neibo

This function uses pre-sorting of y and binary search to find
 one of the k closest values for each miss.

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'

neibo function

<dl><dt>y</dt>
<dd>numeric vector values to be looked up</dd>
<dt>miss</dt>
<dd>numeric vector values to be looked for</dd>
<dt>k</dt>
<dd>integer number of nearest neighbours to sample from</dd></dl>

Arguments

Finding in random manner one of the k closest points in a certain vector
for each value in a second vector — neibo

<dl>

<dt>y</dt>
<dd>numeric vector values to be looked up</dd>


<dt>miss</dt>
<dd>numeric vector values to be looked for</dd>


<dt>k</dt>
<dd>integer number of nearest neighbours to sample from</dd>

</dl>

neibo: Finding in random manner one of the k closest points in a certain vector for each value in a second vector

Description

Usage

Value

Arguments