missing_pattern: Characterise missing data for 'finalfit' models

In finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

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,
  ...
)

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)

finalfit documentation built on Nov. 17, 2023, 1:09 a.m.