calculate_variance_explained {MOFA2} | R Documentation

## Calculate variance explained by the model

### 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

    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

    # 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)

[Package MOFA2 version 1.0.1 Index]