initialise

Rough estimation of missing values in a vector according to its type.

manip

New tools for the visualization of missing and/or imputed values
are introduced, which can be used for exploring the data and the structure of
the missing and/or imputed values. Depending on this structure of the missing
values, the corresponding methods may help to identify the mechanism generating
the missing values and allows to explore the data including missing values.
In addition, the quality of imputation can be visually explored using various
univariate, bivariate, multiple and multivariate plot methods. A graphical user
interface available in the separate package VIMGUI allows an easy handling of
the implemented plot methods.

Matthias Templ

Visualization and Imputation of Missing Values

Alexander Kowarik

Andreas Alfons

Gregor de Cillia

Bernd Prantner

Wolfgang Rannetbauer

initialise function

<dl><dt>x</dt>
<dd>a vector.</dd>
<dt>mixed</dt>
<dd>a character vector containing the names of variables of type
mixed (semi-continous).</dd>
<dt>method</dt>
<dd>Method used for Initialization (median or kNN)</dd>
<dt>mixed.constant</dt>
<dd>vector with length equal to the number of
semi-continuous variables specifying the point of the semi-continuous
distribution with non-zero probability</dd></dl>

Arguments

Matthias Templ, modifications by Andreas Alfons

Author

Initialization of missing values — initialise

<dl>

<dt>x</dt>
<dd>a vector.</dd>


<dt>mixed</dt>
<dd>a character vector containing the names of variables of type
mixed (semi-continous).</dd>


<dt>method</dt>
<dd>Method used for Initialization (median or kNN)</dd>


<dt>mixed.constant</dt>
<dd>vector with length equal to the number of
semi-continuous variables specifying the point of the semi-continuous
distribution with non-zero probability</dd>

</dl>

initialise: Initialization of missing values

Description

Usage

Value

Arguments

Author

Details