ncvreg (version 3.4-0)

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

Description

Summary method for cv.ncvreg objects

Usage

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

Arguments

object

A "cv.ncvreg" object.

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.

...

Further arguments passed to or from other methods.

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.
• 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).

References

Breheny, P. and Huang, J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Statist., 5: 232-253.

See Also

ncvreg, cv.ncvreg, plot.cv.ncvreg

Examples

## Linear regression
data(prostate)
X <- as.matrix(prostate[,1:8])
y <- prostate$lpsa
cvfit <- cv.ncvreg(X, y)
summary(cvfit)

## Logistic regression
data(heart)
X <- as.matrix(heart[,1:9])
y <- heart$chd
cvfit <- cv.ncvreg(X, y, family="binomial")
summary(cvfit)