Produces a plot for model interpretation, displaying feature weights, robustness of feature weights, and features scores across patients.
model.interpretation.plot(siamcat, fn.plot, color.scheme = "BrBG", consens.thres = 0.5,heatmap.type = c("zscore", "fc"), norm.models = FALSE, limits = c(-3, 3), detect.lim = 1e-06, max.show = 50, verbose = 1)
siamcat | object of class siamcat-class |
---|---|
fn.plot | string, filename for the pdf-plot |
color.scheme | color scheme for the heatmap, defaults to |
consens.thres | minimal ratio of models incorporating a feature in order
to include it into the heatmap, defaults to |
heatmap.type | type of the heatmap, can be either |
norm.models | boolean, should the feature weights be normalized across
models?, defaults to |
limits | vector, cutoff for extreme values in the heatmap,
defaults to |
detect.lim | float, pseudocount to be added before log-transformation
of features, defaults to |
max.show | integer, maximum number of features to be shown in the model interpretation plot, defaults to 50 |
verbose | control output: |
Does not return anything, but produces the model interpretion plot.
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.
data(siamcat_example) # simple working example model.interpretation.plot(siamcat_example, fn.plot='./interpretion,pdf', heatmap.type='zscore')#>#> Error in label$label: $ operator not defined for this S4 class