mar {missr} | R Documentation |
Missing at random (MAR) test
Description
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
missing |
Column of M with missing data |
p_value |
Smallest p-value of the logistic regressions |
explanatory |
Variable corresponding to |
p_values |
The p-values of the logistic regressions |
variables |
Variables corresponding to |
combined |
Paired |
Examples
mar(healthcheck)