model.interpretation.plot {SIAMCAT} | R Documentation

## Model Interpretation Plot

### Description

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.

### Usage

    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`

### 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.

### Value

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

### Examples

    data(siamcat_example)

    # simple working example
    model.interpretation.plot(siamcat_example, fn.plot='./interpretion.pdf',
        heatmap.type='zscore')

[Package SIAMCAT version 1.6.0 Index]