Summarize Cross-Validated PENSE Fit

If lambda = "se" and object contains fitted estimates for every penalization level in the sequence, extract the coefficients of the most parsimonious model with prediction performance statistically indistinguishable from the best model. This is determined to be the model with prediction performance within se_mult * cv_se from the best model.

Usage

# S3 method for class 'pense_cvfit'
summary(object, alpha, lambda = "min", se_mult = 1, ...)

# S3 method for class 'pense_cvfit'
print(x, alpha, lambda = "min", se_mult = 1, ...)

Arguments

object, x

an (adaptive) PENSE fit with cross-validation information.

alpha

Either a single number or missing. If given, only fits with the given alpha value are considered. If lambda is a numeric value and object was fit with multiple alpha values, the parameter alpha must not be missing.

lambda

either a string specifying which penalty level to use ("min", "se", "{x}-se") or a single numeric value of the penalty parameter. See details.

se_mult

If lambda = "se", the multiple of standard errors to tolerate.

...

ignored.

See also

prediction_performance() for information about the estimated prediction performance.

coef.pense_cvfit() for extracting only the estimated coefficients.

Other functions for plotting and printing: plot.pense_cvfit(), plot.pense_fit(), prediction_performance()