Getting glmnet coefficients at 'best' lambda

Question

I am using following code with glmnet:

> library(glmnet)
> fit = glmnet(as.matrix(mtcars[-1]), mtcars[,1])
> plot(fit, xvar=\'lambda\')

Answer 1

boxcox(){MASS} provides a maximum-likelihood plot showing which value of l provides the best fit in a linear model

boxcox(lm.fit) provides the maximum-likelihood plot for a wide range of l’s in the linear model

lm.fit pick the l with the highest ML value

boxcox(lm.fit,lambda=seq(-0.1, 0.1, 0.01)) if, for example, the highest l is around 0.04, get a zoomed in plot around that area

In the example, the function provides a plot between l =- 0.1 and 0.1 in 0.01 increments.