library(mitools)
library(mice)
library(Amelia)
library(jomo)
#############################################################################
# EXAMPLE 1: data.graham.8a | Imputation under multivariate normal model
#############################################################################
data(data.graham.ex8a)
dat <- data.graham.ex8a
dat <- dat[,1:10]
vars <- colnames(dat)
V <- length(vars)
# remove persons with completely missing data
dat <- dat[ rowMeans( is.na(dat) ) < 1 , ]
summary(dat)
# some descriptive statistics
psych::describe(dat)
#**************
# imputation under a multivariate normal model
M <- 7 # number of imputations
#--------- mice package
# define imputation method
impM <- rep("norm" , V)
names(impM) <- vars
# mice imputation
imp1a <- mice::mice( dat , imputationMethod=impM , m=M , maxit=4 )
summary(imp1a)
# convert into a list of datasets
datlist1a <- miceadds::mids2datlist(imp1a)
#--------- Amelia package
imp1b <- Amelia::amelia( dat , m=M )
summary(imp1b)
datlist1b <- imp1b$imputations
#--------- jomo package
imp1c <- jomo::jomo1con(Y = dat , nburn=100, nbetween=10, nimp=M)
str(imp1c)
# convert into a list of datasets
datlist1c <- miceadds::jomo2datlist(imp1c)
#############################################################################
# EXAMPLE 2: data.graham.8b | Imputation with categorical variables
#############################################################################
data(data.graham.ex8b)
dat <- data.graham.ex8b
vars <- colnames(dat)
V <- length(vars)
# descriptive statistics
psych::describe(dat)
#*******************************
# imputation in mice using predictive mean matching
imp1a <- mice( dat , m=5 , maxit=10)
datlist1a <- mitools::imputationList( miceadds::mids2datlist(imp1a) )
print(datlist1a)
#*******************************
# imputation in jomo treating all variables as categorical
# Note that variables must have values from 1 to N
# use categorize function from sirt package here
dat.categ <- sirt::categorize( dat , categorical=colnames(dat) , lowest=1 )
dat0 <- dat.categ$data
# imputation in jomo treating all variables as categorical
Y_numcat <- apply( dat0 , 2 , max , na.rm=TRUE )
imp1b <- jomo::jomo1cat(Y_cat = dat0, Y_numcat = Y_numcat, nburn=100,
nbetween=10, nimp=5)
# recode original categories
datlist1b <- sirt::decategorize( imp1b , categ_design = dat.categ$categ_design )
# convert into a list of datasets
datlist1b <- miceadds::jomo2datlist(datlist1b)
datlist1b <- mitools::imputationList( datlist1b )
#****************************
# compare frequency tables for both imputation packages
fun_prop <- function( variable ){
t1 <- table(variable)
t1 / sum(t1)
}
# variable rskreb71
res1a <- with( datlist1a , fun_prop(rskreb71) )
res1b <- with( datlist1b , fun_prop(rskreb71) )
summary( miceadds::NMIcombine(qhat = res1a , NMI = FALSE ) )
summary( miceadds::NMIcombine(qhat = res1b , NMI = FALSE ) )
# variable posatt
res2a <- with( datlist1a , fun_prop(posatt) )
res2b <- with( datlist1b , fun_prop(posatt) )
summary( miceadds::NMIcombine(qhat = res2a , NMI = FALSE ) )
summary( miceadds::NMIcombine(qhat = res2b , NMI = FALSE ) )Run the code above in your browser using DataLab