R: Datasets with various missing data patterns

pattern {mice} | R Documentation

## 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

Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.

### Details

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

### 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)

[Package mice version 3.8.0 Index]