plot_variance_explained_per_feature {MOFA2} | R Documentation

## Plot variance explained by the model for a set of features Returns a tile plot with a group on the X axis and a feature along the Y axis

### Description

Plot variance explained by the model for a set of features

Returns a tile plot with a group on the X axis and a feature along the Y axis

### Usage

    plot_variance_explained_per_feature(
      object,
      view,
      features = 10,
      split_by_factor = FALSE,
      group_features_by = NULL,
      groups = "all",
      factors = "all",
      min_r2 = 0,
      max_r2 = NULL,
      legend = TRUE,
      return_data = FALSE,
      ...
    )

### Arguments

`object` | a `MOFA` object.
`view` | a view name or index.
`features` | a vector with indices or names for features from the respective view, or number of top features to be fetched by their loadings across specified factors. "all" to plot all features.
`split_by_factor` | logical indicating whether to split R2 per factor or plot R2 jointly
`group_features_by` | column name of features metadata to group features by
`groups` | a vector with indices or names for sample groups (default is all)
`factors` | a vector with indices or names for factors (default is all)
`min_r2` | minimum variance explained for the color scheme (default is 0).
`max_r2` | maximum variance explained for the color scheme.
`legend` | logical indicating whether to add a legend to the plot (default is TRUE).
`return_data` | logical indicating whether to return the data frame to plot instead of plotting
`...` | extra arguments to be passed to `calculate_variance_explained`

### Value

ggplot object

### Examples

    # Using an existing trained model
    file <- system.file("extdata", "model.hdf5", package = "MOFA2")
    model <- load_model(file)
    plot_variance_explained_per_feature(model, view = 1)

[Package MOFA2 version 1.0.1 Index]