Create an MVPA Model — mvpa

Create an MVPA model based on a caret-based classification or regression model.

Usage

mvpa_model(
  model,
  dataset,
  design,
  model_type = c("classification", "regression"),
  crossval = NULL,
  feature_selector = NULL,
  tune_grid = NULL,
  tune_reps = 15,
  performance = NULL,
  class_metrics = TRUE,
  compute_performance = TRUE,
  return_predictions = TRUE,
  return_fits = FALSE
)

Arguments

model: A caret-based classification or regression model.
dataset: An `mvpa_dataset` instance.
design: An `mvpa_design` instance.
model_type: A character string indicating the problem type: "classification" or "regression".
crossval: An optional `cross_validation` instance.
feature_selector: An optional `feature_selector` instance.
tune_grid: An optional parameter tuning grid as a `data.frame`.
tune_reps: The number of replications used during parameter tuning. Only relevant if `tune_grid` is supplied.
performance: An optional custom function for computing performance metrics.
class_metrics: A logical flag indicating whether to compute performance metrics for each class.
compute_performance: A logical indicating whether to compute and store performance measures for each voxel set (defaults to TRUE).
return_predictions: A logical indicating whether to return row-wise predictions for each voxel set (defaults to TRUE).
return_fits: A logical indicating whether to return the model fit for each voxel set (defaults to FALSE).

Details

If `performance` is supplied, it must be a function that takes one argument and returns a named list of scalar values. The argument the function takes is a class deriving from `classification_result` appropriate for the problem at hand. See example below.

Examples


mod <- load_model("sda")
traindat <- neuroim2::NeuroVec(array(rnorm(6*6*6*100), c(6,6,6,100)), neuroim2::NeuroSpace(c(6,6,6,100)))
mask <- neuroim2::LogicalNeuroVol(array(rnorm(6*6*6)>-.2, c(6,6,6)), neuroim2::NeuroSpace(c(6,6,6)))

mvdset <- mvpa_dataset(traindat,mask=mask)
#> Error in assert_that(inherits(train_data, "NeuroVec")): could not find function "assert_that"
design <- data.frame(fac=rep(letters[1:4], 25), block=rep(1:10, each=10))
cval <- blocked_cross_validation(design$block)
mvdes <- mvpa_design(design, ~ fac, block_var=~block)
#> Error in mvpa_design(design, ~fac, block_var = ~block): argument "y_train" is missing, with no default

custom_perf <- function(result) {
  c(accuracy=sum(result$observed == result$predicted)/length(result$observed))
}
mvpmod <- mvpa_model(mod, dataset=mvdset, design=mvdes, crossval=cval, performance=custom_perf)
#> Error in assert_that(!is.null(model$fit)): could not find function "assert_that"
ret <- run_searchlight(mvpmod)
#> Error: object 'mvpmod' not found
stopifnot("accuracy" %in% names(ret))
#> Error: "accuracy" %in% names(ret) is not TRUE