GLM with Regularization (Cross Validated Lambda)

Factors automatically get converted to dummy columns, ordered factors to integer.

Argument of: glmnet::predict.glmnet

Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.

Argument of: glmnet::glmnet.control

minimum fractional change in deviance for stopping path; factory default = 1.0e-5

Argument of: glmnet::glmnet.control

maximum fraction of explained deviance for stopping path; factory default = 0.999

Argument of: glmnet::glmnet.control

minimum value of lambda.min.ratio (see glmnet); factory default= 1.0e-6

Argument of: glmnet::glmnet.control

large floating point number; factory default = 9.9e35. Inf in definition of upper.limit is set to big

Argument of: glmnet::glmnet.control

minimum number of path points (lambda values) allowed; factory default = 5

Argument of: glmnet::glmnet.control

minimum probability for any class. factory default = 1.0e-9. Note that this implies a pmax of 1-pmin.

Argument of: glmnet::glmnet.control

maximum allowed exponent. factory default = 250.0

Argument of: glmnet::glmnet.control

convergence threshold for multi response bounds adjustment solution. factory default = 1.0e-10

Argument of: glmnet::glmnet.control

maximum iterations for multiresponse bounds adjustment solution.  factory default = 100

Argument of: glmnet::glmnet.control

If TRUE, reset all the parameters to the factory default; default is FALSE

Argument of: glmnet::glmnet

The elasticnet mixing parameter, with 0≤α≤ 1. The penalty is defined as (1-α)/2||β||_2^2+α||β||_1. alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.

Argument of: glmnet::glmnet

The number of lambda values - default is 100.

Argument of: glmnet::glmnet

Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero). The default depends on the sample size nobs relative to the number of variables nvars. If nobs > nvars, the default is 0.0001, close to zero.  If nobs < nvars, the default is 0.01. A very small value of lambda.min.ratio will lead to a saturated fit in the nobs < nvars case. This is undefined for "binomial" and "multinomial" models, and glmnet will exit gracefully when the percentage deviance explained is almost 1.