diagMAAR.dtmm: Directly testing a postulated missingness mechanism.
In bojinov/diagMAAR: Diagnostic tests for missing always at random

Description Usage Arguments Details Value See Also Examples

diagMAAR.dtmm takes a preprocessed data matrix test the MAAR assumption using a logistic regression as the response propensity model.

1	diagMAAR.dtmm(prep, alpha = 0.05, verbose = FALSE)

`prep`	A preprocessed S3 class that contains the data that is going to be tested.
`alpha`	A numeric value indicating the level of the test. The default is set to 0.05.

diagMAAR.dtmm is part of the diagnostic tools functions used for diagnosing for the MAAR assumption. This function tests if the response propensity in one variable depends on partially observed outcome variables. To perform the likelihood ratio test, the function first imputes the missing values - using default mice settings.

Note: In simulation studies this test has low power for sample sizes < 100.

diagMAAR A S3 object that contains: reject, a logical indicating if the test rejected; res, the results from the likelihood ratio test; which.reject, a vector indicating which variables were reject; method, a string indicating the diagnostic method used.

Other diagnostic: diagMAAR.ccm, diagMAAR.cop, diagMAAR

# Generate 100 iid samples from a MVN with correlation equal to 0.3
samples.mvn <- sample_mvn(5, 0.3, 100)
# Take the Gaussian data and and delete some values from the fourth row.
obs.nvm <- MAAR_mechanism(samples = samples.mvn, miss.coef = 0.2, 
                           miss.nvar = 1, miss.var = 4, 
                           prob.coef = matrix(c(-1, 0.5, 0.7, - 0.2), 1, 4))
Y.dtmm <- prep.dtmm(obs.mvn)
diagMAAR.dtmm(Y.dtmm)