tab.disjonctif.prop

Create a disjunctive table. The missing values are replaced by the proportion of the category.

algebra

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Francois Husson

FactoMineR

Multivariate Exploratory Data Analysis and Data Mining

tab.disjonctif.prop function

<dl> <dt>tab</dt>
<dd>a data frame with factors</dd> <dt>row.w</dt>
<dd>an optional row weights (by default, a vector of 1 for uniform row weights)</dd> <dt>seed</dt>
<dd>a single value, interpreted as an integer for the set.seed function (if seed = NULL, missing values are initially imputed by the mean of each variable)</dd></dl>

Arguments

Make a disjunctive table when missing values are present — tab.disjonctif.prop

<dl>

 <dt>tab</dt>
<dd>a data frame with factors</dd>

 <dt>row.w</dt>
<dd>an optional row weights (by default, a vector of 1 for uniform row weights)</dd>

 <dt>seed</dt>
<dd>a single value, interpreted as an integer for the set.seed function (if seed = NULL, missing values are initially imputed by the mean of each variable)</dd>

</dl>

tab.disjonctif.prop: Make a disjunctive table when missing values are present

Description

Usage

Value

Arguments