R: Class "experiment_missing_data.frame"

experiment_missing_data.frame {mi} | R Documentation

## 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)

[Package mi version 1.0 Index]