Create an MVPA Model

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
)

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.

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)
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)

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)
ret <- run_searchlight(mvpmod)
#> INFO [2024-04-23 13:39:49] model is: sda
#> INFO [2024-04-23 13:39:49] running randomized searchlight with 8 radius and 4 iterations
#> INFO [2024-04-23 13:39:49] searchlight iteration: 1
#> INFO [2024-04-23 13:39:49] mvpa_iterate: compute analysis for batch 1 ...
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9919 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.8768 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9139 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9382 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9521 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9832 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> INFO [2024-04-23 13:39:50] searchlight iteration: 2
#> INFO [2024-04-23 13:39:50] mvpa_iterate: compute analysis for batch 1 ...
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9822 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.8811 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.906 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9376 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9457 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9706 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.99 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Number of variables: 123 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 123 features.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> INFO [2024-04-23 13:39:50] mvpa_iterate: compute analysis for batch 2 ...
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.6666 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.8191 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.599 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9471 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> Number of variables: 4 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.8865 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 4 features.
#> INFO [2024-04-23 13:39:50] searchlight iteration: 3
#> INFO [2024-04-23 13:39:51] mvpa_iterate: compute analysis for batch 1 ...
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9919 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.8768 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9139 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9382 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9521 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9832 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> INFO [2024-04-23 13:39:51] searchlight iteration: 4
#> INFO [2024-04-23 13:39:51] mvpa_iterate: compute analysis for batch 1 ...
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9919 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.8768 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9139 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9382 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9521 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 0.9832 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Number of variables: 127 
#> Number of observations: 90 
#> Number of classes: 4 
#> 
#> Estimating optimal shrinkage intensity lambda.freq (frequencies): 1 
#> Estimating variances (pooled across classes)
#> Estimating optimal shrinkage intensity lambda.var (variance vector): 1 
#> 
#> 
#> Computing inverse correlation matrix (pooled across classes)
#> Specified shrinkage intensity lambda (correlation matrix): 0 
#> Prediction uses 127 features.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> Warning: Estimated correlation matrix doesn't have full rank - pseudoinverse used for inversion.
#> INFO [2024-04-23 13:39:52] number of models fit: 5
stopifnot("accuracy" %in% names(ret))