plotVarianceExplained {MOFA} | R Documentation

## Plot variance explained by the model

### Description

Method to plot variance explained (R-squared) by the MOFA model for each view and latent factor.
As a measure of variance explained for gaussian data we adopt the coefficient of determination (R2).
For details on the computation see the help of the `calculateVarianceExplained` function

### Usage

    plotVarianceExplained(object, cluster = TRUE, ...)

### Arguments

`object` | a `MOFAmodel` object.
`cluster` | logical indicating whether to do hierarchical clustering on the plot
`...` | extra arguments to be passed to `calculateVarianceExplained`

### Value

ggplot object

### Examples

    # Using an existing trained model on the CLL data
    filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata")
    MOFA_CLL <- loadModel(filepath)
    plotVarianceExplained(MOFA_CLL)

    # Using an existing trained model on the scMT data
    filepath <- system.file("extdata", "scMT_model.hdf5", package = "MOFAdata")
    MOFA_scMT <- loadModel(filepath)
    plotVarianceExplained(MOFA_scMT)

[Package MOFA version 1.2.0 Index]