform a cross-join, generating all combinations ofx andy, seecross_join().

copy

Ifx andy are not from the same data source,andcopy isTRUE, theny will be copied into thesame src asx. This allows you to join tables across srcs, butit is a potentially expensive operation so you must opt into it.

suffix

If there are non-joined duplicate variables inx andy, these suffixes will be added to the output to disambiguate them.Should be a character vector of length 2.

Value

a modifiedped object (except fordo)

A formula special used to handle cumulative effect specifications

Description

Can be used in the second part of the formula specification providedtosim_pexp and should only be used in thiscontext.

Usage

fcumu(..., by = NULL, f_xyz, ll_fun)

Extract transition information from different objects

Description

Extract transition information from different objects

Usage

from_to_pairs(t_mat, ...)from_to_pairs2(t_mat, ...)## S3 method for class 'data.frame'from_to_pairs(t_mat, from_col = "from", to_col = "to", ...)

Arguments

t_mat

an object that contains information about possible transitions.

from_col

The name of the column in the data frame that contains "from" states.

to_col

The name of the column in the data frame that contains "to" states.

Examples

## Not run: df = data.frame(id = c(1,1, 2,2), from = c(1, 1, 2, 2), to = c(2, 3, 2, 2))from_to_pairs(df)## End(Not run)

(Cumulative) (Step-) Hazard Plots.

Description

geom_hazard is an extension of thegeom_line, andis optimized for (cumulative) hazard plots. Essentially, it adds a (0,0)row to the data, if not already the case. Stolen from theRmcdrPlugin.KMggplot2 (slightly modified).

Usage

geom_hazard(  mapping = NULL,  data = NULL,  stat = "identity",  position = "identity",  na.rm = FALSE,  show.legend = NA,  inherit.aes = TRUE,  ...)geom_stephazard(  mapping = NULL,  data = NULL,  stat = "identity",  position = "identity",  direction = "vh",  na.rm = FALSE,  show.legend = NA,  inherit.aes = TRUE,  ...)geom_surv(  mapping = NULL,  data = NULL,  stat = "identity",  position = "identity",  na.rm = FALSE,  show.legend = NA,  inherit.aes = TRUE,  ...)

Arguments

mapping

Set of aesthetic mappings created byaes(). If specified andinherit.aes = TRUE (the default), it is combined with the default mappingat the top level of the plot. You must supplymapping if there is no plotmapping.

data

The data to be displayed in this layer. There are threeoptions:

IfNULL, the default, the data is inherited from the plotdata as specified in the call toggplot().

Adata.frame, or other object, will override the plotdata. All objects will be fortified to produce a data frame. Seefortify() for which variables will be created.

Afunction will be called with a single argument,the plot data. The return value must be adata.frame, andwill be used as the layer data. Afunction can be createdfrom aformula (e.g.~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer.When using a⁠geom_*()⁠ function to construct a layer, thestatargument can be used the override the default coupling between geoms andstats. Thestat argument accepts the following:

AStat ggproto subclass, for exampleStatCount.
A string naming the stat. To give the stat as a string, strip thefunction name of thestat_ prefix. For example, to usestat_count(),give the stat as"count".
For more information and other ways to specify the stat, see thelayer stat documentation.

position

A position adjustment to use on the data for this layer. Thiscan be used in various ways, including to prevent overplotting andimproving the display. Theposition argument accepts the following:

The result of calling a position function, such asposition_jitter().This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as astring, strip the function name of theposition_ prefix. For example,to useposition_jitter(), give the position as"jitter".
For more information and other ways to specify the position, see thelayer position documentation.

na.rm

IfFALSE, the default, missing values are removed witha warning. IfTRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends?NA, the default, includes if any aesthetics are mapped.FALSE never includes, andTRUE always includes.It can also be a named logical vector to finely select the aesthetics todisplay.

inherit.aes

IfFALSE, overrides the default aesthetics,rather than combining with them. This is most useful for helper functionsthat define both data and aesthetics and shouldn't inherit behaviour fromthe default plot specification, e.g.borders().

...

Other arguments passed on tolayer()'sparams argument. Thesearguments broadly fall into one of 4 categories below. Notably, furtherarguments to theposition argument, or aesthetics that are requiredcannot be passed through.... Unknown arguments that are not partof the 4 categories below are ignored.

Static aesthetics that are not mapped to a scale, but are at a fixedvalue and apply to the layer as a whole. For example,colour = "red"orlinewidth = 3. The geom's documentation has anAestheticssection that lists the available options. The 'required' aestheticscannot be passed on to theparams. Please note that while passingunmapped aesthetics as vectors is technically possible, the order andrequired length is not guaranteed to be parallel to the input data.
When constructing a layer usinga⁠stat_*()⁠ function, the... argument can be used to pass onparameters to thegeom part of the layer. An example of this isstat_density(geom = "area", outline.type = "both"). The geom'sdocumentation lists which parameters it can accept.
Inversely, when constructing a layer using a⁠geom_*()⁠ function, the... argument can be used to pass on parametersto thestat part of the layer. An example of this isgeom_area(stat = "density", adjust = 0.5). The stat's documentationlists which parameters it can accept.
Thekey_glyph argument oflayer() may also be passed on through.... This can be one of the functions described askey glyphs, to change the display of the layer in the legend.

direction

direction of stairs: 'vh' for vertical then horizontal,'hv' for horizontal then vertical, or 'mid' for step half-way betweenadjacent x-values.

Examples

library(ggplot2)library(pammtools)ped <- tumor[10:50,] %>% as_ped(Surv(days, status)~1)pam <- mgcv::gam(ped_status ~ s(tend), data=ped, family = poisson(), offset = offset)ndf <- make_newdata(ped, tend = unique(tend)) %>% add_hazard(pam)# piece-wise constant hazardsggplot(ndf, aes(x = tend, y = hazard)) + geom_vline(xintercept = c(0, ndf$tend[c(1, (nrow(ndf)-2):nrow(ndf))]), lty = 3) + geom_hline(yintercept = c(ndf$hazard[1:3], ndf$hazard[nrow(ndf)]), lty = 3) + geom_stephazard() + geom_step(col=2) + geom_step(col=2, lty = 2, direction="vh")# comulative hazardndf <- ndf %>% add_cumu_hazard(pam)ggplot(ndf, aes(x = tend, y = cumu_hazard)) + geom_hazard() + geom_line(col=2) # doesn't start at (0, 0)# survival probabilityndf <- ndf %>% add_surv_prob(pam)ggplot(ndf, aes(x = tend, y = surv_prob)) + geom_surv() + geom_line(col=2) # doesn't start at c(0,1)

Step ribbon plots.

