Datasets with various missing data patterns
Four simple datasets with various missing data patterns
Data with a univariate missing data pattern
Data with a monotone missing data pattern
Data with a file matching missing data pattern
Data with a general missing data pattern
Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.
Van Buuren (2012) uses these four artificial datasets to illustrate various missing data patterns.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.