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"))

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.

The measure along the regularization path depending on the
value in `measure`

.#'

Other SLOPE-methods:
`coef.SLOPE()`

,
`deviance.SLOPE()`

,
`plot.SLOPE()`

,
`predict.SLOPE()`

,
`print.SLOPE()`

# NOT RUN { x <- subset(infert, select = c("induced", "age", "pooled.stratum")) y <- infert$case fit <- SLOPE(x, y, family = "binomial") score(fit, x, y, measure = "auc") # }