# calculate_variance_explained: Calculate variance explained by the model In MOFA2: Multi-Omics Factor Analysis v2

## Description

This function takes a trained MOFA model as input and calculates the proportion of variance explained (i.e. the coefficient of determinations (R^2)) by the MOFA factors across the different views.

## Usage

 ```1 2 3 4 5 6``` ```calculate_variance_explained( object, views = "all", groups = "all", factors = "all" ) ```

## Arguments

 `object` a `MOFA` object. `views` character vector with the view names, or numeric vector with view indexes. Default is 'all' `groups` character vector with the group names, or numeric vector with group indexes. Default is 'all' `factors` character vector with the factor names, or numeric vector with the factor indexes. Default is 'all'

## Value

a list with matrices with the amount of variation explained per factor and view.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```# Using an existing trained model on simulated data file <- system.file("extdata", "model.hdf5", package = "MOFA2") model <- load_model(file) # Calculate variance explained (R2) r2 <- calculate_variance_explained(model) # Plot variance explained values (view as x-axis, and factor as y-axis) plot_variance_explained(model, x="view", y="factor") # Plot variance explained values (view as x-axis, and group as y-axis) plot_variance_explained(model, x="view", y="group") # Plot variance explained values for factors 1 to 3 plot_variance_explained(model, x="view", y="group", factors=1:3) # Scale R2 values plot_variance_explained(model, max_r2 = 0.25) ```

MOFA2 documentation built on Nov. 8, 2020, 7:28 p.m.