Datasets with various missing data patterns

Description

Four simple datasets with various missing data patterns

Format

list("pattern1")

Data with a univariate missing data pattern

list("pattern2")

Data with a monotone missing data pattern

list("pattern3")

Data with a file matching missing data pattern

list("pattern4")

Data with a general missing data pattern

Details

Van Buuren (2012) uses these four artificial datasets to illustrate various missing data patterns.

Source

van Buuren, S. (2012). Flexible Imputation of Missing Data. Boca Raton, FL: Chapman & Hall/CRC Press.

Examples

require(lattice)
require(MASS)

pattern4

data <- rbind(pattern1, pattern2, pattern3, pattern4)
mdpat <- cbind(expand.grid(rec = 8:1, pat = 1:4, var = 1:3), r=as.numeric(as.vector(is.na(data))))

types <-  c("Univariate","Monotone","File matching","General")
tp41 <- levelplot(r~var+rec|as.factor(pat), data=mdpat,
         as.table=TRUE, aspect="iso",
         shrink=c(0.9),
         col.regions = mdc(1:2),
         colorkey=FALSE,
         scales=list(draw=FALSE),
         xlab="", ylab="",
         between = list(x=1,y=0),
         strip = strip.custom(bg = "grey95", style = 1,
           factor.levels = types))
print(tp41)

md.pattern(pattern4)
p <- md.pairs(pattern4)
p

### proportion of usable cases
p$mr/(p$mr+p$mm)

### outbound statistics
p$rm/(p$rm+p$rr)


fluxplot(pattern2)

Results