plot_interaction_heatmap: Plot importance heatmap for a view

In saezlab/mistyR: Multiview Intercellular SpaTial modeling framework

Plot importance heatmap for a view

Description

Generate a heatmap with importances of predictor-target interaction.

Usage

plot_interaction_heatmap(
  misty.results,
  view,
  cutoff = 1,
  trim = -Inf,
  trim.measure = c("gain.R2", "multi.R2", "intra.R2", "gain.RMSE", "multi.RMSE",
    "intra.RMSE"),
  clean = FALSE
)

Arguments

misty.results	a results list generated by collect_results().
view	abbreviated name of the view.
cutoff	importance threshold. Importances below this value will be colored white in the heatmap and considered as not relevant.
trim	display targets with performance value above (if R2 or gain) or below (otherwise) this value only.
trim.measure	the measure used for trimming.
clean	a logical indicating whether to remove rows and columns with all importances are below cutoff from the heatmap.

Value

The misty.results list (invisibly).

See Also

collect_results() to generate a results list from raw results.

Other plotting functions: plot_contrast_heatmap(), plot_contrast_results(), plot_improvement_stats(), plot_interaction_communities(), plot_view_contributions()

Examples

all.samples <- list.dirs("results", recursive = FALSE)

collect_results(all.samples) %>%
  plot_interaction_heatmap("intra") %>%
  plot_interaction_heatmap("para.10", cutoff = 0.5)

saezlab/mistyR documentation built on March 25, 2024, 4:12 p.m.