score: Compute one of several loss metrics on a new data set
Description
This function is a unified interface to return various types of loss for a
model fit with SLOPE().
Usage
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.#'
# 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")
# }