| Jackknife_Variance {StackImpute} | R Documentation |
Jackknife_Variance
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)