cv.biglasso {biglasso} | R Documentation |

## Cross-validation for biglasso

### Description

Perform k-fold cross validation for penalized regression models over a grid of values for the regularization parameter lambda.

### Usage

```
cv.biglasso(
X,
y,
row.idx = 1:nrow(X),
family = c("gaussian", "binomial", "cox", "mgaussian"),
eval.metric = c("default", "MAPE", "auc", "class"),
ncores = parallel::detectCores(),
...,
nfolds = 5,
seed,
cv.ind,
trace = FALSE,
grouped = TRUE
)
```

### Arguments

`X` |
The design matrix, without an intercept, as in |

`y` |
The response vector, as in |

`row.idx` |
The integer vector of row indices of |

`family` |
Either |

`eval.metric` |
The evaluation metric for the cross-validated error and
for choosing optimal |

`ncores` |
The number of cores to use for parallel execution of the
cross-validation folds, run on a cluster created by the |

`...` |
Additional arguments to |

`nfolds` |
The number of cross-validation folds. Default is 5. |

`seed` |
The seed of the random number generator in order to obtain reproducible results. |

`cv.ind` |
Which fold each observation belongs to. By default the
observations are randomly assigned by |

`trace` |
If set to TRUE, cv.biglasso will inform the user of its progress by announcing the beginning of each CV fold. Default is FALSE. |

`grouped` |
Whether to calculate CV standard error ( |

### Details

The function calls `biglasso`

`nfolds`

times, each time leaving
out 1/`nfolds`

of the data. The cross-validation error is based on the
residual sum of squares when `family="gaussian"`

and the binomial
deviance when `family="binomial"`

.

The S3 class object `cv.biglasso`

inherits class `ncvreg::cv.ncvreg()`

. So S3
functions such as `"summary", "plot"`

can be directly applied to the
`cv.biglasso`

object.

### Value

An object with S3 class `"cv.biglasso"`

which inherits from
class `"cv.ncvreg"`

. The following variables are contained in the
class (adopted from `ncvreg::cv.ncvreg()`

).

`cve` |
The error for each value of |

`cvse` |
The estimated standard error associated with each value of for |

`lambda` |
The sequence of regularization parameter values along which the cross-validation error was calculated. |

`fit` |
The fitted |

`min` |
The index of |

`lambda.min` |
The value of |

`lambda.1se` |
The largest value of |

`null.dev` |
The deviance for the intercept-only model. |

`pe` |
If |

`cv.ind` |
Same as above. |

### Author(s)

Yaohui Zeng and Patrick Breheny

### See Also

`biglasso()`

, `plot.cv.biglasso()`

, `summary.cv.biglasso()`

, `setupX()`

### Examples

```
## Not run:
## cv.biglasso
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X)
## logistic regression
cvfit <- cv.biglasso(X.bm, y, family = 'binomial', seed = 1234, ncores = 2)
par(mfrow = c(2, 2))
plot(cvfit, type = 'all')
summary(cvfit)
## End(Not run)
```

*biglasso*version 1.6.0 Index]