Using finalfit conventions, produces a missing data matrix using md.pattern.

missing_pattern(.data, dependent = NULL, explanatory = NULL,
  rotate.names = TRUE, ...)

Arguments

.data

Data frame. Missing values must be coded NA.

dependent

Character vector usually of length 1, name of depdendent variable.

explanatory

Character vector of any length: name(s) of explanatory variables.

rotate.names

Logical. Should the orientation of variable names on plot should be vertical.

...

pass other arguments such as plot = TRUE to md.pattern.

Value

A matrix with ncol(x)+1 columns, in which each row corresponds to a missing data pattern (1=observed, 0=missing). Rows and columns are sorted in increasing amounts of missing information. The last column and row contain row and column counts, respectively.

Examples

library(finalfit) library(dplyr) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "mort_5yr" colon_s %>% missing_pattern(dependent, explanatory)
#> sex.factor perfor.factor age.factor mort_5yr obstruct.factor #> 894 1 1 1 1 1 0 #> 21 1 1 1 1 0 1 #> 14 1 1 1 0 1 1 #> 0 0 0 14 21 35