predict {MOFA2} | R Documentation |
This function uses the latent factors and the weights to do data predictions.
predict( object, views = "all", groups = "all", factors = "all", add_intercept = TRUE )
object |
a |
views |
character vector with the view name(s), or numeric vector with the view index(es). Default is "all". |
groups |
character vector with the group name(s), or numeric vector with the group index(es). Default is "all". |
factors |
character vector with the factor name(s) or numeric vector with the factor index(es). Default is "all". |
add_intercept |
add feature intercepts to the prediction (default is TRUE). |
MOFA generates a denoised and condensed low-dimensional representation of the data that captures the main sources of heterogeneity of the data.
This representation can be used to reconstruct a denoised representation of the data, simply using the equation Y = WX
.
For more mathematical details read the supplementary methods of the manuscript.
Returns a list with the data reconstructed by the model predictions.
# Using an existing trained model on simulated data file <- system.file("extdata", "model.hdf5", package = "MOFA2") model <- load_model(file) # Predict observations for all data modalities predictions <- predict(model)