simule

dplot

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

simule function

<dl> <dt>data</dt>
<dd>A data frame from which the rows are the original data from which the simualte data are calculated (by the average
 of a bootstrap sample. The columns corresponds to the variables for which the simulation should be done. The first column must
 be a factor allowing to group the rows. A bootstrap simulation is done for each level of this factor.</dd> <dt>nb.simul</dt>
<dd>The number of simulations.</dd></dl>

Arguments

Author

Simulate by bootstrap — simule

<dl>

 <dt>data</dt>
<dd>A data frame from which the rows are the original data from which the simualte data are calculated (by the average
 of a bootstrap sample. The columns corresponds to the variables for which the simulation should be done. The first column must
 be a factor allowing to group the rows. A bootstrap simulation is done for each level of this factor.</dd>

 <dt>nb.simul</dt>
<dd>The number of simulations.</dd>

</dl>

simule: Simulate by bootstrap

Description

Usage

Value

Arguments

Author

Details