diagMAAR: MAAR diagnostics
In bojinov/diagMAAR: Diagnostic tests for missing always at random

Description Usage Arguments Details Value See Also Examples

diagMAAR takes a preprocessed data matrix test the MAAR assumption.

1	diagMAAR(dt, daigtest = NULL, alpha = 0.05, ..., seed = NULL)

`dt`	A data.frame with missing values, a matrix with missing values, a mids class (the output from mice), or a prep class (the output from) prep.
`daigtest`	The test to be implemented - pick from Gaussian copula test (cop), directly testing a postulated missingness mechanism (dtmm), and comparison of conditional means. Leave blank if dt is of class prep.
`alpha`	A numeric value indicating the level of the test. The default is set to 0.05.
`...`	Other arguments to be passed on to the specific diagnostic method
`seed`	A numeric value used to set a seed, leave blank for NULL.

diagMAAR implements three different diagnostic test for the missing always at random assumption: Gaussian copula test (cop), directly testing a postulated missingness mechanism (dtmm), and comparison of conditional means (ccm). Based on simulation studies the dtmm method should be avoided for small sample sized (< 100). For more details about each of the different diagnostic test see diagMAAR.test_name.

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.dtmm

# 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))
diagMAAR.dtmm(obs.mvn, "ccm")