Pool SUEST systems across multiple imputations
suest_mi() combines suest() results fitted in each of several imputed datasets, applying Rubin’s rules to the full set of coefficients and their joint covariance matrix. Supply a list of compatible suest() results, the mice::mira object returned by with() when it fits a SUEST system, or the result list from mitools::with.imputationList().
Usage
suest_mi(fits)
# S3 method for class 'suest_mi'
coef(object, ...)
# S3 method for class 'suest_mi'
vcov(object, ...)
# S3 method for class 'suest_mi'
nobs(object, ...)
# S3 method for class 'suest_mi'
summary(object, conf.level = 0.95, ...)
# S3 method for class 'suest_mi'
print(x, ...)Arguments
- fits
-
A list containing at least two compatible
"suest_model"objects, one per imputation; a"mira"object containing those fits; or a result list returned bymitools::with.imputationList(). - object, x
-
A
"suest_mi"object. - …
-
Additional arguments; currently ignored.
- conf.level
-
Confidence level for
summary.suest_mi().
Value
A "suest_mi" object containing pooled coefficients, total, within-imputation, and between-imputation covariance matrices, Rubin large-sample degrees of freedom, and the original SUEST fits.
Details
The pooled covariance is Ubar + (1 + 1 / m) B, where Ubar is the mean within-imputation joint covariance, B is the between-imputation covariance of the complete joint coefficient vector, and m is the number of imputations. Because each within-imputation input is a complete SUEST system, cross-model covariance is retained in both components.
A "mira" object from mice::with() is read through its public analyses component. The list returned by mice::getfit() is accepted as well. The mice package is not required when pooling an ordinary list.
Results from mitools::with.imputationList() use the same list-based route, with their originating call retained in the returned object. Legacy "imputationResultList" wrappers are accepted as well. The mitools package is not required for an already-created result list.
This initial interface supports coefficients, covariance matrices, summaries, and coefficient-level hypotheses. Predictions, slopes, and comparisons must be estimated within each imputation and pooled as estimands; they are not yet implemented for "suest_mi" objects.