mice (version 3.16.0)

pattern: 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

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 <- lattice::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 = lattice::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)