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Fit Meta-Analysis and return packed covariance per voxel

Source: R/meta_fit.R
fmri_meta_fit_cov.Rd

Fit Meta-Analysis and return packed covariance per voxel

Usage

fmri_meta_fit_cov(
  Y,
  V,
  X,
  method = c("pm", "dl", "fe", "reml"),
  robust = c("none", "huber"),
  huber_c = 1.345,
  robust_iter = 2,
  n_threads = .fmrireg_num_threads()
)

Arguments

Y

Numeric matrix of effect sizes (subjects x features)

V

Numeric matrix of variances (subjects x features)

X

Numeric matrix; design matrix (subjects x predictors), including intercept

method

Character scalar; meta-analysis method: "pm" (Paule-Mandel), "dl" (DerSimonian-Laird), "fe" (fixed-effects). "reml" is not implemented: it is accepted for backward compatibility, runs Paule-Mandel with a message, and the result records method = "pm".

robust

Character scalar; robust estimation method: "none" or "huber"

huber_c

Numeric scalar; tuning constant for Huber M-estimator (default: 1.345). Smaller values provide more robust estimates but may reduce efficiency.

robust_iter

Integer scalar; number of IRLS iterations for robust estimation (default: 2)

n_threads

Integer scalar; number of OpenMP threads (0 = OpenMP default). Defaults to options(fmrireg.num_threads), else the environment variable FMRIREG_NUM_THREADS, else 0.

Value

List with base outputs and cov_tri (tsize x P) where tsize = K*(K+1)/2