Summarizing inferences based on cross-validation
Summary method for cv.grpreg or cv.grpsurv
objects
## S3 method for class 'cv.grpreg' summary(object, ...) ## S3 method for class 'summary.cv.grpreg' 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(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 |
model |
The type of model: |
n |
Number of observations |
p |
Number of regression coefficients (not including the intercept). |
min |
The index of |
lambda |
The sequence of |
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). |
Patrick Breheny
# 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)
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