summary.cv.grpreg: Summarizing inferences based on cross-validation

In grpreg: Regularization Paths for Regression Models with Grouped Covariates

Summarizing inferences based on cross-validation

Description

Summary method for cv.grpreg or cv.grpsurv objects

Usage

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

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

Arguments

object	A "cv.grpreg" object.
...	Further arguments passed to or from other methods.
x	A "summary.cv.grpreg" 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(cvfit) produces an object with S3 class "summary.cv.grpreg". The class has its own print method and contains the following list elements:

penalty	The penalty used by grpreg/grpsurv.
model	The type of model: "linear", "logistic", "Poisson", "Cox", etc.
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.grpreg/cv.grpsurv.
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).

Author(s)

Patrick Breheny

See Also

grpreg, cv.grpreg, cv.grpsurv, plot.cv.grpreg

Examples

# Birthweight data
data(Birthwt)
X <- Birthwt$X
group <- Birthwt$group

# Linear regression
y <- Birthwt$bwt
cvfit <- cv.grpreg(X, y, group)
summary(cvfit)

# Logistic regression
y <- Birthwt$low
cvfit <- cv.grpreg(X, y, group, family="binomial")
summary(cvfit)

# Cox regression
data(Lung)
cvfit <- with(Lung, cv.grpsurv(X, y, group))
summary(cvfit)

grpreg documentation built on Sept. 11, 2024, 5:13 p.m.