plGrid: Perform High-Throughput Machine Learning

Description Usage Arguments Details Value

View source: R/7.2-plGrid.R


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.


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.


An ExprsPipeline-class object.

exprso documentation built on May 9, 2018, 9:03 a.m.