Fit ITEM decoder weights

Fit ITEM weights with a ridge-stabilized generalized least-squares solve.

Usage

item_fit(
  Gamma_train,
  T_train,
  U_train,
  ridge = 0,
  method = c("chol", "svd", "pinv"),
  tol = sqrt(.Machine$double.eps)
)

Arguments

Gamma_train

Numeric matrix (n_train x n_features).

T_train

Numeric target matrix (n_train x p).

U_train

Trial covariance (n_train x n_train) or run-block list.

ridge

Non-negative ridge term added to the left-hand system.

method

Preferred solver path ("chol", "svd", "pinv").

tol

Numerical tolerance for rank/solver fallbacks.

Value

Numeric weight matrix W_hat (n_features x p) with item_diagnostics attribute.

Examples

Gamma_train <- matrix(
  c(1, 0,
    0.9, 0.1,
    0.1, 0.9),
  ncol = 2,
  byrow = TRUE
)
T_train <- rbind(c(1, 0), c(1, 0), c(0, 1))
W_hat <- item_fit(Gamma_train, T_train, diag(3))
item_predict(Gamma_train, W_hat)
#>             [,1]        [,2]
#> [1,] 1.054794521 -0.05479452
#> [2,] 0.938356164  0.06164384
#> [3,] 0.006849315  0.99315068