Description

geom_stepribbon is an extension of thegeom_ribbon, andis optimized for Kaplan-Meier plots with pointwise confidence intervalsor a confidence band. The defaultdirection-argument"hv" isappropriate for right-continuous step functions like the hazard rates etcreturned bypammtools.

Usage

geom_stepribbon(  mapping = NULL,  data = NULL,  stat = "identity",  position = "identity",  direction = "hv",  na.rm = FALSE,  show.legend = NA,  inherit.aes = TRUE,  ...)

Arguments

mapping

data

The data to be displayed in this layer. There are threeoptions:

IfNULL, the default, the data is inherited from the plotdata as specified in the call toggplot().

Adata.frame, or other object, will override the plotdata. All objects will be fortified to produce a data frame. Seefortify() for which variables will be created.

stat

AStat ggproto subclass, for exampleStatCount.
A string naming the stat. To give the stat as a string, strip thefunction name of thestat_ prefix. For example, to usestat_count(),give the stat as"count".
For more information and other ways to specify the stat, see thelayer stat documentation.

position

A position adjustment to use on the data for this layer. Thiscan be used in various ways, including to prevent overplotting andimproving the display. Theposition argument accepts the following:

The result of calling a position function, such asposition_jitter().This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as astring, strip the function name of theposition_ prefix. For example,to useposition_jitter(), give the position as"jitter".
For more information and other ways to specify the position, see thelayer position documentation.

direction

direction of stairs: 'vh' for vertical then horizontal,'hv' for horizontal then vertical, or 'mid' for step half-way betweenadjacent x-values.

na.rm

IfFALSE, the default, missing values are removed witha warning. IfTRUE, missing values are silently removed.

show.legend

inherit.aes

...

Static aesthetics that are not mapped to a scale, but are at a fixedvalue and apply to the layer as a whole. For example,colour = "red"orlinewidth = 3. The geom's documentation has anAestheticssection that lists the available options. The 'required' aestheticscannot be passed on to theparams. Please note that while passingunmapped aesthetics as vectors is technically possible, the order andrequired length is not guaranteed to be parallel to the input data.
When constructing a layer usinga⁠stat_*()⁠ function, the... argument can be used to pass onparameters to thegeom part of the layer. An example of this isstat_density(geom = "area", outline.type = "both"). The geom'sdocumentation lists which parameters it can accept.
Inversely, when constructing a layer using a⁠geom_*()⁠ function, the... argument can be used to pass on parametersto thestat part of the layer. An example of this isgeom_area(stat = "density", adjust = 0.5). The stat's documentationlists which parameters it can accept.
Thekey_glyph argument oflayer() may also be passed on through.... This can be one of the functions described askey glyphs, to change the display of the layer in the legend.

Examples

library(ggplot2)huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))h <- ggplot(huron, aes(year))h + geom_stepribbon(aes(ymin = level - 1, ymax = level + 1), fill = "grey70") +    geom_step(aes(y = level))h + geom_ribbon(aes(ymin = level - 1, ymax = level + 1), fill = "grey70") +    geom_line(aes(y = level))

Calculate CIF for one cause

Description

Calculate CIF for one cause

Usage

get_cif(newdata, object, ...)## Default S3 method:get_cif(  newdata,  object,  ci,  time_var,  alpha,  nsim,  cause_var,  coefs,  V,  sim_coef_mat,  ...)

Extract cumulative coefficients (cumulative hazard differences)

Description

These functions are designed to extract (or mimic) the cumulative coefficientsusually used in additive hazards models (Aalen model) to depict (time-varying)covariate effects. For PAMMs, these are the differencesbetween the cumulative hazard rates where all covariates except one have theidentical values. For a numeric covariate of interest, this calculates\Lambda(t|x+1) - \Lambda(t|x). For non-numeric covariatesthe cumulative hazard of the reference level is subtracted fromthe cumulative hazards evaluated at all non reference levels. Standarderrors are calculated using the delta method.

Usage

get_cumu_coef(model, data = NULL, terms, ...)## S3 method for class 'gam'get_cumu_coef(model, data, terms, ...)## S3 method for class 'aalen'get_cumu_coef(model, data = NULL, terms, ci = TRUE, ...)## S3 method for class 'cox.aalen'get_cumu_coef(model, data = NULL, terms, ci = TRUE, ...)

Arguments

model

Object from which to extract cumulative coefficients.

data

Additional data if necessary.

terms

A character vector of variables for which the cumulativecoefficient should be calculated.

...

Further arguments passed to methods.

ci

Logical. Indicates if confidence intervals should be returned aswell.

Calculate (or plot) cumulative effect for all time-points of the follow-up

Description

Calculate (or plot) cumulative effect for all time-points of the follow-up

Usage

get_cumu_eff(data, model, term, z1, z2 = NULL, se_mult = 2)gg_cumu_eff(data, model, term, z1, z2 = NULL, se_mult = 2, ci = TRUE)

Arguments

data

Data used to fit themodel.

model

A suitable model object which will be used to estimate thepartial effect ofterm.

term

A character string indicating the model term for which partialeffects should be plotted.

z1

The exposure profile for which to calculate the cumulative effect.Can be either a single number or a vector of same length as unique observationtime points.

z2

If provided, calculated cumulative effect is for the differencebetween the two exposure profiles (g(z1,t)-g(z2,t)).

se_mult

Multiplicative factor used to calculate confidence intervals(e.g., lower = fit - 2*se).

ci

Logical. Indicates if confidence intervals for thetermof interest should be calculated/plotted. Defaults toTRUE.

Calculate cumulative hazard

Description

Calculate cumulative hazard

Usage

get_cumu_hazard(  newdata,  object,  ci = TRUE,  ci_type = c("default", "delta", "sim"),  time_var = NULL,  se_mult = 2,  interval_length = "intlen",  nsim = 100L,  ...)

