This function produces a plot for model interpretation, displaying

  • the feature weights,

  • the robustness of feature weights

  • the features scores across samples,

  • the distribution of metadata across samples, and

  • the proportion of model weights shown.

model.interpretation.plot(siamcat, fn.plot = NULL,
    color.scheme = "BrBG",
    consens.thres = 0.5,
    heatmap.type = "zscore",
    limits = c(-3, 3), detect.lim = 1e-06,
    max.show = 50, prompt=TRUE, verbose = 1)

Arguments

siamcat

object of class siamcat-class

fn.plot

string, filename for the pdf-plot

color.scheme

color scheme for the heatmap, defaults to 'BrBG'

consens.thres

float, minimal ratio of models incorporating a feature in order to include it into the heatmap, defaults to 0.5 Note that for 'randomForest' models, this cutoff specifies the minimum median Gini coefficient for a feature to be included and should therefore be much lower, e.g. 0.01

heatmap.type

string, type of the heatmap, can be either 'fc' or 'zscore', defaults to 'zscore'

limits

vector, cutoff for extreme values in the heatmap, defaults to c(-3, 3)

detect.lim

float, pseudocount to be added before log-transformation of features, defaults to 1e-06

max.show

integer, maximum number of features to be shown in the model interpretation plot, defaults to 50

prompt

boolean, turn on/off prompting user input when not plotting into a pdf-file, defaults to TRUE

verbose

control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1

Value

Does not return anything, but produces the model interpretion plot.

Details

Produces a plot consisting of

  • a barplot showing the feature weights and their robustness (i.e. in what proportion of models have they been incorporated)

  • a heatmap showing the z-scores of the metagenomic features across patients

  • another heatmap displaying the metadata categories (if applicable)

  • a boxplot displaying the poportion of weight per model that is actually shown for the features that are incorporated into more than consens.thres percent of the models.

Examples

data(siamcat_example) # simple working example model.interpretation.plot(siamcat_example, fn.plot='./interpretion.pdf', heatmap.type='zscore')
#> Successfully plotted model interpretation plot to: ./interpretion.pdf