ncvreg (version 3.14.1)

summary.cv.ncvreg: Summarizing cross-validation-based inference

Description

Summary method for cv.ncvreg objects

Usage

# S3 method for cv.ncvreg
summary(object, ...)
# S3 method for summary.cv.ncvreg
print(x, digits, ...)

Value

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

penalty

The penalty used by ncvreg.

model

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

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.ncvreg.

cve

Cross-validation error (deviance).

r.squared

Proportion of variance explained by the model, as estimated by cross-validation. For models outside of linear regression, the Cox-Snell approach to defining R-squared is used.

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).

Arguments

object

A "cv.ncvreg" or "cv.ncvsurv" object.

...

Further arguments passed to or from other methods.

x

A "summary.cv.ncvreg" 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.

Author

Patrick Breheny

References

Breheny P and Huang J. (2011) Coordinate descentalgorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. c("\Sexpr[results=rd]tools:::Rd_expr_doi(\"#1\")", "10.1214/10-AOAS388")tools:::Rd_expr_doi("10.1214/10-AOAS388")

See Also

ncvreg, cv.ncvreg, plot.cv.ncvreg

Examples

# Linear regression --------------------------------------------------
data(Prostate)
cvfit <- cv.ncvreg(Prostate$X, Prostate$y)
summary(cvfit)

# Logistic regression ------------------------------------------------
data(Heart)
cvfit <- cv.ncvreg(Heart$X, Heart$y, family="binomial")
summary(cvfit)

# Cox regression -----------------------------------------------------
data(Lung)
cvfit <- cv.ncvsurv(Lung$X, Lung$y)
summary(cvfit)