interpret.fpcad {dad}

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

## S3 method for class 'fpcad'
interpret(x, nscore = 1, moment=c("mean", "sd", "var", "cov", "cor",
    "skewness", "kurtosis"), ...)

Arguments

x

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

nscore

numeric. Selects the column of the data frame x$scores consisting of a score vector.

Note that since dad-4, nscore can only be a single value (in earlier versions, it could be a vector of length > 1).

Warning: nscore 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)

...

Arguments to be passed to methods.

Details

A graphics device can contain up to 9 graphs. If there are too many (more than 36) graphs for each score, one can display the graphs in a multipage PDF file.

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, Gilles Hunault, 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

data(roses)
rosefold <- as.folder(roses[,c("Sha","Den","Sym","rose")])
result1 <- fpcad(rosefold)
interpret(result1)
## Not run: 
interpret(result1, moment = "var")

## End(Not run)
interpret(result1, moment = "cor")
interpret(result1, nscore = 2)