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\name{dgp_twoclass}
\alias{dgp_twoclass}
\title{Data-Ggnerating Function for Two-Class Problem}
\description{
Data-generating function to generate artificial data sets of a classification
problem with two response classes, denoted as \code{"A"} and \code{"B"}.
}
\usage{
dgp_twoclass(n = 100, p = 4, noise = 16, rho = 0,
b0 = 0, b = rep(1, p), fx = identity)
}
\arguments{
\item{n}{integer. Number of observations. The default is 100.}
\item{p}{integer. Number of signal predictors. The default is 4.}
\item{noise}{integer. Number of noise predictors. The default is 16.}
\item{rho}{numeric value between -1 and 1 specifying the correlation
between the signal predictors. The correlation is given by \code{rho}^k,
where k is an integer value given by \code{\link{toeplitz}}
structure. The default is 0 (no correlation between predictors).}
\item{b0}{numeric value. Baseline probability for class \code{"B"} on the logit
scale. The default is 0.}
\item{b}{numeric value. Slope parameter for the predictors on the logit scale.
The default is 1 for all predictors.}
\item{fx}{a function that is used to transform the predictors. The default
is \code{\link{identity}} (equivalent to no transformation).}
}
\value{
A \code{data.frame} including a column denoted as \code{class} that is
a factor with two levels \code{"A"} and \code{"B"}. All other columns
represent the predictor variables (signal predictors followed by noise
predictors) and are named by \code{"x1"}, \code{"x2"}, etc..
}
\seealso{\code{\link{stability}}}
\examples{dgp_twoclass(n = 200, p = 6, noise = 4)}
\keyword{resampling}
\keyword{similarity}
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