mcml_glmer: lme4 style generlized linear mixed model

Description

A wrapper for Model stochastic maximum likelihood model fitting replicating lme4's syntax

Usage

mcml_glmer(
  formula,
  data,
  family,
  start = NULL,
  offset = NULL,
  verbose = 1L,
  iter.warmup = 100,
  iter.sampling = 50,
  weights = NULL,
  ...
)

Value

A `mcml` model fit object.

Arguments

formula: A two-sided linear formula object including both the fixed and random effects specifications, see Details.
data: A data frame containing the variables named in `formula`.
family: A family object expressing the distribution and link function of the model, see family.
start: Optional. A vector of starting values for the fixed effects.
offset: Optional. A vector of offset values.
verbose: Integer, controls the level of detail printed to the console, either 0 (no output), 1 (main output), or 2 (detailed output)
iter.warmup: The number of warmup iterations for the MCMC sampling step of each iteration.
iter.sampling: The number of sampling iterations for the MCMC sampling step of each iteration.
weights: Optional. A vector of observation level weights to apply to the model fit.
...: additional arguments passed to `Model$MCML()`

Details

This function aims to replicate the syntax of lme4's `lmer` command. The specified formula can be the standard lme4 syntax, or alternatively a glmmrBase style formula can also be used to allow for the wider range of covariance function specifications. For example both `y~x+(1|cl/t)` and `y~x+(1|gr(cl))+(1|gr(cl)*ar1(t))` would be valid formulae.

Examples

Run this code

#create a data frame describing a cross-sectional parallel cluster
#randomised trial
df <- nelder(~(cl(10)*t(5)) > ind(10))
df$int <- 0
df[df$cl > 5, 'int'] <- 1
# simulate data using the Model class
df$y <- Model$new(
  formula = ~ factor(t) + int - 1 + (1|gr(cl)) + (1|gr(cl,t)),
  data = df,
  family = stats::binomial()
)$sim_data()
if (FALSE) {
fit <- mcml_glmer(y ~ factor(t) + int - 1 + (1|cl/t), data = df, family = binomial())
}