register_mvpa_model: Register a Custom MVPA Model

In bbuchsbaum/rMVPA: Multivoxel Pattern Analysis in R

Register a Custom MVPA Model

Description

Adds a user-defined model specification to the rMVPA model registry (MVPAModels).

Usage

register_mvpa_model(name, model_spec)

Arguments

name

A character string, the unique name for the model.

model_spec

A list containing the model specification. It must include elements: 'type' ("Classification" or "Regression"), 'library' (character vector of required packages for the *model itself*, not for rMVPA's wrappers), 'label' (character, usually same as name), 'parameters' (data.frame of tunable parameters: parameter, class, label), 'grid' (function to generate tuning grid, takes x, y, len args), 'fit' (function), 'predict' (function), and 'prob' (function for classification, takes modelFit, newdata; should return matrix/df with colnames as levels).

Examples

## Not run: 
# Example of how a user might define an e1071 SVM spec
my_svm_spec <- list(
  type = "Classification", library = "e1071", label = "my_svm",
  parameters = data.frame(parameter = "cost", class = "numeric", label = "Cost (C)"),
  # grid should return a data.frame with columns matching 'parameter' names in 'parameters'
  grid = function(x, y, len = NULL) { 
     data.frame(cost = if (is.null(len) || len == 1) 1 else 10^seq(-2, 2, length.out = len))
  },
  # fit function receives: x, y, wts (weights), param (current params from grid), 
  # lev (levels of y), last (unused), weights (unused), classProbs (unused by e1071::svm)
  fit = function(x, y, wts, param, lev, last, weights, classProbs, ...) {
     e1071::svm(x, y, cost = param$cost, probability = TRUE, ...) # Ensure probability=TRUE for prob
  },
  # predict function receives: modelFit (output of $fit), newdata
  predict = function(modelFit, newdata, ...) {
     predict(modelFit, newdata, ...)
  },
  # prob function receives: modelFit, newdata
  # Should return a matrix/df with columns named as in levels(y)
  prob = function(modelFit, newdata, ...) {
    pred_obj <- predict(modelFit, newdata, probability = TRUE)
    attr(pred_obj, "probabilities") 
  }
)
register_mvpa_model("my_svm", my_svm_spec)
# Now load_model("my_svm") would work.

## End(Not run)

bbuchsbaum/rMVPA documentation built on June 10, 2025, 8:23 p.m.