prodNA

dataframe subjected to missing value introduction.

proportion of missing values w.r.t. the number of entries
 of 'x'.

noNA

'prodNA' artificially introduces missing values. Entries in the given
dataframe are deleted completely at random up to the specified amount.

 classes 

The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation. It can be run in parallel to
save computation time.

Daniel J Stekhoven

missForest

Nonparametric Missing Value Imputation using Random Forest

prodNA function

<dl> <dt>x</dt>
<dd>dataframe subjected to missing value introduction.</dd><dt>noNA</dt>
<dd>proportion of missing values w.r.t. the number of entries
 of 'x'.</dd></dl>

Arguments

Daniel J. Stekhoven, &lt;stekhoven@stat.math.ethz.ch&gt;

Author

Introduce Missing Values Completely at Random — prodNA

<dl>

 <dt>x</dt>
<dd>dataframe subjected to missing value introduction.</dd>

<dt>noNA</dt>
<dd>proportion of missing values w.r.t. the number of entries
 of 'x'.</dd>

</dl>

prodNA: Introduce Missing Values Completely at Random

Description

Usage

Value

Arguments

Author

See Also

Examples