sim.data.fun: Simulate Data from logistic mixed model or poisson-normal...

In msim: This package implements the method of simulated moments for the GLMM logit mixed model as described in Jiang's (1998) article, Consistent Estimators in Generalized Linear Mixed Models.

Description

This function will simulate the data for the logistic mixed model: logit(y_ij)=mu+sigma* theta_i or the poisson-normal model: log(y_ij)=mu+sigma* theta_i where theta_i is N(0,1) in either case.

Usage

sim.data.fun(num_subs = NULL, obs_per_sub = NULL, true.mu = NULL, true.sigma = NULL,family="binomial", set.seed = NULL)

Arguments

num_subs	Index of i (number of subjects); default is NULL
obs_per_sub	Vector of length num_subs; Index of j (number of observations per subject); default is NULL
true.mu	True value of mu; default is NULL
true.sigma	True value of sigma; default is NULL
family	Exponential family from which to generate top level of hierarchy; default is "binomial"
set.seed	Random seed start value for reproducibility; default is NULL

Details

The user must enter values for num_subs, obs_per_sub, true.mu and true.sigma or the function will produce an error. The set.seed argument is optional.

Value

y	a matrix of generated y_ij of dimension num_subs* max(obs_per_sub)
y.i	a vector of the sums over j of y_ij; this will have length num_subs
n.i	a vector of the number of observations for each subject; this should exactly match obs_per_sub

Author(s)

Lindsey Dietz

msim documentation built on May 2, 2019, 5:50 p.m.