optimize_glmm

the approximated loglikelihood function

lfun

p_beta

p_theta

a <code>glmmFit</code> object, the result of a previous model fit.

prev_fit

controls how much detail to print out while fitting the model.
For verbose = 0, print nothing. For verbose = 1 (the default), print
output approximately once a second during model fitting. For verbose = 2,
print out the parameter value and log-likelihood at every stage of
optimization.

verbose

Find the approximate maximum likelihood estimate, and estimate the variance
of the estimate

internal

Conduct inference about generalized linear mixed models, with a
choice about which method to use to approximate the likelihood. In addition
to the Laplace and adaptive Gaussian quadrature approximations, which are
borrowed from 'lme4', the likelihood may be approximated by the sequential
reduction approximation, or an importance sampling approximation. These
methods provide an accurate approximation to the likelihood in some
situations where it is not possible to use adaptive Gaussian quadrature.

optimize_glmm: Maximize the approximated log-likelihood

Description

Usage

Arguments

Value