data.mb {mdmb}R Documentation

Example Datasets for mdmb Package

Description

Example datasets for mdmb package.

Usage

data(data.mb01)
data(data.mb02)
data(data.mb03)
data(data.mb04)
data(data.mb05)

Format

References

Enders, C. K., Baraldi, A. N., & Cham, H. (2014). Estimating interaction effects with incomplete predictor variables. Psychological Methods, 19(1), 39-55. doi:10.1037/a0035314

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Linear interaction example from Enders et al. (2014)
#############################################################################

# load packages
library(mdmb)
library(mice)
library(mitools)
library(sandwich)

#--- attach example dataset (Enders et al., 2014) from mdmb package
data( data.mb03, package="mdmb")
dat <- data.mb03

#--- center data which speeds convergence of Bayesian estimation
#--- of the imputation model
for (vv in 1:3){
    M_vv <- mean( dat[,vv], na.rm=TRUE )
    dat[,vv] <- dat[,vv] - M_vv
}

#--- generate initial imputed values withj mice package
imp <- mice::mice( dat, m=, maxit=20 )
data_init <- mice::complete(imp, action=1)

#--- define number of iterations and number of imputed datasets
iter <- 50000; burnin <- 5000
Nimp <- 100

#******* imputation model M3 with quadratic effects

# model for dependent variable
dep <- list("model"="linreg", "formula"=y ~ x*z + I(x^2) + I(z^2) )
# covariate models
ind_x <- list( "model"="linreg", "formula"=x ~ z + I(z^2) )
ind_z <- list( "model"="linreg", "formula"=z ~ 1 )
ind <- list( x=ind_x, z=ind_z)

#generate imputations
imp <- mdmb::frm_fb(dat=dat, dep=dep, ind=ind, burnin=burnin, iter=iter,
              data_init=data_init, Nimp=Nimp)

#--- create list of multiply imputed datasets
datlist <- mdmb::frm2datlist(imp)

#-------------------------------
#--- analyze imputed datasets with mice package

# convert into object of class mids
imp2 <- miceadds::datlist2mids(datlist)
# estimate linear model on multiply imputed datasets
mod1 <- with(imp2, stats::lm( y ~ x*z ) )
summary( mice::pool(mod1) )

#-------------------------------
#--- analyze imputed datasets using sandwich standard errors

results <- list()
variances <- list()
Nimp <- length(datlist)
for (ii in 1:Nimp){
    mod_ii <- stats::lm( y ~ x*z, data=datlist[[ii]] )
    variances[[ii]] <- sandwich::vcovHC(mod_ii)
    results[[ii]] <- coef(mod_ii)
}

mod2 <- mitools::MIcombine(results=results,variances=variances,df.complete=69)
summary(mod2)

## End(Not run)
[Package mdmb version 1.9-22 Index]