Arguments

newdata

object

a fittedgam object as produced bygam().

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

ci_type

time_var

Name of the variable used for the baseline hazard. Ifnot given, defaults to"tend" forgam fits, else"interval". The latter is assumed to be a factor, the formernumeric.

se_mult

Factor by which standard errors are multiplied for calculatingthe confidence intervals.

interval_length

The variable in newdata containing the interval lengths.Can be either bare unquoted variable name or character. Defaults to"intlen".

...

Further arguments passed topredict.gam andget_hazard

Expand time-dependent covariates to functionals

Description

Given formula specification on how time-dependent covariates affect theoutcome, creates respective functional covariate as well as auxiliarymatrices for time/latency etc.

Usage

get_cumulative(data, formula)expand_cumulative(data, func, n_func)

Arguments

data

Data frame (or similar) in which variables specified in ...will be looked for

formula

A formula containingcumulative specials,that specify the type of cumulative effect one wants to estimate. For detailssee the vignettes on data transformation and time-dependent covariates.

func

Single evaluatedcumulative term.

Obtain interval break points

Description

Default method words for data frames.The list method applies the default method to each data set within the list.

Usage

get_cut(data, formula, cut = NULL, ...)## Default S3 method:get_cut(data, formula, cut = NULL, max_time = NULL, event = 1L, ...)## S3 method for class 'list'get_cut(  data,  formula,  cut = NULL,  max_time = NULL,  event = 1L,  timescale = "gap",  ...)

Exctract event types

Description

Given a formula that specifies the status variable of the outcome, this functionextracts the different event types (except for censoring, specified bycensor_code).

Usage

get_event_types(data, formula, censor_code)

Arguments

data

formula

censor_code

Specifies the value of the status variable that indicates censoring.Often this will be0, which is the default.

Calculate predicted hazard

Description

Calculate predicted hazard

Usage

get_hazard(object, newdata, ...)## Default S3 method:get_hazard(  object,  newdata,  reference = NULL,  ci = TRUE,  type = c("response", "link"),  ci_type = c("default", "delta", "sim"),  time_var = NULL,  se_mult = 2,  ...)

Arguments

object

a fittedgam object as produced bygam().

newdata

...

Further arguments passed topredict.gam andget_hazard

reference

A data frame with number of rows equal tonrow(newdata) orone, or a named list with (partial) covariate specifications. See examples.

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

type

Either"response" or"link". The former calculateshazard, the latter the log-hazard.

ci_type

time_var

Name of the variable used for the baseline hazard. Ifnot given, defaults to"tend" forgam fits, else"interval". The latter is assumed to be a factor, the formernumeric.

se_mult

Factor by which standard errors are multiplied for calculatingthe confidence intervals.

Information on intervals in which times fall

Description

Information on intervals in which times fall

Usage

get_intervals(x, times, ...)## Default S3 method:get_intervals(x, times, left.open = TRUE, rightmost.closed = TRUE, ...)

Arguments

x

An object from which interval information can be obtained,seeint_info.

times

A vector of times for which corresponding interval informationshould be returned.

...

Further arguments passed tofindInterval.

left.open

logical; if true all the intervals are open at leftand closed at right; in the formulas below,\le should beswapped with< (and> with\ge), andrightmost.closed means ‘leftmost is closed’. This maybe useful, e.g., in survival analysis computations.

rightmost.closed

logical; if true, the rightmost interval,vec[N-1] .. vec[N] is treated asclosed, see below.

Value

Adata.frame containing information on intervals in whichvalues oftimes fall.

Examples

set.seed(111018)brks <- c(0, 4.5, 5, 10, 30)int_info(brks)x <- runif (3, 0, 30)xget_intervals(brks, x)

Construct or extract data that represents a lag-lead window

Description

Constructs lag-lead window data set from raw inputs or from data objectswith suitable information stored in attributes, e.g., objects createdbyas_ped.

Usage

get_laglead(x, ...)## Default S3 method:get_laglead(x, tz, ll_fun, ...)## S3 method for class 'data.frame'get_laglead(x, ...)

Arguments

x

Either a numeric vector of follow-up cut points or a suitable object.

...

Further arguments passed to methods.

tz

A vector of exposure times

ll_fun

Function that specifies how the lag-lead matrixshould be constructed. First argument is the follow up timesecond argument is the time of exposure.

Examples

get_laglead(0:10, tz=-5:5, ll_fun=function(t, tz) { t >= tz + 2 & t <= tz + 2 + 3})gg_laglead(0:10, tz=-5:5, ll_fun=function(t, tz) { t >= tz + 2 & t <= tz + 2 + 3})

Extract variables from the left-hand-side of a formula

Description

Extract variables from the left-hand-side of a formula

Extract variables from the right-hand side of a formula

Usage

get_lhs_vars(formula)get_rhs_vars(formula)

Arguments

formula

Aformula object.

Extract variables from the left-hand-side of a formula

Description

Extract variables from the left-hand-side of a formula

Extract variables from the right-hand side of a formula

Usage

get_ped_form(  formula,  data = NULL,  tdc_specials = c("concurrent", "cumulative"))

Arguments

formula

Aformula object.

Extract plot information for all special model terms

Description

Given amgcvgamObject, returns the informationused for the default plots produced byplot.gam.

Usage

get_plotinfo(x, ...)

Arguments

x

a fittedgam object as produced bygam().

...

Further arguments passed toplot.gam

Calculate simulation based confidence intervals

Description

Calculate simulation based confidence intervals

Usage

get_sim_ci(newdata, object, alpha = 0.05, nsim = 100L, ...)

helper function for add_trans_ci

Description

helper function for add_trans_ci

Usage

get_sim_cumu(newdata, ...)

Calculate survival probabilities

Description

Calculate survival probabilities

Usage

get_surv_prob(  newdata,  object,  ci = TRUE,  ci_type = c("default", "delta", "sim"),  se_mult = 2L,  time_var = NULL,  interval_length = "intlen",  nsim = 100L,  ...)

