Summarizing cross-validation-based inference
Summary method for cv.ncvreg objects
## S3 method for class 'cv.ncvreg' summary(object, ...) ## S3 method for class 'summary.cv.ncvreg' print(x, digits, ...)
object |
A |
x |
A |
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. |
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:
The penalty used by ncvreg.
Either "linear" or "logistic", depending on
the family option in ncvreg.
Number of observations
Number of regression coefficients (not including the intercept).
The index of lambda with the smallest
cross-validation error.
The sequence of lambda values used by
cv.ncvreg.
Cross-validation error (deviance).
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.
Signal to noise ratio, as estimated by cross-validation.
For linear regression models, the scale parameter estimate.
For logistic regression models, the prediction error (misclassification error).
Patrick Breheny
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. doi: 10.1214/10-AOAS388
# 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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.