View source: R/calculate_variance_explained.R
calculate_variance_explained_per_sample | R Documentation |
This function takes a trained MOFA model as input and calculates, **for each sample** the proportion of variance explained (i.e. the coefficient of determinations (R^2)) by the MOFA factors across the different views.
calculate_variance_explained_per_sample(
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 sample 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_per_sample(model)
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