Arguments

newdata

object

a fittedgam object as produced bygam().

ci

logical. Indicates if confidence intervals should becalculated. Defaults toTRUE.

se_mult

Factor by which standard errors are multiplied for calculatingthe confidence intervals.

time_var

Name of the variable used for the baseline hazard. Ifnot given, defaults to"tend" forgam fits, else"interval". The latter is assumed to be a factor, the formernumeric.

interval_length

The variable in newdata containing the interval lengths.Can be either bare unquoted variable name or character. Defaults to"intlen".

...

Further arguments passed topredict.gam andget_hazard

Extract variables from the left-hand-side of a formula

Description

Extract variables from the left-hand-side of a formula

Extract variables from the right-hand side of a formula

Usage

get_tdc_form(  formula,  data = NULL,  tdc_specials = c("concurrent", "cumulative"),  invert = FALSE)

Arguments

formula

Aformula object.

Extract variables from the left-hand-side of a formula

Description

Extract variables from the left-hand-side of a formula

Extract variables from the right-hand side of a formula

Usage

get_tdc_vars(formula, specials = "cumulative", data = NULL)

Arguments

formula

Aformula object.

Extract partial effects for specified model terms

Description

Extract partial effects for specified model terms

Usage

get_term(data, fit, term, n = 100, ...)

Arguments

data

A data frame containing variables used to fit the model. Onlyfirst row will be used.

fit

A fitted object of classgam.

term

The (non-linear) model term of interest.

...

Further arguments passed toseq_range.

Extract the partial effects of non-linear model terms

Description

This function basically creates a newdf fromdata foreach term interms, creating a range from minimum and maximum of thepredict(fit, newdata=df, type="terms"). Terms are thenstacked to a tidy data frame.

Usage

get_terms(data, fit, terms, ...)

Arguments

data

A data frame containing variables used to fit the model. Onlyfirst row will be used.

fit

A fitted object of classgam.

terms

A character vector (can be length one). Specifies the termsfor which partial effects will be returned

...

Further arguments passed toseq_range.

Value

A tibble with 5 columns.

Examples

library(survival)fit <- coxph(Surv(time, status) ~ pspline(karno) + pspline(age), data=veteran)terms_df <- veteran %>% get_terms(fit, terms = c("karno", "age"))head(terms_df)tail(terms_df)

Forrest plot of fixed coefficients

Description

Given a model object, returns a data frame with columnsvariable,coef (coefficient),ci_lower (lower 95\ci_upper (upper 95\

Usage

gg_fixed(x, intercept = FALSE, ...)

Arguments

x

A model object.

intercept

Logical, indicating whether intercept term should be included.Defaults toFALSE.

...

Currently not used.

Examples

g <- mgcv::gam(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species, data=iris)gg_fixed(g, intercept=TRUE)gg_fixed(g)

Plot Lag-Lead windows

Description

Given data defining a Lag-lead window, returns respective plot as aggplot2 object.

Usage

gg_laglead(x, ...)## Default S3 method:gg_laglead(x, tz, ll_fun, ...)## S3 method for class 'LL_df'gg_laglead(  x,  high_col = "grey20",  low_col = "whitesmoke",  grid_col = "lightgrey",  ...)## S3 method for class 'nested_fdf'gg_laglead(x, ...)

Arguments

x

Either a numeric vector of follow-up cut points or a suitable object.

...

Further arguments passed to methods.

tz

A vector of exposure times

ll_fun

Function that specifies how the lag-lead matrixshould be constructed. First argument is the follow up timesecond argument is the time of exposure.

high_col

Color used to highlight exposure times within the lag-lead window.

low_col

Color of exposure times outside the lag-lead window.

grid_col

Color of grid lines.

Examples

## Example 1: supply t, tz, ll_fun directly gg_laglead(1:10, tz=-5:5,  ll_fun=function(t, tz) { t >= tz + 2 & t <= tz + 2 + 3})## Example 2: extract information on t, tz, ll_from data with respective attributesdata("simdf_elra", package = "pammtools")gg_laglead(simdf_elra)

Visualize effect estimates for specific covariate combinations

Description

Depending on the plot function and input, creates either a 1-dimensional slices,bivariate surface or (1D) cumulative effect.

Usage

gg_partial(data, model, term, ..., reference = NULL, ci = TRUE)gg_partial_ll(  data,  model,  term,  ...,  reference = NULL,  ci = FALSE,  time_var = "tend")get_partial_ll(  data,  model,  term,  ...,  reference = NULL,  ci = FALSE,  time_var = "tend")

Arguments

data

Data used to fit themodel.

model

A suitable model object which will be used to estimate thepartial effect ofterm.

term

A character string indicating the model term for which partialeffects should be plotted.

...

Covariate specifications (expressions) that will be evaluatedby looking for variables inx. Must be of the formz = f(z)wherez is a variable in the data set andf a knownfunction that can be usefully applied toz. Note that this is alsonecessary for single value specifications (e.g.age = c(50)).For data in PED (piece-wise exponential data) format, one can also specifythe time argument, but see "Details" an "Examples" below.

reference

If specified, should be a list with covariate value pairs,e.g.list(x1 = 1, x2=50). The calculated partial effect will be relativeto an observation specified inreference.

ci

Logical. Indicates if confidence intervals for thetermof interest should be calculated/plotted. Defaults toTRUE.

time_var

The name of the variable that was used inmodel torepresent follow-up time.

Plot Normal QQ plots for random effects

Description

Plot Normal QQ plots for random effects

Usage

gg_re(x, ...)

Arguments

x

a fittedgam object as produced bygam().

...

Further arguments passed toplot.gam

Examples

library(pammtools)data("patient")ped <- patient %>% dplyr::slice(1:100) %>% as_ped(Surv(Survdays, PatientDied)~ ApacheIIScore + CombinedicuID,)pam <- mgcv::gam(ped_status ~ s(tend) + ApacheIIScore + s(CombinedicuID, bs="re"), data=ped, family=poisson(), offset=offset)gg_re(pam)plot(pam, select = 2)

Plot 1D (smooth) effects

Description

Flexible, high-level plotting function for (non-linear) effects conditionalon further covariate specifications and potentially relative toa comparison specification.

Usage

gg_slice(data, model, term, ..., reference = NULL, ci = TRUE)

Arguments

data

Data used to fit themodel.

model

A suitable model object which will be used to estimate thepartial effect ofterm.

term

A character string indicating the model term for which partialeffects should be plotted.

