A principal component analysis is done in the Aitchison geometry (i.e. ilt-transform). Some gimics simplify the interpretation of the computed components as perturbations of amounts.
# S3 method for aplus
princomp(x,…,scores=TRUE,center=attr(covmat,"center"),
covmat=var(x,robust=robust,giveCenter=TRUE),
robust=getOption("robust"))
# S3 method for princomp.aplus
print(x,…)
# S3 method for princomp.aplus
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.aplus
predict(object,newdata,…)an aplus dataset (for princomp) or a result from princomp.aplus
not used
a logical indicating whether scores should be computed or not
the number of components to be drawn in the scree plot
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.acomp
the multiple of sigma to use when plotting the loadings
title of the plot
a fitted princomp.aplus object
another amount dataset of class aplus
further arguments to pass to internally-called functions
provides the covariance matrix to be used for the principle component analysis
provides the be used for the computation of scores
Gives the robustness type for the calculation of the
covariance matrix. See var.rmult for details.
princomp gives an object of type
c("princomp.acomp","princomp") with the following content:
the standard deviation of the principal components
the matrix of variable loadings (i.e., a matrix which
columns contain the eigenvectors). This is of class
"loadings".
the ilt-transformed vector of means used to center the dataset
the aplus vector of means used to center the dataset
the scaling applied to each variable
number of observations
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
the matched call
not clearly understood
vectors of amounts that represent a perturbation with the vectors represented by the loadings of each of the factors
vectors of amounts that represent a perturbation with the inverses of the vectors represented by the loadings of each of the factors
As a metric euclidean space, the positive real space described in
aplus has its own
principal component analysis, that can be performed either in terms of the
covariance matrix or the correlation matrix. However, since all parts in a composition
or in an amount vector share a natural scaling, they do not need the
standardization (which in fact would produce a loss of important information).
For this reason, princomp.aplus works on the covariance matrix.
To aid the interpretation we added some extra functionality to a
normal princomp(ilt(x)). First of all the result contains as
additional information the amount representation of
returned vectors in the space of the data: the center as an amount
Center, and the loadings in terms of amounts to perturbe
with, either positively
(Loadings) or negatively (DownLoadings). The Up- and
DownLoadings are normalized to the number of parts
and not to one to simplify the interpretation. A value of about one
means no change in the specific component.
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 (log-)ratios (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 the biplot uses, additionally to the classical one, a compositional concept: The differences between two arrowheads can be interpreted as log-ratios between the two components represented by the arrows.
The amount loading plot is introduced with this
package. The loadings of all component can be seen as an orthogonal basis
in the space of ilt-transformed data. These vectors are displayed by a barplot with
their corresponding amounts. A portion of one means no change of this
part. This is equivalent to a zero loading in a real principal component analysis.
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 ilt-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 ratio. The
interpretation of the ratios plot may only be done in an Aitchison-compositional framework
(see princomp.acomp).
ilt,aplus, relativeLoadings
princomp.acomp, princomp.rplus,
barplot.aplus, mean.aplus,
# NOT RUN {
data(SimulatedAmounts)
pc <- princomp(aplus(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$Loadings
pc$DownLoadings
barplot(pc$Loadings)
pc$sdev^2
cov(predict(pc,sa.lognormals5))
# }
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