Train a SLOPE model

This function trains a model fit by SLOPE() by tuning its parameters through cross-validation.

trainSLOPE(
  x,
  y,
  q = 0.2,
  number = 10,
  repeats = 1,
  measure = c("mse", "mae", "deviance", "misclass", "auc"),
  ...
)

Arguments

x

the design matrix, which can be either a dense matrix of the standard matrix class, or a sparse matrix inheriting from Matrix::sparseMatrix. Data frames will be converted to matrices internally.

y

the response, which for family = "gaussian" must be numeric; for family = "binomial" or family = "multinomial", it can be a factor.

q

parameter controlling the shape of the lambda sequence, with usage varying depending on the type of path used and has no effect is a custom lambda sequence is used. Must be greater than 1e-6 and smaller than 1.

number

number of folds (cross-validation)

repeats

number of repeats for each fold (for repeated k-fold cross validation)

measure

measure to try to optimize; note that you may supply multiple values here and that, by default, all the possible measures for the given model will be used.

...

other arguments to pass on to SLOPE()

Value

An object of class "TrainedSLOPE", with the following slots:

summary

a summary of the results with means, standard errors, and 0.95 confidence levels

data

the raw data from the model training

optima

a data.frame of the best (mean) values for the different metrics and their corresponding parameter values

measure

a data.frame listing the used metrics and their labels

model

the model fit to the entire data set

call

the call

Details

Note that by default this method matches all of the available metrics for the given model family against those provided in the argument measure. Collecting these measures is not particularly demanding computationally so it is almost always best to leave this argument as it is and then choose which argument to focus on in the call to plot.TrainedSLOPE().

Parallel operation

This function uses the foreach package to enable parallel operation. To enable this, simply register a parallel backend using, for instance, doParallel::registerDoParallel() from the doParallel package before running this function.

See also

foreach::foreach(), plot.TrainedSLOPE()

Other model-tuning: caretSLOPE(), plot.TrainedSLOPE()

Examples

# 8-fold cross-validation repeated 5 times
tune <- trainSLOPE(subset(mtcars, select = c("mpg", "drat", "wt")),
  mtcars$hp,
  q = c(0.1, 0.2),
  number = 8,
  repeats = 5,
  measure = "mse"
)