trainCurveELBO {MOFA} | R Documentation

## Training curve for Evidence Lower Bound (ELBO)

### Description

MOFA inference is done using the variational Bayes algorithm, which maximises a quantity called the Evidence Lower Bound (ELBO). The ELBO is supposed to increase monotonically up to convergence, but it can decrease substantially when dropping inactive factors. For more details read the supplementary methods. The frequency of ELBO computation as well as the convergence criteria are defined as hyperparameters in `prepareMOFA`.
All Training statistics, including the ELBO, can be fetch from the TrainStats slot of `MOFAmodel` .

### Usage

    trainCurveELBO(object, logScale = FALSE)

### Arguments

`object` | a `MOFAmodel` object.
`logScale` | boolean indicating whether to apply log transform

### Value

plot of ELBO values during training

### Examples

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

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

[Package MOFA version 1.2.0 Index]