data.graham {miceadds}R Documentation

Datasets from Grahams Missing Data Book

Description

Datasets from Grahams missing data book (2012).

Usage

data(data.graham.ex3)
data(data.graham.ex6)
data(data.graham.ex8a)
data(data.graham.ex8b)
data(data.graham.ex8c)

Format

Source

The datasets were downloaded from http://methodology.psu.edu/pubs/books/missing.

References

Graham, J. W. (2012). Missing data. New York: Springer. doi:10.1007/978-1-4614-4018-5

Examples

## Not run: 
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, method=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)

# alternatively one can use the jomo wrapper function
imp1c1 <- jomo::jomo(Y=dat, nburn=100, nbetween=10, nimp=M)

#############################################################################
# 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::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 )

# Alternatively, jomo can be used but categorical variables must be
# declared as factors
dat <- dat0
# define two variables as factors
vars <- miceadds::scan.vec(" rskreb71 rskreb72")
for (vv in vars){
    dat[, vv] <- as.factor( dat[,vv] )
          }
# use jomo
imp1b1 <- jomo::jomo(Y=dat, nburn=30, nbetween=10, nimp=5)

#****************************
# 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 ) )

## End(Not run)
[Package miceadds version 3.17-44 Index]