67 lines
1.5 KiB
R
67 lines
1.5 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/datasets.R
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\name{dataset}
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\alias{dataset}
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\title{Generates test datasets.}
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\usage{
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dataset(name = "M1", n, B, p.mix = 0.3, lambda = 1)
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}
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\arguments{
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\item{name}{One of \code{"M1"}, \code{"M2"}, \code{"M3"}, \code{"M4"} or \code{"M5"}}
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\item{n}{nr samples}
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\item{p.mix}{Only for \code{"M4"}, see: below.}
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\item{lambda}{Only for \code{"M4"}, see: below.}
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\item{p}{Dim. of random variable \code{X}.}
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}
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\value{
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List with elements
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\itemize{
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\item{X}{data}
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\item{Y}{response}
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\item{B}{Used dim-reduction matrix}
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\item{name}{Name of the dataset (name parameter)}
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}
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}
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\description{
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Provides sample datasets. There are 5 different datasets named
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M1, M2, M3, M4 and M5 described in the paper references below.
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The general model is given by:
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\deqn{Y ~ g(B'X) + \epsilon}
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}
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\section{M1}{
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The data follows \eqn{X\sim N_p(0, \Sigma)}{X ~ N_p(0, Sigma)} for a subspace
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dimension of \eqn{k = 2} with a default of \eqn{n = 200} data points.
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The link function \eqn{g} is given as
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\deqn{g(x) = \frac{x_1}{0.5 + (x_2 + 1.5)^2} + \epsilon / 2}{%
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g(x) = x_1 / (0.5 + (x_2 + 1.5)^2) + epsilon / 2}
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}
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\section{M2}{
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\eqn{X\sim N_p(0, \Sigma)}{X ~ N_p(0, Sigma)} with \eqn{k = 2} with a
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default of \eqn{n = 200} data points.
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The link function \eqn{g} is given as
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\deqn{g(x) = (b_1^T X) (b_2^T X)^2 + \epsilon / 2}
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}
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\section{M3}{
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\deqn{g(x) = cos(b_1^T X) + \epsilon / 2}
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}
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\section{M4}{
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TODO:
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}
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\section{M5}{
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TODO:
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}
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