[R] glmnet() vs. lars()

[Vito Muggeo (UniPa)]

dear all,

It appears that glmnet(), when "selecting" the covariates entering the 
model, skips from K covariates, say, to K+2 or K+3. Thus 2 or 3 
variables are "added" at the same time and it is not possible to obtain 
a ranking of the covariates according to their importance in the model. 
On the other hand lars() "adds" the covariates one at a time.
My question is: is it possible to obtain a similar output of lars (in 
terms of order of the variables entering the model) using glmnet()?

many thanks,
vito


#Example (from ?glmnet)

set.seed(123)
x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
fit1=glmnet(x,y)
fit1$df #no. of covariates entering the model at different lambdas

#Thus in the "first" model no covariate is included and in the second 
#one 2 covariates (V8 and V20) are included at the same time. Because 
#two variables are included at the same time I do not know which 
#variable (among the selected ones) is more important.
#Everything is fine with lars

o<-lars(x,y)
o$df #the covariates enter one at a time.. V8 is "better" than V20


-- 
====================================
Vito M.R. Muggeo
Dip.to Sc Statist e Matem `Vianelli'
Università di Palermo
viale delle Scienze, edificio 13
90128 Palermo - ITALY
tel: 091 23895240
fax: 091 485726
http://dssm.unipa.it/vmuggeo