summary.cv.grpregOverlap: Summarizing inferences based on cross-validation
In grpregOverlap: Penalized Regression Models with Overlapping Grouped Covariates

Description Usage Arguments Value Author(s) References See Also Examples

Summary method for cv.grpregOverlap objects

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

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

`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.

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).