Argument of: glmnet::glmnet

Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".

Argument of: glmnet::glmnet

Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE)

Argument of: glmnet::glmnet

Convergence threshold for coordinate descent. Each inner coordinate-descent loop continues until the maximum change in the objective after any coefficient update is less than thresh times the null deviance. Defaults value is 1E-7.

Argument of: glmnet::glmnet

Limit the maximum number of variables in the model. Useful for very large nvars, if a partial path is desired.

Argument of: glmnet::glmnet

Limit the maximum number of variables ever to be nonzero

Argument of: glmnet::glmnet

Indices of variables to be excluded from the model. Default is none. Equivalent to an infinite penalty factor (next item).

Argument of: glmnet::glmnet

Separate penalty factors can be applied to each coefficient. This is a number that multiplies lambda to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables (and implicitly infinity for variables listed in exclude). Note: the penalty factors are internally rescaled to sum to nvars, and the lambda sequence will reflect this change.

Argument of: glmnet::glmnet

Vector of lower limits for each coefficient; default -Inf. Each of these must be non-positive. Can be presented as a single value (which will then be replicated), else a vector of length nvars

Argument of: glmnet::glmnet

Vector of upper limits for each coefficient; default Inf. See lower.limits

Argument of: glmnet::glmnet

Maximum number of passes over the data for all lambda values; default is 10^5.

Argument of: glmnet::cv.glmnet

number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3