4 files changed, 12 insertions, 125 deletions
diff --git a/man/print.sparsestep.Rd b/man/print.sparsestep.Rd
index ec09981..7e3d9bb 100644
--- a/man/print.sparsestep.Rd
+++ b/man/print.sparsestep.Rd
@@ -5,19 +5,20 @@
 \alias{print.sparsestep}
 \title{Print the fitted SparseStep model}
 \usage{
-\method{print}{sparsestep}(obj, ...)
+\method{print}{sparsestep}(x, ...)
 }
 \arguments{
-\item{obj}{a "sparsestep" object to print}
+\item{x}{a "sparsestep" object to print}
+
+\item{...}{further argument are ignored}
 }
 \description{
 Prints a short text of a fitted SparseStep model
 }
 \examples{
-data(diabetes)
-attach(diabetes)
-object <- sparsestep(x, y)
-print(object)
-detach(diabetes)
+x <- matrix(rnorm(100*20), 100, 20)
+y <- rnorm(100)
+fit <- sparsestep(x, y)
+print(fit)
 }
 
diff --git a/man/sparsestep.Rd b/man/sparsestep.Rd
index b6e836e..f9e1ac3 100644
--- a/man/sparsestep.Rd
+++ b/man/sparsestep.Rd
@@ -1,9 +1,7 @@
 % Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/fit.sparsestep.R, R/sparsestep.R
-\docType{package}
+% Please edit documentation in R/sparsestep.R
 \name{sparsestep}
 \alias{sparsestep}
-\alias{sparsestep-package}
 \title{Fits the SparseStep model}
 \usage{
 sparsestep(x, y, lambda = c(0.1, 0.5, 1, 5, 10), gamma0 = 1000,
@@ -49,8 +47,8 @@ absolute zero}
 \item{use.Xy}{whether or not to compute X'y and return it}
 }
 \value{
-A "sparsestep" S3 object is returned, for which predict, coef, and
-plot methods exist. It has the following items:
+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.}
@@ -73,19 +71,14 @@ performed.}
 \description{
 Fits the SparseStep model for a single value of the
 regularization parameter.
-
-sparsestep.
 }
 \examples{
 x <- matrix(rnorm(100*20), 100, 20)
 y <- rnorm(100)
 fit <- sparsestep(x, y)
 }
-\author{
-Gertjan van den Burg (author and maintainer).
-}
 \seealso{
 \code{\link{coef}}, \code{\link{print}}, \code{\link{predict}},
-\code{\link{plot}}, and \code{\link{sparsestep.path}}.
+\code{\link{plot}}, and \code{\link{path.sparsestep}}.
 }
 
diff --git a/man/sparsestep.cd.Rd b/man/sparsestep.cd.Rd
deleted file mode 100644
index 94cf48a..0000000
--- a/man/sparsestep.cd.Rd
+++ /dev/null
@@ -1,13 +0,0 @@
-% Generated by roxygen2 (4.1.1): do not edit by hand
-% Please edit documentation in R/cd.sparsestep.R
-\name{sparsestep.cd}
-\alias{sparsestep.cd}
-\title{Coordinate descent algorithm for SparseStep}
-\usage{
-sparsestep.cd(x, y, lambdas = NULL, epsilon = 1e-05, intercept = TRUE,
-  ...)
-}
-\description{
-Coordinate descent algorithm for SparseStep
-}
-
diff --git a/man/sparsestep.path.Rd b/man/sparsestep.path.Rd
deleted file mode 100644
index c038dd9..0000000
--- a/man/sparsestep.path.Rd
+++ /dev/null
@@ -1,94 +0,0 @@
-% 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).
-}
-