train_model.mvpa_model | R Documentation |
This function trains a Multi-Variate Pattern Analysis (MVPA) model on the provided data, taking care of feature selection, parameter tuning, and model fitting.
## S3 method for class 'mvpa_model'
train_model(obj, train_dat, y, indices, wts = NULL, ...)
obj |
An object of class |
train_dat |
Training data, an instance of class |
y |
The dependent variable (response variable), either a numeric vector or a factor. |
indices |
The spatial indices associated with each column. |
wts |
Optional class weights (if the underlying model supports it). |
... |
Additional arguments passed to other methods. |
A model fit object containing the trained model, its fit, the model type (classification or regression), the best tuning parameters, the voxel indices, and the feature mask.
dset_info <- gen_sample_dataset(D = c(8, 8, 8), nobs = 20,
response_type = "categorical", data_mode = "image", nlevels = 2)
cval <- blocked_cross_validation(dset_info$design$block_var)
sda_model <- load_model("sda_notune")
mspec <- mvpa_model(
model = sda_model,
dataset = dset_info$dataset,
design = dset_info$design,
model_type = "classification",
crossval = cval
)
train_model(mspec, dset_info$dataset$train_data,
dset_info$design$y_train,
indices = seq_len(ncol(dset_info$dataset$train_data)))
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