\name{cutoff} \alias{cutoff.k} \alias{cutoff.k.percent} \alias{cutoff.biggest.diff} \title{ Cutoffs } \description{ The algorithms select a subset from a ranked attributes. } \usage{ cutoff.k(attrs, k) cutoff.k.percent(attrs, k) cutoff.biggest.diff(attrs) } \arguments{ \item{attrs}{ a data.frame containing ranks for attributes in the first column and their names as row names } \item{k}{ a positive integer in case of \code{cutoff.k} and a numeric between 0 and 1 in case of \code{cutoff.k.percent} } } \details{ \code{cutoff.k} chooses k best attributes \code{cutoff.k.percent} chooses best k * 100\% of attributes \code{cutoff.biggest.diff} chooses a subset of attributes which are significantly better than other. } \value{ A character vector containing selected attributes. } \author{ Piotr Romanski } \examples{ data(iris) weights <- information.gain(Species~., iris) print(weights) subset <- cutoff.k(weights, 1) f <- as.simple.formula(subset, "Species") print(f) subset <- cutoff.k.percent(weights, 0.75) f <- as.simple.formula(subset, "Species") print(f) subset <- cutoff.biggest.diff(weights) f <- as.simple.formula(subset, "Species") print(f) }