loss.biglasso: Internal biglasso functions

View source: R/loss.R

loss.biglassoR Documentation

Internal biglasso functions

Description

Internal biglasso functions

Usage

loss.biglasso(y, yhat, family, eval.metric, grouped = TRUE)

Arguments

y

The observed response vector.

yhat

The predicted response vector.

family

Either "gaussian" or "binomial", depending on the response.

eval.metric

The evaluation metric for the cross-validated error and for choosing optimal lambda. "default" for linear regression is MSE (mean squared error), for logistic regression is misclassification error. "MAPE", for linear regression only, is the Mean Absolute Percentage Error. "auc", for logistic regression, is the area under the receiver operating characteristic curve (ROC).

grouped

Whether to calculate loss for the entire CV fold (TRUE), or for predictions individually. Must be TRUE when eval.metric is 'auc'.

Details

These are not intended for use by users. loss.biglasso calculates the value of the loss function for the given predictions (used for cross-validation).

Author(s)

Yaohui Zeng and Patrick Breheny


biglasso documentation built on May 29, 2024, 1:50 a.m.