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Optimized Mixed Model Solver

Source: R/mixed_solve_optimized.R
mixed_solve_optimized.Rd

An optimized implementation of mixed model estimation that precomputes expensive matrix operations and can be reused across multiple voxels for significant performance improvements.

Usage

mixed_solve_optimized(
  X,
  Z,
  Y,
  K = NULL,
  workspace = NULL,
  compute_se = FALSE,
  n_threads = 0
)

Arguments

X

Fixed effects design matrix (n × p)

Z

Random effects design matrix (n × q)

Y

Response data - can be a vector (single voxel) or matrix (n × V for multiple voxels)

K

Kinship/covariance matrix for random effects (q × q). Defaults to identity.

workspace

Precomputed workspace (optional, will compute if NULL)

compute_se

Whether to compute standard errors (default: FALSE)

n_threads

Number of OpenMP threads for multi-voxel (0 = auto)

Value

List with estimated parameters and variance components

Examples

set.seed(1)
X <- matrix(1, 6, 1)
Z <- diag(6)
Y <- matrix(rnorm(12), 6, 2)
ws <- mixed_precompute(X, Z)
#> Workspace precomputed successfully:
#>   - n=6, p=1, q=6
#>   - Effective rank: 5
#>   - Using identity K: yes
fit <- mixed_solve_optimized(X, Z, Y, workspace = ws)
#> Using 4 OpenMP threads
names(fit)
#> [1] "beta"   "u"      "Vu"     "Ve"     "lambda"