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Run a single linear mixed model and extract statistics

Source: R/lmm_helpers.R
single_lmm.Rd

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, y_name, model_template = NULL, weights = NULL)

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.

y_name

String with the outcome name (as specified in the formula)

model_template

Pre-compiled model object for faster estimation. single_lmm uses 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.

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).

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

See also

run_vw_lmm for the main interface.