mar {missr}R Documentation

Missing at random (MAR) test

Description

[Stable] mar() performs multiple logistic regressions to test for MAR. The null hypothesis for each is that the data are not MAR.

Usage

mar(data, debug = FALSE)

Arguments

data

A data frame.

debug

A logical value used only for unit testing.

Details

In the following, each column of M with missing data is regressed on D_obs. Each regression produces a vector of p-values (one for each variable in D_obs). The smallest p-value is the most important. This is because missing data need only be dependent on one observed variable for the data to be MAR. If each reported smallest p-value is significant, the data is MAR. See vignette("background") for definitions of M and D_obs.

Value

A tibble::tibble():

missing

Column of M with missing data

p_value

Smallest p-value of the logistic regressions

explanatory

Variable corresponding to p_value

p_values

The p-values of the logistic regressions

variables

Variables corresponding to p_values

combined

Paired p_values and variables for easier interpretation

Examples

mar(healthcheck)

[Package missr version 1.0.1 Index]