bugfixes, documentation improvements, and generic function agreement

1 files changed, 0 insertions, 94 deletions

diff --git a/man/sparsestep.path.Rd b/man/sparsestep.path.Rd
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-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/path.sparsestep.R
-\name{sparsestep.path}
-\alias{sparsestep.path}
-\title{Approximate path algorithm for the SparseStep model}
-\usage{
-sparsestep.path(x, y, max.depth = 10, gamma0 = 1000, gammastop = 1e-04,
-  IMsteps = 2, gammastep = 2, normalize = TRUE, intercept = TRUE,
-  force.zero = TRUE, threshold = 1e-07, XX = NULL, Xy = NULL,
-  use.XX = TRUE, use.Xy = TRUE)
-}
-\arguments{
-\item{x}{matrix of predictors}
-
-\item{y}{response}
-
-\item{max.depth}{maximum recursion depth}
-
-\item{gamma0}{starting value of the gamma parameter}
-
-\item{gammastop}{stopping value of the gamma parameter}
-
-\item{IMsteps}{number of steps of the majorization algorithm to perform for
-each value of gamma}
-
-\item{gammastep}{factor to decrease gamma with at each step}
-
-\item{normalize}{if TRUE, each variable is standardized to have unit L2
-norm, otherwise it is left alone.}
-
-\item{intercept}{if TRUE, an intercept is included in the model (and not
-penalized), otherwise no intercept is included}
-
-\item{force.zero}{if TRUE, absolute coefficients smaller than the provided
-threshold value are set to absolute zero as a post-processing step,
-otherwise no thresholding is performed}
-
-\item{threshold}{threshold value to use for setting coefficients to
-absolute zero}
-
-\item{XX}{The X'X matrix; useful for repeated runs where X'X stays the same}
-
-\item{Xy}{The X'y matrix; useful for repeated runs where X'y stays the same}
-
-\item{use.XX}{whether or not to compute X'X and return it}
-
-\item{use.Xy}{whether or not to compute X'y and return it}
-}
-\value{
-A "sparsestep" S3 object is returned, for which print, predict,
-coef, and plot methods exist. It has the following items:
-\item{call}{The call that was used to construct the model.}
-\item{lambda}{The value(s) of lambda used to construct the model.}
-\item{gamma0}{The gamma0 value of the model.}
-\item{gammastop}{The gammastop value of the model}
-\item{IMsteps}{The IMsteps value of the model}
-\item{gammastep}{The gammastep value of the model}
-\item{intercept}{Boolean indicating if an intercept was fitted in the
-model}
-\item{force.zero}{Boolean indicating if a force zero-setting was
-performed.}
-\item{threshold}{The threshold used for a forced zero-setting}
-\item{beta}{The resulting coefficients stored in a sparse matrix format
-(dgCMatrix). This matrix has dimensions nvar x nlambda}
-\item{a0}{The intercept vector for each value of gamma of length nlambda}
-\item{normx}{Vector used to normalize the columns of x}
-\item{meanx}{Vector of column means of x}
-\item{XX}{The matrix X'X if use.XX was set to TRUE}
-\item{Xy}{The matrix X'y if use.Xy was set to TRUE}
-}
-\description{
-Fits the entire regularization path for SparseStep using a
-Golden Section search. Note that this algorithm is approximate, there is no
-guarantee that the solutions _between_ induced values of lambdas do not
-differ from those calculated. For instance, if solutions are calculated at
-\eqn{\lambda_{i}}{\lambda[i]} and \eqn{\lambda_{i+1}}{\lambda[i+1]}, this
-algorithm ensures that \eqn{\lambda_{i+1}}{\lambda[i+1]} has one more zero
-than the solution at \eqn{\lambda_{i}}{\lambda[i]} (provided the recursion
-depth is large enough). There is however no guarantee that there are no
-different solutions between \eqn{\lambda_{i}}{\lambda[i]} and
-\eqn{\lambda_{i+1}}{\lambda[i+1]}.  This is an ongoing research topic.
-
-Note that this path algorithm is not faster than running the
-\code{sparsestep} function with the same \code{\lambda} sequence.
-}
-\examples{
-x <- matrix(rnorm(100*20), 100, 20)
-y <- rnorm(100)
-pth <- sparsestep.path(x, y)
-}
-\author{
-Gertjan van den Burg (author and maintainer).
-}
-