neibo

this function using pre-sorting of a y and the binary search the one of the k closest value for each miss is returned.

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 could be achieve for a calculation where a grouping variable have to be 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 look up</dd>
<dt>miss</dt>
<dd>numeric vector a values to be look for</dd>
<dt>k</dt>
<dd>integer a number of values which should be taken into account during sampling one of the k closest point</dd></dl>

Arguments

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

<dl>

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


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


<dt>k</dt>
<dd>integer a number of values which should be taken into account during sampling one of the k closest point</dd>

</dl>

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

Description

Usage

Value

Arguments