I am using following code with glmnet:
> library(glmnet) > fit = glmnet(as.matrix(mtcars[-1]), mtcars[,1]) > plot(fit, xvar='lambda')
However, I want to print out the coefficients at best Lambda, like it is done in ridge regression. I see following structure of fit:
> str(fit) List of 12 $ a0 : Named num [1:79] 20.1 21.6 23.2 24.7 26 ... ..- attr(*, "names")= chr [1:79] "s0" "s1" "s2" "s3" ... $ beta :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots .. ..@ i : int [1:561] 0 4 0 4 0 4 0 4 0 4 ... .. ..@ p : int [1:80] 0 0 2 4 6 8 10 12 14 16 ... .. ..@ Dim : int [1:2] 10 79 .. ..@ Dimnames:List of 2 .. .. ..$ : chr [1:10] "cyl" "disp" "hp" "drat" ... .. .. ..$ : chr [1:79] "s0" "s1" "s2" "s3" ... .. ..@ x : num [1:561] -0.0119 -0.4578 -0.1448 -0.7006 -0.2659 ... .. ..@ factors : list() $ df : int [1:79] 0 2 2 2 2 2 2 2 2 3 ... $ dim : int [1:2] 10 79 $ lambda : num [1:79] 5.15 4.69 4.27 3.89 3.55 ... $ dev.ratio: num [1:79] 0 0.129 0.248 0.347 0.429 ... $ nulldev : num 1126 $ npasses : int 1226 $ jerr : int 0 $ offset : logi FALSE $ call : language glmnet(x = as.matrix(mtcars[-1]), y = mtcars[, 1]) $ nobs : int 32 - attr(*, "class")= chr [1:2] "elnet" "glmnet"
But I am not able to get the best Lambda and the corresponding coefficients. Thanks for your help.
Try this:
fit = glmnet(as.matrix(mtcars[-1]), mtcars[,1], lambda=cv.glmnet(as.matrix(mtcars[-1]), mtcars[,1])$lambda.1se) coef(fit)
Or you can specify a specify a lambda value in coef
:
fit = glmnet(as.matrix(mtcars[-1]), mtcars[,1]) coef(fit, s = cv.glmnet(as.matrix(mtcars[-1]), mtcars[,1])$lambda.1se)
You need to pick a "best" lambda, and lambda.1se
is a reasonable, or justifiable, one to pick. But you could use cv.glmnet(as.matrix(mtcars[-1]), mtcars[,1])$lambda.min
or any other value of lambda that you settle upon as "best" for you.
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