An environment containing custom classification models for MVPA analysis.
Format
An environment with the following models:
- corclass
Correlation-based classifier using template matching with options (pearson, spearman, kendall)
- corsim
Alias for corclass
- sda_notune
Shrinkage Discriminant Analysis (SDA) without parameter tuning
- sda_boot
SDA with bootstrap resampling and feature selection
- glmnet_opt
Elastic net classifier (glmnet) with optimized alpha/lambda via EPSGO
- sparse_sda
SDA with sparsity constraints and feature selection
- sda_ranking
SDA with feature ranking and selection via higher criticism
- mgsda
Multi-Group Sparse Discriminant Analysis
- lda_thomaz
Modified LDA classifier for high-dimensional data
- hdrda
High-Dimensional Regularized Discriminant Analysis
- spacenet_tvl1
Spatially-regularized sparse linear model with TV-L1 penalty for global whole-brain analysis
Details
Models are accessed via load_model(name). Each model specification includes fit, predict, and prob methods.
The spacenet_tvl1 model follows the SpaceNet formulation used in Nilearn.
References
Gramfort, A., Thirion, B., & Varoquaux, G. (2013). Identifying predictive regions from fMRI with TV-L1 prior. Pattern Recognition in Neuroimaging (PRNI), IEEE. https://inria.hal.science/hal-00839984
Examples
# Load simple SDA classifier
model <- load_model("sda_notune")
# Load correlation classifier
model <- load_model("corclass")