Datasets with various missing data patterns

Source: R/pattern1.R

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

#>     A  B  C
#> 25 26 88 32
#> 26 42 66 21
#> 27 86 54 NA
#> 28  9 92 NA
#> 29 20 83 NA
#> 30 89 NA 41
#> 31 NA NA 35
#> 32 NA NA 33

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)

#>   A B C  
#> 2 1 1 1 0
#> 3 1 1 0 1
#> 1 1 0 1 1
#> 2 0 0 1 2
#>   2 3 3 8
p <- md.pairs(pattern4)
p

#> $rr
#>   A B C
#> A 6 5 3
#> B 5 5 2
#> C 3 2 5
#> 
#> $rm
#>   A B C
#> A 0 1 3
#> B 0 0 3
#> C 2 3 0
#> 
#> $mr
#>   A B C
#> A 0 0 2
#> B 1 0 3
#> C 3 3 0
#> 
#> $mm
#>   A B C
#> A 2 2 0
#> B 2 3 0
#> C 0 0 3
#> 

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

#>           A B C
#> A 0.0000000 0 1
#> B 0.3333333 0 1
#> C 1.0000000 1 0

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

#>     A         B   C
#> A 0.0 0.1666667 0.5
#> B 0.0 0.0000000 0.6
#> C 0.4 0.6000000 0.0


fluxplot(pattern2)