dad (version 4.1.6)

interpret.fmdsd: Scores of the fmdsd function vs. moments of the densities

Description

Applies to an object of class "fmdsd", plots the 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 fmdsd
interpret(x, nscore = 1, moment=c("mean", "sd", "var", "cov", "cor",
    "skewness", "kurtosis"), ...)

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.

Arguments

x

object of class "fmdsd" (returned by the fmdsd 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 fmdsd function.

moment

character string. Selects the moments to cross with scores:

• "mean" (means, which is the default value)

• "sd" (standard deviations)

• "cov" (covariances)

• "cor" (correlation coefficients)

• "skewness" (skewness coefficients)

• "kurtosis" (kurtosis coefficients)

...

Arguments to be passed to methods.

Author

Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine Demotes-Mainard

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 fmdsd.

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.

Delicado, P. (2011). Dimensionality reduction when data are density functions. Computational Statistics & Data Analysis, 55, 401-420.

See Also

fmdsd; plot.fmdsd.

Examples

data(roses)
x <- roses[,c("Sha","Den","Sym","rose")]
rosesfold <- as.folder(x)
result1 <- fmdsd(rosesfold)
interpret(result1)
if (FALSE) {
interpret(result1, moment = "var")
}
interpret(result1, nscore = 2)