https://github.com/cran/FSelector
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Tip revision: d2854b77f74f8ee2cb0f4916e5d5a940f035fc6e authored by Lars Kotthoff on 07 January 2021, 17:10 UTC
version 0.32
Tip revision: d2854b7
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)
}
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