produce_NA

Produces a dataframe with missing values.
For each variable, a random sample of observations of size <code>proportion * nrow(x)</code> will be set to NA.

Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>)
with adaptations for prediction settings. The function missForest() is used
to impute a (training) dataset with missing values and to learn imputation
models that can be later used for imputing new observations.
The function missForestPredict() is used to impute one or multiple new
observations (test set) using the models learned on the training data.

Elena Albu

missForestPredict

Missing Value Imputation using Random Forest for Prediction
Settings

produce_NA function

<dl><dt>x</dt>
<dd>a dataframe</dd>
<dt>proportion</dt>
<dd>proportion of missing values to be produced; a vector of size <code>ncol(x)</code>
or a single value to be applied to all variables in the dataframe.</dd></dl>

Arguments

Produces a dataframe with missing values — produce_NA

<dl>

<dt>x</dt>
<dd>a dataframe</dd>


<dt>proportion</dt>
<dd>proportion of missing values to be produced; a vector of size <code>ncol(x)</code>
or a single value to be applied to all variables in the dataframe.</dd>

</dl>

produce_NA: Produces a dataframe with missing values

Description

Usage

Value

Arguments