Contents
How is the percent of null deviance explained in glmnet?
It shows from left to right the number of nonzero coefficients ( Df ), the percent (of null) deviance explained ( %dev) and the value of λ ( Lambda ). Although glmnet fits the model for 100 values of lambda by default, it stops early if %dev does not change sufficently from one lambda to the next (typically near the end of the path.)
When to use a negative binomial in glmnet?
The negative binomial is often used to model over-dispersed count data (instead of Poisson regression), and is also easy: There are many other families, including quasi where users can customize their own families. There are additional specialized families such as statmod:tweedie for overdispersed count data.
How does the family argument work in glmnet?
The family argument to glmnet can be the result of a call to a family function. (To learn more about family functions in R, run ?family in the R console.) All the functionality of glmnet applies to these new families, and hence their addition expands the scope of glmnet considerably.
What’s the difference between GLM family and Gaussian family?
There is a hard-wired option for this, specified via family=”gaussian” (which is also the default for glmnet ). Now we can also use family = gaussian () to fit the same model. glmnet distinguishes these two cases because the first is a character string, while the second is a GLM family object.
What is the lambda value in cv.glmnet?
This is an experimental argument, designed to fix the problems users were having with CV, with possible values “lambda” (the default) else “fraction”. With “lambda” the lambda values from the master fit (on all the data) are used to line up the predictions from each of the folds.
Which is the best value for lambda.min?
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.
How to cross validation in cv.glmnet?
Arguments x x matrix as in glmnet. y response y as in glmnet. weights Observation weights; defaults to 1 per . offset Offset vector (matrix) as in glmnet lambda Optional user-supplied lambda sequence;