finalfit (version 1.0.8)

missing_pattern: Characterise missing data for finalfit models

Description

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

Usage

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

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.

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.

Examples

library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"

colon_s %>%
	missing_pattern(dependent, explanatory)