run_misty {mistyR} | R Documentation

## Train MISTy models

### Description

Trains multi-view models for all target markers, estimates the performance, the contributions of the view specific models and the importance of predictor markers for each target marker.

### Usage

    run_misty(
      views,
      results.folder = "results",
      seed = 42,
      target.subset = NULL,
      bypass.intra = FALSE,
      cv.folds = 10,
      cached = FALSE,
      append = FALSE,
      ...
    )

### Arguments

`views` | view composition.
`results.folder` | path to the top level folder to store raw results.
`seed` | seed used for random sampling to ensure reproducibility.
`target.subset` | subset of targets to train models for. If `NULL`, models will be trained for markers in the intraview.
`bypass.intra` | a `logical` indicating whether to train a baseline model using the intraview data (see Details).
`cv.folds` | number of cross-validation folds to consider for estimating the performance of the multi-view models.
`cached` | a `logical` indicating whether to cache the trained models and to reuse previously cached ones if they already exist for this sample.
`append` | a `logical` indicating whether to append the performance and coefficient files in the `results.folder`. Consider setting to `TRUE` when rerunning a workflow with different `target.subset` parameters.
`...` | all additional parameters are passed to `ranger()` for training the view-specific models (see Details for defaults).

### Details

If `bypass.intra` is set to `TRUE` all variable in the intraview the intraview data will be treated as targets only. The baseline intraview model in this case is a trivial model that predicts the average of each target. If the intraview has only one variable this switch is automatically set to `TRUE`.

Default values passed to `ranger()` for training the view-specific models: `num.trees = 100`, `importance = "impurity"`, `num.threads = 1`, `seed = seed`.

### Value

Path to the results folder that can be passed to `collect_results()`.

### See Also

`create_initial_view()` for starting a view composition.

### Examples

    # Create a view composition of an intraview and a paraview with radius 10 then
    # run MISTy for a single sample.

    library(dplyr)

    # get the expression data
    data("synthetic")
    expr <- synthetic[[1]] %>% select(-c(row, col, type))
    # get the coordinates for each cell
    pos <- synthetic[[1]] %>% select(row, col)

    # compose
    misty.views <- create_initial_view(expr) %>% add_paraview(pos, l = 10)

    # run with default parameters
    run_misty(misty.views)

[Package mistyR version 1.2.1 Index]