dimdesc: Dimension description

In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

Description

This function is designed to point out the variables and the categories that are the most characteristic according to each dimension obtained by a Factor Analysis.

Usage

dimdesc(res, axes = 1:3, proba = 0.05)

Arguments

res	an object of class PCA, MCA, CA, MFA or HMFA
axes	a vector with the dimensions to describe
proba	the significance threshold considered to characterized the dimension (by default 0.05)

Value

Returns a list including:

quanti	the description of the dimensions by the quantitative variables. The variables are sorted.
quali	the description of the dimensions by the categorical variables

Author(s)

Francois Husson [email protected]

References

Husson, F., Le, S. and Pages, J. (2010). Exploratory Multivariate Analysis by Example Using R, Chapman and Hall.

See Also

PCA, CA, MCA, MFA, HMFA,
Video showing how to use this function

Examples

data(decathlon)
res.pca <- PCA(decathlon, quanti.sup = 11:12, quali.sup=13, graph=FALSE)
dimdesc(res.pca)

Example output

$Dim.1
$Dim.1$quanti
            correlation      p.value
Points        0.9561543 2.099191e-22
Long.jump     0.7418997 2.849886e-08
Shot.put      0.6225026 1.388321e-05
High.jump     0.5719453 9.362285e-05
Discus        0.5524665 1.802220e-04
Rank         -0.6705104 1.616348e-06
400m         -0.6796099 1.028175e-06
110m.hurdle  -0.7462453 2.136962e-08
100m         -0.7747198 2.778467e-09


$Dim.2
$Dim.2$quanti
          correlation      p.value
Discus      0.6063134 2.650745e-05
Shot.put    0.5983033 3.603567e-05
400m        0.5694378 1.020941e-04
1500m       0.4742238 1.734405e-03
High.jump   0.3502936 2.475025e-02
Javeline    0.3169891 4.344974e-02
Long.jump  -0.3454213 2.696969e-02


$Dim.3
$Dim.3$quanti
           correlation      p.value
1500m        0.7821428 1.554450e-09
Pole.vault   0.6917567 5.480172e-07
Javeline    -0.3896554 1.179331e-02

FactoMineR documentation built on May 4, 2018, 5:04 p.m.