var.acomp | R Documentation |
Compute the (co)variance matrix in the several approaches of compositional and amount data analysis.
var(x,...)
## Default S3 method:
var(x, y=NULL, na.rm=FALSE, use, ...)
## S3 method for class 'acomp'
var(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'rcomp'
var(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'aplus'
var(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'rplus'
var(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'rmult'
var(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
cov(x,y=x,...)
## Default S3 method:
cov(x, y=NULL, use="everything",
method=c("pearson", "kendall", "spearman"), ...)
## S3 method for class 'acomp'
cov(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'rcomp'
cov(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'aplus'
cov(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'rplus'
cov(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
## S3 method for class 'rmult'
cov(x,y=NULL,...,robust=getOption("robust"),
use="all.obs",giveCenter=FALSE)
x |
a dataset, eventually of amounts or compositions |
y |
a second dataset, eventually of amounts or compositions |
na.rm |
see |
use |
see |
method |
see |
... |
further arguments to |
robust |
A description of a robust estimator. FALSE for the classical estimators. See robustnessInCompositions for further details. |
giveCenter |
If TRUE the center used in the variance calculation is reported as a "center" attribute. This is especially necessary for robust estimations, where a reasonable center can not be computed independently for the me variance calculation. |
The basic functions of
stats::var
and stats::cov
are turned to
S3-generics. The original versions are copied to the default
method. This allows us to introduce generic methods to handle
variances and covariances of other data types, such as amounts or
compositions.
If classed amounts or compositions are involved, they are transformed
with their corresponding transforms, using the centered default
transform (cdt
). That implies that the variances have to
be interpreded in a log scale level for acomp
and
aplus
.
We should be aware that variance matrices of compositions
(acomp
and rcomp
) are
singular. They can be transformed to the correponding nonsingular
variances of ilr or ipt-space by clrvar2ilr
.
In R versions older than v2.0.0,
stats::var
and stats::cov
were defined in package “base” instead of in “stats”.
This might produce some misfunction.
The variance matrix of x or the covariance matrix of x and y.
K.Gerald v.d. Boogaart http://www.stat.boogaart.de
cdt
, clrvar2ilr
, clo
,
mean.acomp
, acomp
, rcomp
,
aplus
, rplus
, variation
data(SimulatedAmounts)
meanCol(sa.lognormals)
var(acomp(sa.lognormals))
var(rcomp(sa.lognormals))
var(aplus(sa.lognormals))
var(rplus(sa.lognormals))
cov(acomp(sa.lognormals5[,1:3]),acomp(sa.lognormals5[,4:5]))
cov(rcomp(sa.lognormals5[,1:3]),rcomp(sa.lognormals5[,4:5]))
cov(aplus(sa.lognormals5[,1:3]),aplus(sa.lognormals5[,4:5]))
cov(rplus(sa.lognormals5[,1:3]),rplus(sa.lognormals5[,4:5]))
cov(acomp(sa.lognormals5[,1:3]),aplus(sa.lognormals5[,4:5]))
svd(var(acomp(sa.lognormals)))
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