centroids {sda} | R Documentation |
centroids
computes group centroids and optionally the pooled mean
and pooled variance, the group specific variances, and powers of the pooled correlation matrix.
centroids(x, L, mean.pooled=FALSE, var.pooled=TRUE, var.groups=FALSE, powcor.pooled=FALSE, alpha=1, shrink=FALSE, verbose=TRUE)
x |
A matrix containing the data set. Note that the rows are sample observations and the columns are variables. |
L |
A factor with the group labels. |
mean.pooled |
Estimate the pooled mean. |
var.pooled |
Estimate the pooled variances. |
var.groups |
Estimate all group-specific variances. |
powcor.pooled |
Estimate pooled correlation matrix (taken to the power of alpha ). |
alpha |
exponent for the pooled correlation matrix (default: alpha=1 ). |
shrink |
Use empirical or shrinkage estimator. |
verbose |
Provide some messages while computing. |
If option shrink=TRUE
then the shrinkage estimators
var.shrink
from Opgen-Rhein and Strimmer (2007)
and cor.shrink
from Sch"afer and Strimmer (2005) are used.
centroids
returns a list
with the following components:
samples |
a vector containing the samples sizes in each group, |
means |
the empirical group means, |
mean.pooled |
the pooled empirical mean, |
var.pooled |
a vector containing the pooled variances, |
var.groups |
a matrix containing the group-specific variances, and |
powcor.pooled |
a matrix containing the pooled correlation matrix to the power of alpha
(if all correlations are zero a vector containing only the is returned to save space). |
alpha |
exponent for the pooled correlation matrix. |
Korbinian Strimmer (http://strimmerlab.org).
# load sda library library("sda") ## prepare data set data(iris) # good old iris data X = as.matrix(iris[,1:4]) Y = iris[,5] ## estimate centroids and empirical pooled variances centroids(X, Y) ## show pooled mean centroids(X, Y, mean.pooled=TRUE) ## compute group-specific variances centroids(X, Y, var.groups=TRUE) ## and inverse pooled correlation centroids(X, Y, var.groups=TRUE, powcor.pooled=TRUE, alpha=-1) ## use shrinkage estimator for variances and correlations centroids(X, Y, var.groups=TRUE, powcor.pooled=TRUE, alpha=-1, shrink=TRUE)