R: Summarizing inferences based on cross-validation

summary.cv.grpreg {grpreg} | R Documentation

## Summarizing inferences based on cross-validation

### Description

Summary method for `cv.grpreg` or `cv.grpsurv` objects

### Usage

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

### Arguments

`object` | A `"cv.grpreg"` object.
`x` | A `"summary.cv.grpreg"` 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(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 `grpreg`/`grpsurv`.
`model` | The type of model: `"linear"`, `"logistic"`, `"Poisson"`, `"Cox"`, etc.
`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.grpreg`/`cv.grpsurv`.
`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

### See Also

`grpreg`, `cv.grpreg`, `cv.grpsurv`, `plot.cv.grpreg`

### Examples

    # 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)

[Package grpreg version 3.2-1 Index]