| Title: | Bayesian Zero-and-One Inflated Dirichlet Regression Modelling |
| Version: | 1.3.1 |
| Description: | Fits Dirichlet regression and zero-and-one inflated Dirichlet regression with Bayesian methods implemented in Stan. These models are sometimes referred to as trinomial mixture models; covariates and overdispersion can optionally be included. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Biarch: | true |
| URL: | https://noaa-nwfsc.github.io/zoid/ |
| BugReports: | https://github.com/noaa-nwfsc/zoid/issues |
| Depends: | R (≥ 3.4.0) |
| Imports: | gtools, methods, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1),rstan (≥ 2.26.0), rstantools (≥ 2.1.1) |
| Suggests: | testthat, knitr, rmarkdown |
| LinkingTo: | BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0),RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥2.26.0) |
| SystemRequirements: | GNU make |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2024-01-24 17:46:08 UTC; ericward |
| Author: | Eric J. Ward [aut, cre], Alexander J. Jensen [aut], Ryan P. Kelly [aut], Andrew O. Shelton [aut], William H. Satterthwaite [aut], Eric C. Anderson [aut] |
| Maintainer: | Eric J. Ward <eric.ward@noaa.gov> |
| Repository: | CRAN |
| Date/Publication: | 2024-01-24 18:10:02 UTC |
The 'zoid' package.
Description
A DESCRIPTION OF THE PACKAGE
References
Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org
Random generation of datasets using the dirichlet broken stick method
Description
Random generation of datasets using the dirichlet broken stick method
Usage
broken_stick( n_obs = 1000, n_groups = 10, ess_fraction = 1, tot_n = 100, p = NULL)Arguments
n_obs | Number of observations (rows of data matrix to simulate). Defaults to 10 |
n_groups | Number of categories for each observation (columns of data matrix). Defaults to 10 |
ess_fraction | The effective sample size fraction, defaults to 1 |
tot_n | The total sample size to simulate for each observation. This is approximate and the actualsimulated sample size will be slightly smaller. Defaults to 100 |
p | The stock proportions to simulate from, as a vector. Optional, and when not included,random draws from the dirichlet are used |
Value
A 2-element list, whose 1st elementX_obs is the simulated dataset, and whose2nd element is the underlying vector of proportionsp used to generate the data
Examples
y <- broken_stick(n_obs = 3, n_groups = 5, tot_n = 100)# add custom proportionsy <- broken_stick( n_obs = 3, n_groups = 5, tot_n = 100, p = c(0.1, 0.2, 0.3, 0.2, 0.2))Data from Satterthwaite, W.H., Ciancio, J., Crandall, E., Palmer-Zwahlen,M.L., Grover, A.M., O’Farrell, M.R., Anson, E.C., Mohr, M.S. & Garza,J.C. (2015). Stock composition and ocean spatial distribution fromCalifornia recreational chinook salmon fisheries using genetic stockidentification. Fisheries Research, 170, 166–178. The datagenetic data collected from port-based sampling of recreationally-landedChinook salmon in California from 1998-2002.
Description
Data from Satterthwaite, W.H., Ciancio, J., Crandall, E., Palmer-Zwahlen,M.L., Grover, A.M., O’Farrell, M.R., Anson, E.C., Mohr, M.S. & Garza,J.C. (2015). Stock composition and ocean spatial distribution fromCalifornia recreational chinook salmon fisheries using genetic stockidentification. Fisheries Research, 170, 166–178. The datagenetic data collected from port-based sampling of recreationally-landedChinook salmon in California from 1998-2002.
Usage
chinookFormat
A data frame.
Data from Magnussen, E. 2011. Food and feeding habits of cod (Gadus morhua)on the Faroe Bank. – ICES Journal of Marine Science, 68: 1909–1917. The datahere are Table 3 from the paper, with sample proportions (columns w) multipliedby total weight to yield total grams (g) for each sample-diet item combination. Dasheshave been replaced with 0s.
Description
Data from Magnussen, E. 2011. Food and feeding habits of cod (Gadus morhua)on the Faroe Bank. – ICES Journal of Marine Science, 68: 1909–1917. The datahere are Table 3 from the paper, with sample proportions (columns w) multipliedby total weight to yield total grams (g) for each sample-diet item combination. Dasheshave been replaced with 0s.
Usage
coddietFormat
A data frame.
Extract point estimates of compositions from fitted model.
Description
Extract point estimates of compositions from fitted model.
Usage
fit_dirichlet(data)Arguments
data | The data to fit the dirichlet distribution to |
Find appropriate standard deviations for prior
Description
Find appropriate standard deviations for prior
Usage
fit_prior(n_bins, n_draws = 10000, target = 1/n_bins, iterations = 5)Arguments
n_bins | Bins for the Dirichlet distribution |
n_draws | Numbers of samples to use for doing calculation |
target | The goal of the specified prior, e.g. 1 or 1/n_bins |
iterations | to try, to ensure robust solution. Defaults to 5 |
Value
A 3-element list consisting ofsd (the approximate standard deviationin transformed space that gives a similar prior to that specified),value (thevalue of the root mean squared percent error function being minimized),andconvergence (0 if convergence occurred, error code fromoptim() otherwise)
Examples
# fit model with 3 components / alpha = 1set.seed(123)f <- fit_prior(n_bins = 3, n_draws = 1000, target = 1)# fit model with 20 components / alpha = 1/20f <- fit_prior(n_bins = 20, n_draws = 1000, target = 1 / 20)Fit a trinomial mixture model with Stan
Description
Fit a trinomial mixture model that optionally includes covariates to estimateeffects of factor or continuous variables on proportions.
