princomp.rcomp: Principal component analysis for real compositions

Description

A principal component analysis is done in real geometry (i.e. cpt-transform) of the simplex. Some gimics simplify the interpretation of the obtained components.

Usage

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

Arguments

an rcomp dataset (for princomp) or a result from princomp.rcomp

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.rcomp object

newdata

another compositional 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". The last eigenvalue is removed since it should contain the irrelevant scaling.

Loadings

the loadings as an rmult-object

center

the cpt-transformed vector of means used to center the dataset

Center

the rcomp vector of means used to center the dataset

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(cpt(x)) is performed. To avoid confusion, the meaningless last principal component is removed.

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 strongly differs from a classical one. The relevant variables are not the arrows drawn (one for each component), but rather the links (i.e., the differences) between two arrow heads, which represents the difference between the two components represented by the arrows, or the transfer of mass from one to the other.

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

The loading plot can work in two different modes: If scale.sdev is set to NA it displays the composition being represented by the unit vector of loadings in cpt-transformed space. If scale.sdev is numeric we use this composition 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.

Examples

Run this code

# NOT RUN {
data(SimulatedAmounts)
pc <- princomp(rcomp(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))
# }

Run the code above in your browser using DataLab

Get 50% off unlimited learning