Trains and deploys models across a vast parameter search space.
plGrid(array.train, array.valid = NULL, top, how, fold = 10,
aucSkip = FALSE, verbose = FALSE, ...)
Specifies the ExprsArray
object to use as training set.
Specifies the ExprsArray
object to use as validation set.
A numeric scalar or character vector. A numeric scalar indicates
the number of top features that should undergo feature selection. A character vector
indicates specifically which features by name should undergo feature selection.
Set top = 0
to include all features. Note that providing a numeric vector
for the top
argument will have plGrid
search across multiple
top features. However, by providing a list of numeric vectors as the top
argument, the user can force the default handling of numeric vectors.
A character string. Specifies the build
method to iterate.
A numeric scalar. Specifies the number of folds for cross-validation.
Set fold = 0
to perform leave-one-out cross-validation. Argument passed
to plCV
. Set fold = NULL
to skip cross-validation altogether.
A logical scalar. Argument passed to calcStats
.
A logical scalar. Argument passed to exprso-predict
.
Arguments passed to the how
method. Unlike the build
method,
plGrid
allows multiple parameters for each argument, supplied as a vector.
See Details.
An ExprsPipeline-class
object.
plGrid
will build
and exprso-predict
for
each combination of parameters provided as additional arguments (...
).
When using plGrid
, supplying a numeric vector as the top
argument will train and deploy a model of each mentioned size for
each combination of parameters provided in ...
. To skip validation set
prediction, use array.valid = NULL
. Either way, this function returns an
ExprsPipeline-class
object which contains a summary of the build
parameters and the models themselves. The argument fold
controls
cross-validation via plCV
.