GenBinaryY: Generate binary response data from a Marginalized Transition...

In binaryMM: Flexible Marginalized Models for Binary Correlated Outcomes

Generate binary response data from a Marginalized Transition and Latent Variable Model

Description

Generate binary response data from a Marginalized Transition and Latent Variable Model

Usage

GenBinaryY(
  mean.formula,
  lv.formula = NULL,
  t.formula = NULL,
  beta = NULL,
  sigma = NULL,
  gamma = NULL,
  id,
  data,
  q = 10
)

Arguments

mean.formula	Right hand side of mean model formula
lv.formula	Latent variable model formula (right hand side only)
t.formula	Transition model formula (right hand side only)
beta	a vector of values for mean.formula
sigma	a vector of values for the latent variable portion of the association model (else NULL)
gamma	a vector of values for the transition porition of the association model (else NULL)
id	a vector of cluster identifiers (it should be the same length nrow(data))
data	a required data frame
q	a scalar to denote the number of quadrature points used for GH numerical integration

Value

The function returns a binary response vector.

Examples

set.seed(1)
N      = 100
nclust = sample( seq(10,10), N, replace=TRUE)
id     = rep(seq(N), nclust)
Xe     = rep(rbinom(N,size=1,prob=.5), nclust) # binary exposure
time   = unlist( sapply( as.list(nclust), function(ZZ) seq(ZZ)-1 ) )
data   = data.frame(id, time, Xe)
data   = data[order(data$id, data$time),]
newdata = GenBinaryY(mean.formula=~time*Xe, lv.formula=~1, t.formula=~1,
beta=c(-2.5, 0.25, 0.25, 0.1), sigma=1, gamma=1, id=id, data=data, q=20)

binaryMM documentation built on Oct. 12, 2022, 1:06 a.m.