Compute one of several loss metrics on a new data set — score • SLOPE

This function is a unified interface to return various types of loss for a model fit with SLOPE().

score(object, x, y, measure)

# S3 method for GaussianSLOPE
score(object, x, y, measure = c("mse", "mae"))

# S3 method for BinomialSLOPE
score(object, x, y, measure = c("mse", "mae", "deviance", "misclass", "auc"))

# S3 method for MultinomialSLOPE
score(object, x, y, measure = c("mse", "mae", "deviance", "misclass"))

# S3 method for PoissonSLOPE
score(object, x, y, measure = c("mse", "mae"))

Arguments

object: an object of class "SLOPE"
x: feature matrix
y: response
measure: type of target measure. "mse" returns mean squared error. "mae" returns mean absolute error, "misclass" returns misclassification rate, and "auc" returns area under the ROC curve.

Value

The measure along the regularization path depending on the value in measure.#'

See also

SLOPE(), predict.SLOPE()

Other SLOPE-methods: coef.SLOPE(), deviance.SLOPE(), plot.SLOPE(), predict.SLOPE(), print.SLOPE()

Examples

x <- subset(infert, select = c("induced", "age", "pooled.stratum"))
y <- infert$case

fit <- SLOPE(x, y, family = "binomial")
score(fit, x, y, measure = "auc")
#>        p1        p2        p3        p4        p5        p6 
#> 0.5150055 0.4921504 0.4952903 0.5025192 0.5090179 0.5101132