\name{choose_spenv}
\alias{choose_spenv}
\title{Choose u and lambda for sparse envelope model.}
\description{
  Select the dimension of the envelope subspace using Bayesian information
  criterion, Akaike information criterion and Likelihood ratio testing. 
  }
\usage{
       choose_spenv(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 r.}
  \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 Z. Su, G. Zhu and X. Chen(2015). 

Su Z, Zhu G, Chen X, Yang Y. Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression. Biometrika. 2016 Sep 1;103(3):579-93.
}

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



\examples{
## Berkeley
data(Berkeley)
X= Berkeley$X
Y2 = Berkeley$Y[,c(1,2,21,23)]
choose_spenv(X,Y2)
m1=spenv(X,Y2,1)
}