dimdesc: Dimension description
In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/dimdesc.r

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.

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

`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)

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

Francois Husson [email protected]

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

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

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

$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