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exprso (version 0.5.1)

ExprsPipeline-class: An S4 class to store models built during high-throughput learning

Description

An S4 class to store models built during high-throughput learning

Usage

# S4 method for ExprsPipeline
show(object)
# S4 method for ExprsPipeline,ANY,ANY,ANY
[(x, i, j)
# S4 method for ExprsPipeline
$(x, name)
# S4 method for ExprsPipeline
subset(x, subset, select)
# S4 method for ExprsPipeline
summary(object)
# S4 method for ExprsPipeline
getFeatures(object, index)
# S4 method for ExprsPipeline
getWeights(object, index, ...)

Arguments

object, x

An object of class ExprsPipeline.

i, j

Subsets entire ExprsPipeline object via object@summary[i, j]. Returns object@summary[, j] if argument i is missing.

name

Returns object@summary[, name].

subset

Subsets entire ExprsPipeline object via object@summary[subset, ]. Can be used to rearrange summary table.

select

Subsets entire ExprsPipeline object via object@summary[, select]. Can be used to rearrange summary table.

index

A numeric scalar. The i-th model from which to retrieve features or weights. If missing, function will tabulate features or weights across all models.

...

For getWeights, optional arguments passed to glmnet::coef.cv.glmnet.

Methods (by generic)

  • show: Method to show ExprsPipeline object.

  • [: Method to subset ExprsPipeline object.

  • $: Method to subset ExprsPipeline object.

  • subset: Method to subset ExprsPipeline object.

  • summary: Method to summarize ExprsPipeline results.

  • getFeatures: Method to return features within an ExprsPredict model.

  • getWeights: Method to return LASSO weights.

Slots

summary

Typically a data.frame. Stores the parameters and performances for the models.

machs

Typically a list. Stores the models referenced in summary slot.

See Also

ExprsArray-class ExprsModel-class ExprsPipeline-class ExprsEnsemble-class ExprsPredict-class RegrsPredict-class