fit_zoid: Fit a trinomial mixture model with Stan

In zoid: Bayesian Zero-and-One Inflated Dirichlet Regression Modelling

Fit a trinomial mixture model with Stan

Description

Fit a trinomial mixture model that optionally includes covariates to estimate effects 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, then the 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 loo::loo_moment_match(). This increases memory by adding all temporary parmaeters to be saved and returned
prior_sd	Parameter to be passed in to use as standard deviation of the normal distribution in transformed space. If covariates are included this defaults to 1, but for models with single replicate, defaults to 1/n_bins.
...	Any other arguments to pass to rstan::sampling().

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 covariates
fit <- fit_zoid(data_matrix = y, chains = 1, iter = 100)

# fit a model with 1 factor
design <- 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 effects
set.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)

zoid documentation built on May 29, 2024, 2 a.m.