plotFactorCor {MOFA} | R Documentation

## Plot correlation matrix between latent factors

### Description

Function to plot the correlation matrix between the latent factors.

### Usage

    plotFactorCor(object, method = "pearson", ...)

### Arguments

`object` | a trained `MOFAmodel` object.
`method` | a character indicating the type of correlation coefficient to be computed: pearson (default), kendall, or spearman.
`...` | arguments passed to `corrplot`

### Details

This method plots the correlation matrix between the latent factors.
The model encourages the factors to be uncorrelated, so this function usually yields a diagonal correlation matrix.
However, it is not a hard constraint such as in Principal Component Analysis and correlations between factors can occur, particularly with large number factors.
Generally, correlated factors are redundant and should be avoided, as they make interpretation harder. Therefore, if you have too many correlated factors we suggest you try reducing the number of factors.

### Value

Returns a symmetric matrix with the correlation coefficient between every pair of factors.

### Examples

    # Example on the CLL data
    filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata")
    MOFA_CLL <- loadModel(filepath)
    plotFactorCor(MOFA_CLL)

    # Example on the scMT data
    filepath <- system.file("extdata", "scMT_model.hdf5", package = "MOFAdata")
    MOFA_scMT <- loadModel(filepath)
    plotFactorCor(MOFA_scMT)

[Package MOFA version 1.0.0 Index]