data-raw/mixExample.r

In jwb133/mlmi: Maximum Likelihood Multiple Imputation

#simulate a partially observed dataset with a mixture of categorical and continuous variables
set.seed(1234)

n <- 100

#for simplicity we simulate completely independent categorical variables
x1 <- ceiling(3*runif(n))
x2 <- ceiling(2*runif(n))
x3 <- ceiling(2*runif(n))
y <- 1+0.5*(x1==2)+1.5*(x1==3)+x2+x3+rnorm(n)

temp <- data.frame(x1=x1,x2=x2,x3=x3,y=y)

#make some data missing in all variables
for (i in 1:4) {
  temp[(runif(n)<0.25),i] <- NA
}

#impute conditional on MLE, assuming two-way associations in the log-linear model
#and main effects of categorical variables on continuous one (the default)
imps <- mixImp(temp, nCat=3, M=10, pd=FALSE, rseed=4423)