...

reference

If specified, should be a list with covariate value pairs,e.g.list(x1 = 1, x2=50). The calculated partial effect will be relativeto an observation specified inreference.

ci

Logical. Indicates if confidence intervals for thetermof interest should be calculated/plotted. Defaults toTRUE.

Examples

ped <- tumor[1:200, ] %>% as_ped(Surv(days, status) ~ . )model <- mgcv::gam(ped_status~s(tend) + s(age, by = complications), data=ped,  family = poisson(), offset=offset)make_newdata(ped, age = seq_range(age, 20), complications = levels(complications))gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications))gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications), ci = FALSE)gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications),  reference=list(age = 50))

Plot smooth 1d terms of gam objects

Description

Given a gam model this convenience function returns a plot of allsmooth terms contained in the model. If more than one smooth is present, thedifferent smooth are faceted.

Usage

gg_smooth(x, ...)## Default S3 method:gg_smooth(x, fit, ...)

Arguments

x

A data frame or object of classped.

...

Further arguments passed toget_terms

fit

A model object.

Value

Aggplot object.

Examples

g1 <- mgcv::gam(Sepal.Length ~ s(Sepal.Width) + s(Petal.Length), data=iris)gg_smooth(iris, g1, terms=c("Sepal.Width", "Petal.Length"))

Plot tensor product effects

Description

Given a gam model this convenience function returns aggplot2 objectdepicting 2d smooth terms specified in the model as heat/contour plots. Ifmore than one 2d smooth term is present individual terms are faceted.

Usage

gg_tensor(x, ci = FALSE, ...)

Arguments

x

a fittedgam object as produced bygam().

ci

A logical value indicating whether confidence intervals should becalculated and returned. Defaults toTRUE.

...

Further arguments passed toplot.gam

Examples

g <- mgcv::gam(Sepal.Length ~ te(Sepal.Width, Petal.Length), data=iris)gg_tensor(g)gg_tensor(g, ci=TRUE)gg_tensor(update(g, .~. + te(Petal.Width, Petal.Length)))

Checks if data contains timd-dependent covariates

Description

Checks if data contains timd-dependent covariates

Usage

has_tdc(data, id_var)

Arguments

data

A data frame (potentially) containing time-dependent covariates.

id_var

A character indicating the grouping variable. For each covariateit will be checked if their values change within a group specified byid_var.

Value

Logical.TRUE if data contains time-dependent covariates, elseFALSE.

Create start/end times and interval information

Description

Given interval breaks points, returns data frame with information oninterval start time, interval end time, interval length and a factorvariable indicating the interval (left open intervals). If an object of classped is provided, extracts unique interval information from object.

Usage

int_info(x, ...)## Default S3 method:int_info(x, min_time = 0L, ...)## S3 method for class 'data.frame'int_info(x, min_time = 0L, ...)## S3 method for class 'ped'int_info(x, ...)## S3 method for class 'pamm'int_info(x, ...)

Arguments

x

A numeric vector of cut points in which the follow-up should bepartitioned in or object of classped.

...

Currently ignored.

min_time

Only intervals that have lower borders larger thanthis value will be included in the resulting data frame.

Value

A data frame containing the start and end times of theintervals specified by thex argument. Additionally, the intervallength, interval mid-point and a factor variable indicating the intervals.

Examples

## create interval information from cut pointsint_info(c(1, 2.3, 5))## extract interval information used to create ped objecttdf <- data.frame(time=c(1, 2.3, 5), status=c(0, 1, 0))ped <- tdf %>% as_ped(Surv(time, status)~.,)int_info(ped)

Create design matrix from a suitable object

Description

Create design matrix from a suitable object

Usage

make_X(object, ...)## Default S3 method:make_X(object, newdata, ...)## S3 method for class 'gam'make_X(object, newdata, ...)

Arguments

object

A suitable object from which a design matrix can be generated.Often a model object.

newdata

A data frame from which design matrix will be constructed

Create design matrix from a suitable object

Description

Create design matrix from a suitable object

Usage

## S3 method for class 'scam'make_X(object, newdata, ...)

Arguments

object

A suitable object from which a design matrix can be generated.Often a model object.

newdata

A data frame from which design matrix will be constructed

Construct a data frame suitable for prediction

Description

This functions provides a flexible interface to create a data set thatcan be plugged in asnewdata argument to a suitablepredictfunction (or similar).The function is particularly useful in combination with one of theadd_* functions, e.g.,add_term,add_hazard, etc.

Usage

make_newdata(x, ...)## Default S3 method:make_newdata(x, ...)## S3 method for class 'ped'make_newdata(x, ...)## S3 method for class 'fped'make_newdata(x, ...)

Arguments

x

A data frame (or object that inherits fromdata.frame).

...

Details

Depending on the type of variables inx, mean or modus valueswill be used for variables not specified in ellipsis(see alsosample_info). Ifx is an objectthat inherits from classped, useful data set completion will beattempted depending on variables specified in ellipsis. This is especiallyuseful, when creating a data set with different time points, e.g. tocalculate survival probabilities over time (add_surv_prob)or to calculate a time-varying covariate effects (add_term).To do so, the time variable has to be specified in..., e.g.,tend = seq_range(tend, 20). The problem with this specification is thatnot all values produced byseq_range(tend, 20) will be actual valuesoftend used at the stage of estimation (and in general, it willoften be tedious to specify exacttend values).make_newdatatherefore finds the correct interval and setstend to the respectiveinterval endpoint. For example, if the intervals of the PED object are(0,1], (1,2] thentend = 1.5 will be set to2 and theremaining time-varying information (e.g. offset) completed accordingly.See examples below.

Examples

# General functionalitytumor %>% make_newdata()tumor %>% make_newdata(age=c(50))tumor %>% make_newdata(days=seq_range(days, 3), age=c(50, 55))tumor %>% make_newdata(days=seq_range(days, 3), status=unique(status), age=c(50, 55))# mean/modus values of unspecified variables are calculated over whole datatumor %>% make_newdata(sex=unique(sex))tumor %>% group_by(sex) %>% make_newdata()# Examples for PED dataped <- tumor %>% slice(1:3) %>% as_ped(Surv(days, status)~., cut = c(0, 500, 1000))ped %>% make_newdata(age=c(50, 55))# if time information is specified, other time variables will be specified# accordingly and offset calculated correctlyped %>% make_newdata(tend = c(1000), age = c(50, 55))ped %>% make_newdata(tend = unique(tend))ped %>% group_by(sex) %>% make_newdata(tend = unique(tend))# tend is set to the end point of respective interval:ped <- tumor %>% as_ped(Surv(days, status)~.)seq_range(ped$tend, 3)make_newdata(ped, tend = seq_range(tend, 3))

Create matrix components for cumulative effects

Description

These functions are called internally byget_cumulative andshould usually not be called directly.

