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

 calculate_variance_explained 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")

# 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)
``````

bioFAM/MOFA2 documentation built on March 21, 2023, 5:27 p.m.