Usage
fit_zoid( formula = NULL, design_matrix, data_matrix, chains = 3, iter = 2000, warmup = floor(iter/2), overdispersion = FALSE, overdispersion_sd = 5, posterior_predict = FALSE, moment_match = FALSE, prior_sd = NA, ...)Arguments
formula | The model formula for the design matrix. Does not need to have a response specified. If =NULL, thenthe design matrix is ignored and all rows are treated as replicates |
design_matrix | A data frame, dimensioned as number of observations, and covariates in columns |
data_matrix | A matrix, with observations on rows and number of groups across columns |
chains | Number of mcmc chains, defaults to 3 |
iter | Number of mcmc iterations, defaults to 2000 |
warmup | Number iterations for mcmc warmup, defaults to 1/2 of the iterations |
overdispersion | Whether or not to include overdispersion parameter, defaults to FALSE |
overdispersion_sd | Prior standard deviation on 1/overdispersion parameter, Defaults to inv-Cauchy(0,5) |
posterior_predict | Whether or not to return draws from posterior predictive distribution (requires more memory) |
moment_match | Whether to do moment matching via |
prior_sd | Parameter to be passed in to use as standard deviation of the normal distribution in transformed space. Ifcovariates are included this defaults to 1, but for models with single replicate, defaults to 1/n_bins. |
... | Any other arguments to pass to |
Examples
y <- matrix(c(3.77, 6.63, 2.60, 0.9, 1.44, 0.66, 2.10, 3.57, 1.33), nrow = 3, byrow = TRUE)# fit a model with no covariatesfit <- fit_zoid(data_matrix = y, chains = 1, iter = 100)# fit a model with 1 factordesign <- data.frame("fac" = c("spring", "spring", "fall"))fit <- fit_zoid(formula = ~fac, design_matrix = design, data_matrix = y, chains = 1, iter = 100)# try a model with random effectsset.seed(123)y <- matrix(runif(99,1,4), ncol=3)design <- data.frame("fac" = sample(letters[1:5], size=nrow(y), replace=TRUE))design$fac <- as.factor(design$fac)fit <- fit_zoid(formula = ~(1|fac), design_matrix = design, data_matrix = y, chains = 1, iter = 100)Extract estimates of predicted latent proportions.
Description
Extract point estimates of compositions from fitted model.
Usage
get_fitted(fitted_model, conf_int = 0.05)Arguments
fitted_model | The fitted model returned as an rstan object from the call to zoid |
conf_int | Parameter controlling confidence intervals calculated, defaults to 0.05for 95% intervals |
Value
A list containing the posterior summaries of estimated parameters, withelementmu (the predicted values in normal space). For predictionsin transformed space, or overdispersion, seeget_pars()
Examples
y <- matrix(c(3.77, 6.63, 2.60, 0.9, 1.44, 0.66, 2.10, 3.57, 1.33), nrow = 3, byrow = TRUE)# fit a model with no covariatesfit <- fit_zoid(data_matrix = y)p_hat <- get_fitted(fit)Extract parameters from fitted model.
Description
Extract estimated parameters from fitted model.
Usage
get_pars(fitted_model, conf_int = 0.05)Arguments
fitted_model | The fitted model returned as an rstan object from the call to zoid |
conf_int | Parameter controlling confidence intervals calculated, defaults to 0.05for 95% intervals |
Value
A list containing the posterior summaries of estimated parameters. At minimum,this will includep (the estimated proportions) andbetas (the predicted values intransformed space). For models with overdispersion, an extraelementphi will also be returned, summarizing overdispersion. For models with randomintercepts, estimates of the group level effects will also be returned aszetas (again,in transformed space). For predictionsin normal space, seeget_fitted()
Examples
y <- matrix(c(3.77, 6.63, 2.60, 0.9, 1.44, 0.66, 2.10, 3.57, 1.33), nrow = 3, byrow = TRUE)# fit a model with no covariatesfit <- fit_zoid(data_matrix = y)p_hat <- get_pars(fit)Fit a trinomial mixture model that optionally includes covariates to estimateeffects of factor or continuous variables on proportions.
Description
Fit a trinomial mixture model that optionally includes covariates to estimateeffects of factor or continuous variables on proportions.
Usage
parse_re_formula(formula, data)Arguments
formula | The model formula for the design matrix. |
data | The data matrix used to construct RE design matrix |
Find appropriate prior for a given target distribution.
Description
Extract point estimates of compositions from fitted model.
Usage
rmspe_calc(par, n_bins, n_draws, target)Arguments
par | The parameter (standard deviation) to be searched over to find a Dirichlet equivalent |
n_bins | Bins for the Dirichlet distribution |
n_draws | Numbers of samples to use for doing calculation |
target | The goal of the specified prior, e.g. 1 or 1/n_bins |