Scores of the fpcad function vs. moments of the densities

Description

Applies to an object of class fpcad, plots the principal scores vs. the moments of the densities (means, standard deviations, variances, correlations, skewness and kurtosis coefficients), and computes the correlations between these scores and moments.

Usage

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interpret.fpcad(x, nscore = 1:3, moment = "mean")

Arguments

x

object of class fpcad (returned by the fpcad function).

nscore

numeric vector. Selects the columns of the data frame x$scores to be interpreted.

Warning: Its components cannot be greater than the nb.factors argument in the call of the fpcad function.

moment

characters string. Selects the moments to cross with scores:

  • "mean" (means)

  • "sd" (standard deviations)

  • "cov" (covariances)

  • "cor" (correlation coefficients)

  • "skewness" (skewness coefficients)

  • "kurtosis" (kurtosis coefficients)

  • "all" (for univariate densities only. It simultaneously considers means, standard deviations, variances and skewness and kurtosis coefficients)

Details

A new graphics device is opened for each score column. A device can contain up to 9 graphs. If there are too many (more than 36) graphs for each score, a multipage PDF file is created in the current working directory, and the graphs are displayed in it.

The number of principal scores to be interpreted cannot be greater than nb.factors of the data frame x$scores returned by the function fpcad.

Value

Returns a list including:

pearson

matrix of Pearson correlations between selected scores and moments.

spearman

matrix of Spearman correlations between selected scores and moments.

Author(s)

Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard.

References

Boumaza, R., Yousfi, S., Demotes-Mainard, S. (2015). Interpreting the principal component analysis of multivariate density functions. Communications in Statistics - Theory and Methods, 44 (16), 3321-3339.

See Also

fpcad; plot.fpcad.

Examples

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data(roses)
result1 = fpcad(roses[,c("Sha","Den","Sym","Lot")])
interpret.fpcad(result1)
interpret.fpcad(result1, moment = "var")
interpret.fpcad(result1, moment = "cor")
interpret.fpcad(result1, nscore = 1:2)

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