load_model {MOFA2} | R Documentation

## Load a trained MOFA

### Description

Method to load a trained MOFA
The training of mofa is done using a Python framework, and the model output is saved as an .hdf5 file, which has to be loaded in the R package.

### Usage

    load_model(
      file,
      sort_factors = TRUE,
      on_disk = FALSE,
      load_data = TRUE,
      remove_outliers = FALSE,
      remove_inactive_factors = TRUE,
      verbose = FALSE,
      load_interpol_Z = FALSE
    )

### Arguments

`file` | an hdf5 file saved by the mofa Python framework
`sort_factors` | logical indicating whether factors should be sorted by variance explained (default is TRUE)
`on_disk` | logical indicating whether to work from memory (FALSE) or disk (TRUE). This should be set to TRUE when the training data is so big that cannot fit into memory. On-disk operations are performed using the `HDF5Array` and `DelayedArray` framework.
`load_data` | logical indicating whether to load the training data (default is TRUE, it can be memory expensive)
`remove_outliers` | logical indicating whether to mask outlier values.
`remove_inactive_factors` | logical indicating whether to remove inactive factors from the model.
`verbose` | logical indicating whether to print verbose output (default is FALSE)
`load_interpol_Z` | (MEFISTO) logical indicating whether to load predictions for factor values based on latent processed (only relevant for models trained with covariates and Gaussian processes, where prediction was enabled)

### Value

a `MOFA` model

### Examples

    #' # Using an existing trained model on simulated data
    file <- system.file("extdata", "model.hdf5", package = "MOFA2")
    model <- load_model(file)

[Package MOFA2 version 1.4.0 Index]