Usage

make_time_mat(data, nz)make_latency_mat(data, tz)make_lag_lead_mat(data, tz, ll_fun = function(t, tz) t >= tz)make_z_mat(data, z_var, nz, ...)

Arguments

data

A data set (or similar) from which meta information on cut-points,interval-specific time, covariates etc. can be obtained.

z_var

Which should be transformed into functional covariate formatsuitable to fit cumulative effects inmgcv::gam.

Calculate the modus

Description

Calculate the modus

Usage

modus(var)

Arguments

var

A atomic vector

Create nested data frame from data with time-dependent covariates

Description

Provides methods to nest data with time-dependent covariates (TDCs).Aformula must be provided where the right hand side (RHS) containsthe structure of the TDCs

Usage

nest_tdc(data, formula, ...)## Default S3 method:nest_tdc(data, formula, ...)## S3 method for class 'list'nest_tdc(data, formula, ...)

Arguments

data

A suitable data structure (e.g. unnested data frame withconcurrent TDCs or a list where each element is a data frame, potentiallycontaining TDCs as specified in the RHS offormula).Only TDCs present informula will be returned.

formula

A two sided formula with a two part RHS, where the secondpart indicates the structure of the TDC structure.

...

Further arguments passed to methods.

Fit a piece-wise exponential additive model

Description

A thin wrapper aroundgam, however, some arguments areprespecified:family=poisson() andoffset=data$offset.These two can not be overwritten. In many cases it will also be advisable tosetmethod="REML".

Usage

pamm(formula, data = list(), ..., trafo_args = NULL, engine = "gam")is.pamm(x)## S3 method for class 'pamm'print(x, ...)## S3 method for class 'pamm'summary(object, ...)## S3 method for class 'pamm'plot(x, ...)

Arguments

formula

A GAM formula, or a list of formulae (seeformula.gam and alsogam.models). These are exactly like the formula for a GLM except that smooth terms,s,te,ti andt2, can be added to the right hand side to specify that the linear predictor depends on smooth functions of predictors (or linear functionals of these).

data

A data frame or list containing the model response variable and covariates required by the formula. By default the variables are taken fromenvironment(formula): typically the environment from whichgam is called.

...

Further arguments passed toengine.

trafo_args

A named list. If data is not in PED format,as_pedwill be called internally with arguments provided intrafo_args.

engine

Character name of the function that will be called to fit themodel. The intended entries are either"gam" or"bam"(both from packagemgcv).

x

Any R object.

object

An object of classpamm as returned bypamm.

Examples

ped <- tumor[1:100, ] %>% as_ped(Surv(days, status) ~ complications, cut = seq(0, 3000, by = 50))pam <- pamm(ped_status ~ s(tend) + complications, data = ped)summary(pam)## Alternativelypamm( ped_status ~ s(tend) + complications, data = tumor[1:100, ],trafo_args = list(formula = Surv(days, status)~complications))

Survival data of critically ill ICU patients

Description

A data set containing the survival time (or hospital release time) amongother covariates.The full data is availablehere.The following variables are provided:

Year: The year of ICU Admission
CombinedicuID: Intensive Care Unit (ICU) ID
CombinedID: Patient identificator
Survdays: Survival time of patients. Here it is assumed that patientssurvive until t=30 if released from hospital.
PatientDied: Status indicator; 1=death, 0=censoring
survhosp: Survival time in hospital. Here it is assumed that patientsare censored at time of hospital release (potentially informative)
Gender: Male or female
Age: The patients age at Admission
AdmCatID: Admission category: medical, surgical elective or surgical emergency
ApacheIIScore: The patient's Apache II Score at Admission
BMI: Patient's Body Mass Index
DiagID2: Diagnosis at admission in 9 categories

Usage

patient

Format

An object of classdata.frame with 2000 rows and 12 columns.

Extract interval information and median/modus values for covariates

Description

Given an object of classped, returns data frame with one row for eachinterval containing interval information, mean values for numericalvariables and modus for non-numeric variables in the data set.

Usage

ped_info(ped)## S3 method for class 'ped'ped_info(ped)

Arguments

ped

An object of classped as returned byas_ped.

Value

A data frame with one row for each unique interval inped.

Examples

ped <- tumor[1:4,] %>% as_ped(Surv(days, status)~ sex + age)ped_info(ped)

S3 method for pamm objects for compatibility with package pec

Description

S3 method for pamm objects for compatibility with package pec

Usage

## S3 method for class 'pamm'predictSurvProb(object, newdata, times, ...)

Arguments

object

A fitted model from which to extract predicted survivalprobabilities

newdata

A data frame containing predictor variable combinations forwhich to compute predicted survival probabilities.

times

A vector of times in the range of the response variable, e.g.times when the response is a survival object, at which to return thesurvival probabilities.

...

Additional arguments that are passed on to the current method.

Extract information on concurrent effects

Description

Extract information on concurrent effects

Usage

prep_concurrent(x, formula, ...)## S3 method for class 'list'prep_concurrent(x, formula, ...)

Arguments

x

A suitable object from which variables contained informula can be extracted.

...

Further arguments passed to methods.

Draw random numbers from piece-wise exponential distribution.

Description

This is a copy of the same function fromrpexp from packagemsm.Copied here to reduce dependencies.

Usage

rpexp(n = 1, rate = 1, t = 0)

Arguments

n

number of observations. Iflength(n) > 1, the length istaken to be the number required.

rate

vector of rates.

t

vector of the same length asrate, giving the times at whichthe rate changes. The values oft should be in increasing order.

Extract information of the sample contained in a data set

Description

Given a data set and grouping variables, this function returns mean valuesfor numeric variables and modus for characters and factors. Usuallythis function should not be called directly but will rather be calledas part of a call tomake_newdata.

