Fit Meta-Analysis Models

Low-level function that calls the C++ meta-analysis implementation. This is typically called internally by higher-level functions like fmri_meta().

Usage

fmri_meta_fit(
  Y,
  V,
  X,
  method = c("pm", "dl", "fe", "reml"),
  robust = c("none", "huber"),
  huber_c = 1.345,
  robust_iter = 2,
  n_threads = getOption("fmrireg.num_threads", 0)
)

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), or "reml" (REML, uses PM solver)

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 = use all available)

Value

List with components:

beta

Numeric matrix of coefficients (predictors x features)

se

Numeric matrix of standard errors (predictors x features)

z

Numeric matrix of z-scores (predictors x features)

tau2

Numeric vector of between-study variance estimates

Q_fe

Numeric vector of Q statistics from fixed-effects model

I2_fe

Numeric vector of I² statistics from fixed-effects model

df

Numeric vector of degrees of freedom

ok

Logical vector indicating successful fits

See also

fmri_meta