Characterise missing data for finalfit models
Using 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.