Load a Pre-defined MVPA Model
load_model.Rd
Retrieves a model specification from the pre-defined set of MVPA models.
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
#> parameters class label
#> 1 method character correlation type: pearson, spearman, or kendall
#> 2 robust logical mean or huber