compositions (version 2.0-1)

princomp.rplus: Principal component analysis for real amounts

Description

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

Usage

# S3 method for rplus
princomp(x,…,scores=TRUE,center=attr(covmat,"center"),
                           covmat=var(x,robust=robust,giveCenter=TRUE),
                           robust=getOption("robust"))
 # S3 method for princomp.rplus
print(x,…)
 # S3 method for 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 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.

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.

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 amount loading plot is more or less a standard loadings plot. The loadings are displayed by a barplot as positive and negative changes of amounts.

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.

See Also

iit,rplus, relativeLoadings princomp.rcomp, princomp.aplus,

Examples

# NOT RUN {
data(SimulatedAmounts)
pc <- princomp(rplus(sa.lognormals5))
pc
summary(pc)
plot(pc)           #plot(pc,type="screeplot")
plot(pc,type="v")
plot(pc,type="biplot")
plot(pc,choice=c(1,3),type="biplot")
plot(pc,type="loadings")
plot(pc,type="loadings",scale.sdev=-1) # Downward
plot(pc,type="relative",scale.sdev=NA) # The directions
plot(pc,type="relative",scale.sdev=1) # one sigma Upward 
plot(pc,type="relative",scale.sdev=-1) # one sigma Downward
biplot(pc)
screeplot(pc)
loadings(pc)
relativeLoadings(pc,mult=FALSE)
relativeLoadings(pc)
relativeLoadings(pc,scale.sdev=1)
relativeLoadings(pc,scale.sdev=2)

pc$sdev^2
cov(predict(pc,sa.lognormals5))
# }