Summarizing inferences based on cross-validation

Summary method for cv.biglasso objects.

Usage

# S3 method for class 'cv.biglasso'
summary(object, ...)

# S3 method for class 'summary.cv.biglasso'
print(x, digits, ...)

Arguments

object

A cv.biglasso object.

...

Further arguments passed to or from other methods.

x

A "summary.cv.biglasso" object.

digits

Number of digits past the decimal point to print out. Can be a vector specifying different display digits for each of the five non-integer printed values.

Value

summary.cv.biglasso produces an object with S3 class "summary.cv.biglasso". The class has its own print method and contains the following list elements:

penalty

The penalty used by biglasso.

model

Either "linear" or "logistic", depending on the family option in biglasso.

n

Number of observations

p

Number of regression coefficients (not including the intercept).

min

The index of lambda with the smallest cross-validation error.

lambda

The sequence of lambda values used by cv.biglasso.

cve

Cross-validation error (deviance).

r.squared

Proportion of variance explained by the model, as estimated by cross-validation.

snr

Signal to noise ratio, as estimated by cross-validation.

sigma

For linear regression models, the scale parameter estimate.

pe

For logistic regression models, the prediction error (misclassification error).

See also

biglasso(), cv.biglasso(), plot.cv.biglasso(), biglasso-package

Author

Yaohui Zeng and Patrick Breheny

Examples

## See examples in "cv.biglasso" and "biglasso-package"