# R/demoExample.R In kflorios/AVEmGLMM: Pairwise likelihood methods for multivariate GLMMs

#### Documented in demoExample

```#' This is a Demo Example of the AVEmGLMM package
#'
#' It sets up an estimation problem, and performs the estimation with methods AVE and WAVE
#'@return Returns the demo example output. It runs as a script with no input.
#'@export
demoExample <- function() {

library(MASS)

n <- 100  # sample size
Q <- 4 # number of longitudinal binary outcomes
P <- choose(Q,2) # number of pairs of items
times <- seq(0, 5, length.out = 11)
times <- times - (0+times[length(times)])/2    # center around zero
id <- rep(seq_len(n), each = length(times))
betas <- c(-1, 0.5)
D <- bdiag(rep(list(cbind(c(1, 0.2), c(0.2, 0.25))), Q))
D[D == 0] <- c(0.1)
D[3,1]=0.15
D[5,1]=0.15
D[7,1]=0.15
D[5,3]=0.15
D[7,3]=0.15
D[7,5]=0.15

D[1,3]=0.15
D[1,5]=0.15
D[1,7]=0.15
D[3,5]=0.15
D[3,7]=0.15
D[5,7]=0.15

X <- cbind(1, rep(times, n))
Z <- cbind(1, rep(times, n))
ncz <- ncol(Z)

m <- 1
set.seed(1000 + m)

b <- mvrnorm(n, rep(0, Q*2), D)

Data <- generateData(id,times,n,X,Z,betas,b,Q)

modelFit <- estimateModelFit(Data,Q,n)

betasN=c(-1,0.5,1,0.25,0.2,  -1,0.5,1,0.25,0.2, -1,0.5,1,0.25,0.2, -1,0.5,1,0.25,0.2)

modelFitAndTarget <- as.data.frame(cbind("True" = betasN, modelFit))

return(modelFitAndTarget)

}
```
kflorios/AVEmGLMM documentation built on May 4, 2019, 7:42 p.m.