R: Summarizing inferences based on cross-validation
summary.cv.ncvreg
R Documentation
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).
Author(s)
Patrick Breheny <patrick-breheny@uiowa.edu>
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)
providing 
.