bestVars

The number of best variables is estimated by finding an 
 apparent inflection point in the relationship between the 
 generalized root mean square distance (see <code>grmsd</code> and the number
 of X-variables.

multivariate

Performs nearest neighbor-based imputation using one or more alternative
approaches to processing multivariate data. These include methods based on canonical
correlation: analysis, canonical correspondence analysis, and a multivariate adaptation
of the random forest classification and regression techniques of Leo Breiman and Adele
Cutler. Additional methods are also offered. The package includes functions for
comparing the results from running alternative techniques, detecting imputation targets
that are notably distant from reference observations, detecting and correcting
for bias, bootstrapping and building ensemble imputations, and mapping results.

Jeffrey S Evans

yaImpute

Nearest Neighbor Observation Imputation and Evaluation Tools

Jeffrey S. Evans

Nicholas L. Crookston

Andrew O. Finley

John Coulston (Sunil Arya and David Mount for ANN)

bestVars function

<dl> <dt>obj</dt>
<dd>an object create by <code>varSelection</code></dd> <dt>nbest</dt>
<dd>number of variables designated as the best;
 if null the number is estimated</dd></dl>

Arguments

Nicholas L. Crookston <a href="/link/ncrookston.fs%40gmail.com?package=yaImpute&version=1.0-34.1" data-mini-rdoc="yaImpute::ncrookston.fs@gmail.com">ncrookston.fs@gmail.com</a>

Author

Computes the number of best X-variables — bestVars

<dl>

 <dt>obj</dt>
<dd>an object create by <code>varSelection</code></dd>

 <dt>nbest</dt>
<dd>number of variables designated as the best;
 if null the number is estimated</dd>

</dl>

Nicholas L. Crookston <a href='mailto:ncrookston.fs@gmail.com'>ncrookston.fs@gmail.com</a>

bestVars: Computes the number of best X-variables

Description

Usage

Value

Arguments

Author

See Also

Examples