pattern {mice} | R Documentation |
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 (2012) uses these four artificial datasets to illustrate various missing data patterns.
van Buuren, S. (2012). Flexible Imputation of Missing Data. Boca Raton, FL: Chapman & Hall/CRC Press.
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)