missing_pattern {finalfit} | R Documentation |
finalfit
modelsUsing finalfit
conventions, produces a missing data matrix using
md.pattern
.
missing_pattern( .data, dependent = NULL, explanatory = NULL, rotate.names = TRUE, ... )
.data |
Data frame. Missing values must be coded |
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 |
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.
library(finalfit) library(dplyr) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "mort_5yr" colon_s %>% missing_pattern(dependent, explanatory)