StackImpute (version 0.1.0)

Bootstrap_Variance: Bootstrap_Variance

Description

This function takes a dataset with stacked multiple imputation and a model fit and applies bootstrap to estimate the covariance matrix accounting for imputation uncertainty.

Usage

Bootstrap_Variance(fit, stack, M, n_boot = 100)

Arguments

fit

object with corresponding vcov method (e.g. glm, coxph, survreg, etc.) from fitting to the (weighted) stacked dataset

stack

data frame containing stacked dataset across multiple imputations. Could have 1 or M rows for each subject with complete data. Should have M rows for each subject with imputed data. Must contain the following named columns: (1) stack$.id, which correspond to a unique identifier for each subject. This column can be easily output from MICE. (2) stack$wt, which corresponds to weights assigned to each row. Standard analysis of stacked multiple imputations should set these weights to 1 over the number of times the subject appears in the stack. (3) stack$.imp, which indicates the multiply imputed dataset (from 1 to M). This column can be easily output from MICE.

M

number of multiple imputations

n_boot

number of bootstrap samples

Value

Variance, estimated covariance matrix accounting for within and between imputation variation

Details

This function implements the bootstrap-based estimation method for stacked multiple imputations proposed by Dr. Paul Bernhardt in ``A Comparison of Stacked and Pooled Multiple Imputation" at the Joint Statistical Meetings, 2019.

Examples

# NOT RUN {
data(stackExample)

fit = stackExample$fit
stack = stackExample$stack

bootcovar = Bootstrap_Variance(fit, stack, M = 5, n_boot = 10)
VARIANCE_boot = diag(bootcovar)

# }