mi.inference: Multiple imputation inference

In norm: Analysis of Multivariate Normal Datasets with Missing Values

Multiple imputation inference

Description

Combines estimates and standard errors from m complete-data analyses performed on m imputed datasets to produce a single inference. Uses the technique described by Rubin (1987) for multiple imputation inference for a scalar estimand.

Usage

mi.inference(est, std.err, confidence=0.95)

Arguments

est	a list of $m$ (at least 2) vectors representing estimates (e.g., vectors of estimated regression coefficients) from complete-data analyses performed on $m$ imputed datasets.
std.err	a list of $m$ vectors containing standard errors from the complete-data analyses corresponding to the estimates in est.
confidence	desired coverage of interval estimates.

Value

a list with the following components, each of which is a vector of the same length as the components of est and std.err:

est	the average of the complete-data estimates.
std.err	standard errors incorporating both the between and the within-imputation uncertainty (the square root of the "total variance").
df	degrees of freedom associated with the t reference distribution used for interval estimates.
signif	P-values for the two-tailed hypothesis tests that the estimated quantities are equal to zero.
lower	lower limits of the (100*confidence)% interval estimates.
upper	upper limits of the (100*confidence)% interval estimates.
r	estimated relative increases in variance due to nonresponse.
fminf	estimated fractions of missing information.

METHOD

Uses the method described on pp. 76-77 of Rubin (1987) for combining the complete-data estimates from $m$ imputed datasets for a scalar estimand. Significance levels and interval estimates are approximately valid for each one-dimensional estimand, not for all of them jointly.

References

See Rubin (1987) or Schafer (1996), Chapter 4.

norm documentation built on July 9, 2023, 7:34 p.m.