trainSLOPE | R Documentation |
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"),
...
)
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 |
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 |
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 |
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()
.
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 |
measure |
a |
model |
the model fit to the entire data set |
call |
the call |
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.
foreach::foreach()
, plot.TrainedSLOPE()
Other model-tuning:
caretSLOPE()
,
plot.TrainedSLOPE()
# 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"
)
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