\name{lassoReg}
\alias{lassoReg}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Multiple regression using the Lasso algorithm as implemented in the glmnet package}
\description{
Multiple regression using the Lasso algorithm as implemented in the glmnet package.
This is a theoretically nice approach to see which combination of genes predict best 
a continuous response. Empirical evidence that this actually works with high-dimensional
data is however scarce.
}
\usage{
lassoReg(object, covariate)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{object}{object containing the expression measurements; currently the
    only method supported is one for ExpressionSet objects}
  \item{covariate}{character string indicating the column containing 
  the continuous covariate.}
}

\value{
  object of class \code{glmnet}
}
\references{Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix
    Microarrays, Chapman \& Hall/CRC, pp. 211.}
\author{Willem Talloen}


\seealso{\code{\link[a4Classif]{lassoClass}}}
\examples{
if (require(ALL)){
  data(ALL, package = "ALL")
  ALL <- addGeneInfo(ALL)
  ALL$BTtype <- as.factor(substr(ALL$BT,0,1))

  resultLasso <- lassoReg(object = ALL[1:100,], covariate = "age")
  plot(resultLasso, label = TRUE,
	   main = "Lasso coefficients in relation to degree of penalization.")
  featResultLasso <- topTable(resultLasso, n = 15)
}
}