calculate_variance_explained {MOFA2} | R Documentation |
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.
calculate_variance_explained( object, views = "all", groups = "all", factors = "all" )
object |
a |
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' |
a list with matrices with the amount of variation explained per factor and view.
# 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)