Usage

sample_info(x)## S3 method for class 'data.frame'sample_info(x)## S3 method for class 'ped'sample_info(x)## S3 method for class 'fped'sample_info(x)

Arguments

x

A data frame (or object that inherits fromdata.frame).

Value

A data frame containing sample information (for each group).If applied to an object of classped, the sample means of theoriginal data is returned.Note: When applied to aped object, that doesn't contain covariates(only interval information), returns data frame with 0 columns.

Generate a sequence over the range of a vector

Description

Stolen fromhere

Usage

seq_range(x, n, by, trim = NULL, expand = NULL, pretty = FALSE)

Arguments

x

A numeric vector

n,by

Specify the output sequence either by supplying thelength of the sequence withn, or the spacing between valuewithby. Specifying both is an error.

I recommend that you name these arguments in order to make it clear tothe reader.

trim

Optionally, trim values off the tails.trim / 2 * length(x) values are removed from each tail.

expand

Optionally, expand the range byexpand * (1 + range(x)(computed after trimming).

pretty

IfTRUE, will generate a pretty sequence. Ifnis supplied, this will usepretty() instead ofseq(). Ifby is supplied, it will round the firstvalue to a multiple ofby.

Examples

x <- rcauchy(100)seq_range(x, n = 10)seq_range(x, n = 10, trim = 0.1)seq_range(x, by = 1, trim = 0.1)# Make pretty sequencesy <- runif (100)seq_range(y, n = 10)seq_range(y, n = 10, pretty = TRUE)seq_range(y, n = 10, expand = 0.5, pretty = TRUE)seq_range(y, by = 0.1)seq_range(y, by = 0.1, pretty = TRUE)

Simulate survival times from the piece-wise exponential distribution

Description

Simulate survival times from the piece-wise exponential distribution

Usage

sim_pexp(formula, data, cut)

Arguments

formula

An extended formula that specifies the linear predictor.If you want to include a smooth baselineor time-varying effects, uset within your formula asif it was a covariate in the data, although it is not and should notbe included in thedata provided tosim_pexp. See examplesbelow.

data

A data set with variables specified informula.

cut

A sequence of time-points starting with 0.

Examples

library(survival)library(dplyr)library(pammtools)# set number of observations/subjectsn <- 250# create data set with variables which will affect the hazard rate.df <- cbind.data.frame(x1 = runif (n, -3, 3), x2 = runif (n, 0, 6)) %>% as_tibble()# the formula which specifies how covariates affet the hazard ratef0 <- function(t) { dgamma(t, 8, 2) *6}form <- ~ -3.5 + f0(t) -0.5*x1 + sqrt(x2)set.seed(24032018)sim_df <- sim_pexp(form, df, 1:10)head(sim_df)plot(survfit(Surv(time, status)~1, data = sim_df ))# for control, estimate with Cox PHmod <- coxph(Surv(time, status) ~ x1 + pspline(x2), data=sim_df)coef(mod)[1]layout(matrix(1:2, nrow=1))termplot(mod, se = TRUE)# and using PAMslayout(1)ped <- sim_df %>% as_ped(Surv(time, status)~., max_time=10)library(mgcv)pam <- gam(ped_status ~ s(tend) + x1 + s(x2), data=ped, family=poisson, offset=offset)coef(pam)[2]plot(pam, page=1)## Not run: # Example 2: Functional covariates/cumulative coefficients# function to generate one exposure profile, tz is a vector of time points# at which TDC z was observedrng_z = function(nz) {  as.numeric(arima.sim(n = nz, list(ar = c(.8, -.6))))}# two different exposure times  for two different exposurestz1 <- 1:10tz2 <- -5:5# generate exposures and add to data setdf <- df %>%  add_tdc(tz1, rng_z) %>%  add_tdc(tz2, rng_z)df# define tri-variate function of time, exposure time and exposure zft <- function(t, tmax) {  -1*cos(t/tmax*pi)}fdnorm <- function(x) (dnorm(x,1.5,2)+1.5*dnorm(x,7.5,1))wpeak2 <- function(lag) 15*dnorm(lag,8,10)wdnorm <- function(lag) 5*(dnorm(lag,4,6)+dnorm(lag,25,4))f_xyz1 <- function(t, tz, z) {  ft(t, tmax=10) * 0.8*fdnorm(z)* wpeak2(t - tz)}f_xyz2 <- function(t, tz, z) {  wdnorm(t-tz) * z}# define lag-lead window functionll_fun <- function(t, tz) {t >= tz}ll_fun2 <- function(t, tz) {t - 2 >= tz}# simulate data with cumulative effectsim_df <- sim_pexp(  formula = ~ -3.5 + f0(t) -0.5*x1 + sqrt(x2)|     fcumu(t, tz1, z.tz1, f_xyz=f_xyz1, ll_fun=ll_fun) +     fcumu(t, tz2, z.tz2, f_xyz=f_xyz2, ll_fun=ll_fun2),  data = df,  cut = 0:10)## End(Not run)

Simulate data for competing risks scenario

Description

Simulate data for competing risks scenario

Usage

sim_pexp_cr(formula, data, cut)

Simulated data with cumulative effects

Description

This is data simulated using thesim_pexp function.It contains two time-constant and two time-dependent covariates (observedon different exposure time grids). The code used for simulation iscontained in the examples of?sim_pexp.

Usage

simdf_elra

Format

An object of classnested_fdf (inherits fromsim_df,tbl_df,tbl,data.frame) with 250 rows and 9 columns.

New basis for penalized lag selection

Description

Originally proposed in Obermeier et al., 2015, Flexible Distributed Lags for Modelling Earthquake Data,Journal of the Royal Statistical Society: Series C (Applied Statistics),10.1111/rssc.12077.Here extended in order to penalize lead times in addition to lag times.Ideally the lag-lead window would then be selected in a data-driven fashion.Treat as experimental.

Usage

## S3 method for class 'fdl.smooth.spec'smooth.construct(object, data, knots)

Arguments

object

An object handled by mgcv

data

The data set

knots

A vector of knots

Function to transform data without time-dependent covariates into piece-wiseexponential data format

Description

Function to transform data without time-dependent covariates into piece-wiseexponential data format

Usage

split_data(  formula,  data,  cut = NULL,  max_time = NULL,  multiple_id = FALSE,  ...)

