summary.miceFast_pool

Displays the full pooling diagnostics including within- and
 between-imputation variance, relative increase in variance, proportion of
 variance due to missingness, fraction of missing information, and
 confidence intervals.

Fast imputations under the object-oriented programming paradigm.
Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
The biggest improvement in time performance can be achieved for a calculation where a grouping variable is used.
A single evaluation of a quantitative model for the multiple imputations is another major enhancement.
A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.

Maciej Nasinski

miceFast

Fast Imputations Using 'Rcpp' and 'Armadillo'

summary.miceFast_pool function

<dl><dt>object</dt>
<dd>an object of class <code>"miceFast_pool"</code>, as returned by <code>pool</code>.</dd>
<dt>...</dt>
<dd>additional arguments (currently ignored).</dd></dl>

Arguments

Summary method for pooled MI results — summary.miceFast_pool

<dl>

<dt>object</dt>
<dd>an object of class <code>"miceFast_pool"</code>, as returned by <code>pool</code>.</dd>


<dt>...</dt>
<dd>additional arguments (currently ignored).</dd>

</dl>

summary.miceFast_pool: Summary method for pooled MI results

Description

Usage

Value

Arguments