View source: R/summary-cv-ncvreg.R

summary.cv.ncvreg | R Documentation |

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 |

`...` |
Further arguments passed to or from other methods. |

`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. |

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 instances

- 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. For models outside of linear regression, the Cox-Snell approach to defining R-squared is used.

- 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

Breheny P and Huang J. (2011) Coordinate descent algorithms for nonconvex
penalized regression, with applications to biological feature selection.
*Annals of Applied Statistics*, **5**: 232-253. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/10-AOAS388")}

`ncvreg()`

, `cv.ncvreg()`

, `plot.cv.ncvreg()`

```
# 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)
```

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