Arguments

formula

data

cut

Split points, used to partition the follow up into intervals.If unspecified, all unique event times will be used.

max_time

Ifcut is unspecified, this will be the lastpossible event time. All event times aftermax_timewill be administratively censored atmax_time.

multiple_id

Are occurences of same id allowed (per transition).Defaults toFALSE, but is sometimes set toTRUE, e.g., in case ofmulti-state models with back transitions.

...

Further arguments passed to thedata.frame method andeventually tosurvSplit

Split data to obtain recurrent event data in PED format

Description

Currently, the input data must be in start-stop notation for each spell andcontain a colum that indicates the spell (event number).

Usage

split_data_multistate(  formula,  data,  transition = character(),  cut = NULL,  max_time = NULL,  event = 1L,  min_events = 1L,  timescale = c("gap", "calendar"),  ...)

Arguments

formula

data

transition

A character indicating the column in data that indicates theevent/episode number for recurrent events.

cut

Split points, used to partition the follow up into intervals.If unspecified, all unique event times will be used.

max_time

Ifcut is unspecified, this will be the lastpossible event time. All event times aftermax_timewill be administratively censored atmax_time.

event

The value that encodes the occurrence of an event in the data set.

min_events

Minimum number of events for each event number.

timescale

Defines the timescale for the recurrent event data transformation.Defaults to"gaptime".

...

Further arguments passed to thedata.frame method andeventually tosurvSplit

Time until staphylococcus aureaus infection in children, with possible recurrence

Description

This dataset originates from the Drakenstein child health study.The data contains the following variables:

id: Randomly generated unique child ID
t.start: The time at which the child enters the risk set for the $k$-th event
t.stop: Time of $k$-th infection or censoring

enum: Event number. Maximum of 6.
hiv

Usage

staph

Format

An object of classtbl_df (inherits fromtbl,data.frame) with 374 rows and 6 columns.

Extract fixed coefficient table from model object

Description

Given a model object, returns a data frame with columnsvariable,coef (coefficient),ci_lower (lower 95\ci_upper (upper 95\

Usage

tidy_fixed(x, ...)## S3 method for class 'gam'tidy_fixed(x, intercept = FALSE, ...)## S3 method for class 'coxph'tidy_fixed(x, ...)

Arguments

x

A model object.

...

Currently not used.

intercept

Should intercept also be returned? Defaults toFALSE.

Examples

library(survival)gc <- coxph(Surv(days, status)~age + sex, data = tumor)tidy_fixed(gc)

Extract random effects in tidy data format.

Description

Extract random effects in tidy data format.

Usage

tidy_re(x, keep = c("fit", "main", "xlab", "ylab"), ...)

Arguments

x

a fittedgam object as produced bygam().

keep

A vector of variables to keep.

...

Further arguments passed toplot.gam

Extract 1d smooth objects in tidy data format.

Description

Extract 1d smooth objects in tidy data format.

Usage

tidy_smooth(x, keep = c("x", "fit", "se", "xlab", "ylab"), ci = TRUE, ...)

Arguments

x

a fittedgam object as produced bygam().

keep

A vector of variables to keep.

ci

A logical value indicating whether confidence intervals should becalculated and returned. Defaults toTRUE.

...

Further arguments passed toplot.gam

Extract 2d smooth objects in tidy format.

Description

Extract 2d smooth objects in tidy format.

Usage

tidy_smooth2d(  x,  keep = c("x", "y", "fit", "se", "xlab", "ylab", "main"),  ci = FALSE,  ...)

Arguments

x

a fittedgam object as produced bygam().

keep

A vector of variables to keep.

ci

A logical value indicating whether confidence intervals should becalculated and returned. Defaults toTRUE.

...

Further arguments passed toplot.gam

Stomach area tumor data

Description

Information on patients treated for a cancer diseaselocated in the stomach area.The data set includes:

days: Time from operation until death in days.
status: Event indicator (0 = censored, 1 = death).
age: The subject's age.
sex: The subject's sex (male/female).
charlson_score: Charlson comorbidity score, 1-6.
transfusion: Has subject received transfusions (no/yes).
complications: Did major complications occur during operation (no/yes).
metastases: Did the tumor develop metastases? (no/yes).
resection: Was the operation accompanied by a major resection (no/yes).

Usage

tumor

Format

An object of classtbl_df (inherits fromtbl,data.frame) with 776 rows and 9 columns.

Warn if new t_j are used

Description

Warn if new t_j are used

Usage

warn_about_new_time_points(object, newdata, ...)## S3 method for class 'pamm'warn_about_new_time_points(object, newdata, ...)

Warn if new t_j are used

Description

Warn if new t_j are used

Usage

## S3 method for class 'glm'warn_about_new_time_points(object, newdata, time_var, ...)

Movatterモバイル変換

pammtools: Piece-wise exponential Additive Mixed Modeling tools.

Description

Details

Author(s)

References

See Also

Pipe operator

Description

Usage

Add cumulative incidence function to data

Description

Usage

Arguments

Add counterfactual observations for possible transitions

Description

Usage

Arguments

Add predicted (cumulative) hazard to data set

Description

Usage

Arguments

See Also

Examples

Add survival probability estimates

Description

Usage

Arguments

See Also

Examples

Add time-dependent covariate to a data set

Description

Usage

Arguments

Embeds the data set with the specified (relative) term contribution

Description

Usage

Arguments

Examples

Add transition probabilities confidence intervals

Description

Usage

Add transition probabilities

Description

Usage

Arguments

See Also

Examples

Transform crps object to data.frame

Description

Usage

Arguments

Transform data to Piece-wise Exponential Data (PED)

Description

Usage

Arguments

Value

Examples

Competing risks trafo

Description

Usage

Arguments

Value

Examples

Calculate confidence intervals

Description

Usage

Arguments

Create a data frame from all combinations of data frames

Description

Usage

Arguments

Examples

Calculate difference in cumulative hazards and respective standard errors

Description

Usage

Arguments

Formula specials for defining time-dependent covariates

Description

Usage

Time-dependent covariates of the`patient` data set.

`dplyr` Verbs for`ped`-Objects