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 3.12.0 Index]