Summarizing inferences based on cross-validation

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Description

Summary method for cv.grpregOverlap objects

Usage

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

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

Arguments

object

A "cv.grpregOverlap" object for summary function.

x

A "summary.cv.grpregOverlap" object for print function.

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.

...

Further arguments passed to or from other methods.

Value

summary.cv.grpregOverlap produces an object with S3 class "summary.cv.grpregOverlap" which inherits class "summary.cv.grpreg". The object contains the following list elements:

penalty

The penalty used by grpregOverlap.

model

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

n

Number of observations

p

Number of regression coefficients (not including the intercept).

p.latent

Number of latent 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.

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)

Yaohui Zeng and Patrick Breheny

Maintainer: Yaohui Zeng <yaohui-zeng@uiowa.edu>

References

See Also

grpregOverlap, cv.grpregOverlap

Examples

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## See examples in "grpregOverlap" and "cv.grpregOverlap".