# R in Action (2nd ed): Chapter 18
# Advanced methods for missing data
# requires packages VIM, mice
# install.packages(c("VIM", mice))
#----------------------------------

par(ask=TRUE)


# load the dataset
data(sleep, package="VIM")

# list the rows that do not have missing values
sleep[complete.cases(sleep),]

# list the rows that have one or more missing values
sleep[!complete.cases(sleep),]

# tabulate missing values patters
library(mice)
md.pattern(sleep)

# plot missing values patterns
library("VIM")
aggr(sleep, prop=FALSE, numbers=TRUE)
matrixplot(sleep)
marginplot(sleep[c("Gest","Dream")], pch=c(20), col=c("dardgray", "red", "blue"))

# use correlations to explore missing values
x <- sleep
x[is.na(sleep)] <- 1
x[!is.na(sleep)]  <- 0
head(sleep, n=5)
head(x, n=5)
y <- x[which(sd(x) > 0)]
cor(y)
cor(sleep, y, use="pairwise.complete.obs")

# complete case analysis (listwise deletion)

cor(na.omit(sleep))
fit <- lm(Dream ~ Span + Gest, data=na.omit(sleep))
summary(fit)

# multiple imputation
library(mice)
data(sleep, package="VIM")
imp <- mice(sleep, seed=1234)
fit <- with(imp, lm(Dream ~ Span + Gest))
pooled <- pool(fit)
summary(pooled)
imp