\name{choose_spxenv}
\alias{choose_spxenv}
\title{Choose u and lambda for envelope based sparse partial lease squares model.}
\description{
  Select the dimension of the envelope subspace using Bayesian information
  criterion, Akaike information criterion and Likelihood ratio testing. 
  }
\usage{
	   choose_spxenv(X, Y)
}

\arguments{
  \item{X}{Predictors. An n by p matrix, p is the number of predictors. The predictors can be univariate or multivariate, discrete or continuous.}
  \item{Y}{Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables.}
\item{dims}{A vector. The dimensions to be chosen from. The default is  0 to p.}
  \item{maxiter}{Maximum number of iterations.  Default value: 100.}
  \item{ftol}{Tolerance parameter for F.  Default value: 1e-2. } 
  \item{verbose}{Flag for print out model fitting process, logical 0 or 1. Default value: 0.}  
}

\value{
  \item{result}{Dimensions of the envelope subspace chosen by BIC, AIC and LRT. And the corresponding tuning parameter chosen for the dimension.}
  \item{detail}{-2*Loglik, BIC, AIC, degree of freedom and p-values for LRT test.}
}
  
\references{
The codes are implemented based on the algorithm in Guangyu Zhu and Zhihua Su (2017). 'Envelope-based Sparse Partial Least Squares', Manuscript sumbitted for publication.
}

\author{Guangyu Zhu and Zhihua Su\cr Maintainer: Guangyu Zhu \email{gzhu22@ufl.edu}}



%\examples{

%}