load_model: Load a Pre-defined MVPA Model

In bbuchsbaum/rMVPA: Multivoxel Pattern Analysis in R

Load a Pre-defined MVPA Model

Description

Retrieves a model specification from the pre-defined set of MVPA models.

Usage

load_model(name)

Arguments

name

Character string specifying the model to load. Must be a pre-defined MVPA model name: corclass Correlation-based classifier with template matching sda_notune Simple Shrinkage Discriminant Analysis without tuning sda_boot SDA with bootstrap resampling glmnet_opt Elastic net with EPSGO parameter optimization sparse_sda SDA with sparsity constraints sda_ranking SDA with automatic feature ranking mgsda Multi-Group Sparse Discriminant Analysis lda_thomaz Modified LDA for high-dimensional data hdrda High-Dimensional Regularized Discriminant Analysis

Value

A list containing the model specification with the following components:

type

Model type: "Classification" or "Regression"

library

Required R package(s) for the model

label

Human-readable model name

parameters

Data frame describing tunable parameters

grid

Function to generate parameter tuning grid

fit

Function to fit the model

predict

Function to generate predictions

prob

Function to generate class probabilities (classification only)

See Also

MVPAModels for the complete list of available custom MVPA models

mvpa_model for using these models in MVPA analyses

Examples

# Load custom MVPA model
model <- load_model("sda_notune")

# Load correlation classifier with parameter tuning options
corr_model <- load_model("corclass")
print(corr_model$parameters)  # View tunable parameters

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