Run a single linear mixed model and extract statistics

Fits a linear mixed model to a single vertex outcome using lme4::lmer() and extracts fixed effects statistics. This function is called repeatedly across all cortical vertices during vertex-wise analysis.

Usage

single_lmm(
  imp,
  y,
  formula,
  model_template = NULL,
  weights = NULL,
  lmm_control = lme4::lmerControl()
)

Arguments

imp

A data.frame containing the phenotype dataset (in verywise format).

y

A numeric vector of outcome values representing a single vertex from the super-subject matrix.

formula

An R formula object describing the linear mixed model using lme4 notation.

model_template

Optional pre-compiled model object for faster estimation. When provided, single_lmm will use an "update"-based workflow instead of refitting the model from scratch. This minimizes repeated parsing and model construction overhead, significantly reducing computation time for large-scale vertex-wise analyses. Default: NULL.

weights

Optional string or numeric vector of weights for the linear mixed model. You can use this argument to specify inverse-probability weights. If this is a string, the function look for a column with that name in the phenotype data. Note that these are not normalized or standardized in any way. Default: NULL (no weights).

lmm_control

Optional list (of correct class, resulting from lmerControl() containing control parameters to be passed to lme4::lmer() (e.g. optimizer choice, convergence criteria, see the *lmerControl documentation for details. Default: (uses default settings).

Value

A list with two elements:

• stats - A data.frame with columns:

  • term - Fixed effect term names

  • qhat - Parameter estimates

  • se - Standard errors

• resid - A numeric vector of model residuals

• warning - Warning message(s) if any

Details

Additional parameters are currently passed to the lme4::lmer call using the lmm_control argument.

See also

run_vw_lmm for the main interface.