Description Usage Arguments Details Value Note Author(s) References Examples
Estimation in the regression model : Y= X β + σ N(0,1)
Variable selection by choosing the best predictor among
predictors emanating
from different methods as lasso,
elasticnet, adaptive lasso, pls, randomForest.
1 2 3 4 5 6 7 8 9  VARselect(Y, X, dmax = NULL, normalize = TRUE, method = c("lasso",
"ridge", "pls", "en", "ALridge", "ALpls", "rF", "exhaustive"),
pen.crit = NULL, lasso.dmax = NULL, ridge.dmax = NULL, pls.dmax = NULL,
en.dmax = NULL, ALridge.dmax = NULL, ALpls.dmax = NULL, rF.dmax = NULL,
exhaustive.maxdim = 5e+05, exhaustive.dmax = NULL, en.lambda = c(0.01,
0.1, 0.5, 1, 2, 5), ridge.lambda = c(0.01, 0.1, 0.5,
1, 2, 5), rF.lmtry = 2, pls.ncomp = 5, ALridge.lambda = c(0.01,
0.1, 0.5, 1, 2, 5), ALpls.ncomp = 5, max.steps = NULL,
K = 1.1, verbose = TRUE, long.output = FALSE)

Y 
vector with n components : response variable. 
X 
matrix with n rows and p columns : covariates. 
dmax 
integer : maximum number of variables in the lasso
estimator. 
normalize 
logical : if TRUE the columns of X are scaled 
method 
vector of characters whose components are subset of 
pen.crit 
vector with 
lasso.dmax 
integer lower than 
ridge.dmax 
integer lower than 
pls.dmax 
integer lower than 
en.dmax 
integer lower than 
ALridge.dmax 
integer lower than 
ALpls.dmax 
integer lower than 
rF.dmax 
integer lower than 
exhaustive.maxdim 
integer : maximum number of subsets of covariates considered in the exhaustive method. See details. 
exhaustive.dmax 
integer lower than 
en.lambda 
vector : tuning parameter of the
ridge. It is the input parameter 
ridge.lambda 
vector : tuning parameter of the
ridge. It is the input parameter lambda of function

rF.lmtry 
vector : tuning paramer 
pls.ncomp 
integer : tuning parameter of the pls. It is the
input parameter 
ALridge.lambda 
similar to

ALpls.ncomp 
similar to 
max.steps 
integer. Maximum number of steps in the lasso
procedure. Corresponds to the input 
K 
scalar : value of the parameter K in the LINselect criteria. 
verbose 
logical : if TRUE a trace of the current process is displayed in real time. 
long.output 
logical : if FALSE only the component summary will be returned. See Value. 
When method is pls
or ALpls
, the
LINselect
procedure is carried out considering the number
of components in the pls
method as the tuning
parameter.
This tuning parameter varies from 1 to pls.ncomp
.
When method is exhaustive
, the maximum
number of variate d is calculated as
follows.
Let q be the largest integer such that choose(p,q)
<
exhaustive.maxdim
. Then d = min(q, exhaustive.dmax,dmax)
.
A list with at least length(method)
components.
For each procedure in method
a list with components
support
: vector of integers. Estimated support of the
parameters β for the considered procedure.
crit
: scalar equals to the LINselect criteria
calculated in the estimated support.
fitted
: vector with length n. Fitted value of
the response calculated when the support of β
equals support
.
coef
: vector whose first component is the estimated
intercept.
The other components are the estimated non zero
coefficients when the support of β
equals support
.
If length(method)
> 1, the additional component summary
is a list with three
components:
support
: vector of integers. Estimated support of the
parameters β corresponding to the minimum
of the criteria among all procedures.
crit
: scalar. Minimum value of the
criteria among all procedures.
method
: vector of characters. Names of the
procedures for
which the minimum is reached
If pen.crit = NULL
, the component pen.crit
gives the
values of the penalty calculated by the function penalty
.
If long.output
is TRUE the component named
chatty
is a list with length(method)
components.
For each procedure in method
, a list with components
support
where support[[l]]
is a vector of
integers containing an estimator of the support of the
parameters β.
crit
: vector where crit[l]
contains the
value of the LINselect criteria calculated in
support[[l]]
.
When method is lasso
, library elasticnet
is loaded.
When method is en
, library elasticnet
is loaded.
When method is ridge
, library MASS
is loaded.
When method is rF
, library randomForest
is loaded.
When method is pls
, library pls
is loaded.
When method is ALridge
, libraries MASS
and elasticnet
are loaded.
When method is ALpls
, libraries pls
and elasticnet
are loaded.
When method is exhaustive
, library gtools
is loaded.
Yannick Baraud, Christophe Giraud, Sylvie Huet
See Baraud et al. 2010
http://hal.archivesouvertes.fr/hal00502156/fr/
Giraud et al., 2013,
http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1356098553
1 2 3 4 5 6 7 8 9 10 11 12 13  #source("charge.R")
library("LINselect")
# simulate data with
# beta=c(rep(2.5,5),rep(1.5,5),rep(0.5,5),rep(0,p15))
ex < simulData(p=100,n=100,r=0.8,rSN=5)
## Not run: ex1.VARselect < VARselect(ex$Y,ex$X,exhaustive.dmax=2)
## Not run: data(diabetes)
## Not run: attach(diabetes)
## Not run: ex.diab < VARselect(y,x2,exhaustive.dmax=5)
## Not run: detach(diabetes)

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