simglmm: Simulate Data from Generalized Linear Mixed Models

In rsq: R-Squared and Related Measures

Simulate Data from Generalized Linear Mixed Models

Description

Simulate data from linear and generalized linear mixed models. The coefficients of the two covariate are specified by beta.

Usage

simglmm(family=c("binomial","gaussian","poisson","negative.binomial"),
beta=c(2,0),tau=1,n=200,m=10,balance=TRUE)

Arguments

family	the family of the distribution.
beta	regression coefficients (excluding the intercept which is set as zero).
tau	the variance of the random intercept.
n	the sample size.
m	the number of groups.
balance	simulate balanced data if TRUE, unbalanced data otherwise.

Details

The first covariate takes 1 in half of the observations, and 0 or -1 in the other half. When beta gets larger, it is supposed to easier to predict the response variable.

Value

Returned values include yx, beta, and u.

yx	a data frame including the response y and covariates x1, x2, and so on.
beta	true values of the regression coefficients.
u	the random intercepts.

Author(s)

Dabao Zhang, Department of Statistics, Purdue University

References

Zhang, D. (2020). Coefficients of determination for generalized linear mixed models. Technical Report, 20-01, Department of Statistics, Purdue University.

See Also

rsq, rsq.lmm, rsq.glmm, simglm,

Examples

require(lme4)

# Linear mixed models
gdata <- simglmm(family="gaussian")
lmm1 <- lmer(y~x1+x2+(1|subject),data=gdata$yx)
rsq(lmm1)

# Generalized linear mixed models
bdata <- simglmm(family="binomial",n=400,m=20)
glmm1 <- glmer(y~x1+x2+(1|subject),family="binomial",data=bdata$yx)
rsq(glmm1)

rsq documentation built on Oct. 22, 2023, 5:07 p.m.