plotRelevance {slalom} | R Documentation

## Plot results of a Slalom model

### Description

Plot results of a Slalom model

### Usage

    plotRelevance(object, n_active = 20, mad_filter = 0.4, annotated = TRUE,
      unannotated_dense = FALSE, unannotated_sparse = FALSE)

### Arguments

`object` | an object of class `Rcpp_SlalomModel`
`n_active` | number of terms (factors) to be plotted (default is 20)
`mad_filter` | numeric(1), filter factors by this mean absolute deviation to exclude outliers. For large datasets this can be set to 0
`annotated` | logical(1), should annotated factors be plotted? Default is `TRUE`
`unannotated_dense` | logical(1), should dense unannotated factors be plotted? Default is `FALSE`
`unannotated_sparse` | logical(1), should sparse unannotated factors be plotted? Default is `FALSE`

### Value

invisibly returns a list containing the two ggplot objects that make up the plot

### Examples

    gmtfile <- system.file("extdata", "reactome_subset.gmt", package = "slalom")
    genesets <- GSEABase::getGmt(gmtfile)
    data("mesc")
    model <- newSlalomModel(mesc, genesets, n_hidden = 5, min_genes = 10)
    model <- initSlalom(model)
    model <- trainSlalom(model, nIterations = 10)
    plotRelevance(model)

[Package slalom version 1.2.0 Index]