Description Usage Arguments Details Value Examples

View source: R/Jackknife_Variance.R

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.

1 | ```
Jackknife_Variance(fit, stack, M)
``` |

`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 |

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

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

1 2 3 4 5 6 7 | ```
data(stackExample)
fit = stackExample$fit
stack = stackExample$stack
jackcovar = Jackknife_Variance(fit, stack, M = 5)
VARIANCE_jack = diag(jackcovar)
``` |

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