https://github.com/cran/FSelector
Tip revision: c3894396287d55ab31393f3317dbaa4bab83caed authored by Lars Kotthoff on 28 February 2013, 00:00:00 UTC
version 0.19
version 0.19
Tip revision: c389439
correlation.Rd
\name{correlation}
\alias{linear.correlation}
\alias{rank.correlation}
\title{ Correlation filter}
\description{
The algorithm finds weights of continous attributes basing on their correlation with continous class attribute.
}
\usage{
linear.correlation(formula, data)
rank.correlation(formula, data)
}
\arguments{
\item{formula}{ a symbolic description of a model }
\item{data}{ data to process }
}
\details{
\code{linear.correlation} uses Pearson's correlation
\code{rank.correlation} uses Spearman's correlation
Rows with \code{NA} values are not taken into consideration.
}
\value{
a data.frame containing the worth of attributes in the first column and their names as row names
}
\author{ Piotr Romanski }
\examples{
library(mlbench)
data(BostonHousing)
d=BostonHousing[-4] # only numeric variables
weights <- linear.correlation(medv~., d)
print(weights)
subset <- cutoff.k(weights, 3)
f <- as.simple.formula(subset, "medv")
print(f)
weights <- rank.correlation(medv~., d)
print(weights)
subset <- cutoff.k(weights, 3)
f <- as.simple.formula(subset, "medv")
print(f)
}