ldaHmat | R Documentation |
Computes total and between-group matrices of Sums of Squares and Cross-Product (SSCP) deviations in linear discriminant analysis. These matrices may be used as input to the variable selection search routines anneal
, genetic
improve
or eleaps
.
## Default S3 method:
ldaHmat(x,grouping,...)
## S3 method for class 'data.frame'
ldaHmat(x,grouping,...)
## S3 method for class 'formula'
ldaHmat(formula,data=NULL,...)
x |
A matrix or data frame containing the discriminators for which the SSCP matrix is to be computed. |
grouping |
A factor specifying the class for each observation. |
formula |
A formula of the form |
data |
Data frame from which variables specified in 'formula' are preferentially to be taken. |
... |
further arguments for the method. |
A list with four items:
mat |
The total SSCP matrix |
H |
The between-groups SSCP matrix |
r |
The expected rank of the H matrix which equals the minimum between the number of discriminators and the number of groups minus one. The true rank of H can be different from r if the discriminators are linearly dependent. |
call |
The function call which generated the output. |
anneal
, genetic
, improve
, eleaps
.
##--------------------------------------------------------------------
## An example with a very small data set. We consider the Iris data
## and three groups, defined by species (setosa, versicolor and
## virginica).
data(iris)
irisHmat <- ldaHmat(iris[1:4],iris$Species)
irisHmat
##$mat
## Sepal.Length Sepal.Width Petal.Length Petal.Width
##Sepal.Length 102.168333 -6.322667 189.8730 76.92433
##Sepal.Width -6.322667 28.306933 -49.1188 -18.12427
##Petal.Length 189.873000 -49.118800 464.3254 193.04580
##Petal.Width 76.924333 -18.124267 193.0458 86.56993
##$H
## Sepal.Length Sepal.Width Petal.Length Petal.Width
##Sepal.Length 63.21213 -19.95267 165.2484 71.27933
##Sepal.Width -19.95267 11.34493 -57.2396 -22.93267
##Petal.Length 165.24840 -57.23960 437.1028 186.77400
##Petal.Width 71.27933 -22.93267 186.7740 80.41333
##$r
##[1] 2
##$call
##ldaHmat.data.frame(x = iris[1:4], grouping = iris$Species)
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