Jackknife_Variance: Jackknife_Variance

In StackImpute: Tools for Analysis of Stacked Multiple Imputations

Description

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

Usage

Jackknife_Variance(fit, stack, M)

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

Details

This function implements the jackknife-based estimation method for stacked multiple imputations proposed by Beesley and Taylor (2021).

Value

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

Examples

data(stackExample)

fit = stackExample$fit
stack = stackExample$stack

jackcovar = Jackknife_Variance(fit, stack, M = 5)
VARIANCE_jack = diag(jackcovar)

StackImpute documentation built on Sept. 10, 2021, 5:07 p.m.