princomprplus: Principal component analysis for real amounts In compositions: Compositional Data Analysis

Description

A principal component analysis is done in real geometry (i.e. using iit-transform).

Usage

 1 2 3 4 5 6 7 8 9 10 11 12 ## S3 method for class 'rplus' princomp(x,...,scores=TRUE,center=attr(covmat,"center"), covmat=var(x,robust=robust,giveCenter=TRUE), robust=getOption("robust")) ## S3 method for class 'princomp.rplus' print(x,...) ## S3 method for class 'princomp.rplus' plot(x,y=NULL,...,npcs=min(10,length(x\$sdev)), type=c("screeplot","variance","biplot","loadings","relative"), main=NULL,scale.sdev=1) ## S3 method for class 'princomp.rplus' predict(object,newdata,...)

Arguments

 x an rplus-dataset (for princomp) or a result from princomp.rplus y not used scores a logical indicating whether scores should be computed or not npcs the number of components to be drawn in the scree plot type type of the plot: "screeplot" is a lined screeplot, "variance" is a boxplot-like screeplot, "biplot" is a biplot, "loadings" displays the loadings as a barplot scale.sdev the multiple of sigma to use when plotting the loadings main title of the plot object a fitted princomp.rplus object newdata another amount dataset of class rcomp ... further arguments to pass to internally-called functions covmat provides the covariance matrix to be used for the principle component analysis center provides the be used for the computation of scores robust Gives the robustness type for the calculation of the covariance matrix. See var.rmult for details.

Details

Mainly a princomp(iit(x)) is performed. Note all parts in a composition or in an amount vector share a natural scaling. Therefore, they do not need any preliminary standardization (which in fact would produce a loss of important information). For this reason, princomp.rplus works on the covariance matrix.
The plot routine provides screeplots (type = "s",type= "v"), biplots (type = "b"), plots of the effect of loadings (type = "b") in scale.sdev*sdev-spread, and loadings of pairwise differences (type = "r").
The interpretation of a screeplot does not differ from ordinary screeplots. It shows the eigenvalues of the covariance matrix, which represent the portions of variance explained by the principal components.
The interpretation of the biplot uses, additionally to the classical interperation, a compositional concept: the differences between two arrowheads can be interpreted as the shift of mass between the two components represented by the arrows.
The loadings plot can work in two different modes: If scale.sdev is set to NA it displays the amount vector being represented by the unit vector of loadings in the iit-transformed space. If scale.sdev is numeric we use this amount vector scaled by the standard deviation of the respective component.
The relative plot displays the relativeLoadings as a barplot. The deviation from a unit bar shows the effect of each principal component on the respective differences.

Value

princomp gives an object of type c("princomp.rcomp","princomp") with the following content:

 sdev the standard deviation of the principal components loadings the matrix of variable loadings (i.e., a matrix which columns contain the eigenvectors). This is of class "loadings" Loadings the loadings as an "rmult"-object center the iit-transformed vector of means used to center the dataset Center the rplus vector of means used to center the dataset (center and Center have no difference, except that the second has a class) scale the scaling applied to each variable n.obs number of observations scores if scores = TRUE, the scores of the supplied data on the principal components. Scores are coordinates in a basis given by the principal components and thus not compositions call the matched call na.action not clearly understood

predict returns a matrix of scores of the observations in the newdata dataset.
The other routines are mainly called for their side effect of plotting or printing and return the object x.