experiment_missing_data.frame {mi}

Class "experiment_missing_data.frame"

Description

This class inherits from the missing_data.frame-class but is customized for the situation where the sample is a randomized experiment.

Details

The fit_model-methods for the experiment_missing_data.frame class take into account the special nature of a randomized experiment. At the moment, the treatment variable must be binary and fully observed.

Objects from the Class

Objects can be created by calls of the form new("experiment_missing_data.frame", ...). However, its users almost always will pass a data.frame to the missing_data.frame function and specify the subclass and concept arguments.

Slots

The experiment_missing_data.frame class inherits from the missing_data.frame-class and has two additional slots

concept

Object of class factor whose length is equal to the number of variables and whose levels are "treatment", "covariate" and "outcome"

case

Object of class character of length one, indicating whether the missingness is in the outcomes only, in the covariates only, or in both the outcomes and covariates. This slot is filled automatically by the initialize method

Author(s)

Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman.

See Also

missing_data.frame

Examples

rdf <- rdata.frame(n_full = 2, n_partial = 2, 
                   restrictions = "stratified", experiment = TRUE,
                   types = c("t", "ord", "con", "pos"),
                   treatment_cor = c(0, 0, NA, 0, NA))
Sigma <- tcrossprod(rdf$L)
rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2",
                                        "missing_y_1", "missing_Y_2")
print(round(Sigma, 3))

concept <- as.factor(c("treatment", "covariate", "covariate", "outcome"))
mdf <- missing_data.frame(rdf$obs, subclass = "experiment", concept = concept)