Marginal grids, predictions, means, and comparisons

These helpers provide a small native marginal-quantities surface for Gaussian LMM fits. They cover the common population-level workflow: construct a reference grid, evaluate fixed-effect predictions, average them into marginal means, and compare those means by simple differences.

Usage

mm_grid(fit, specs, by = NULL, at = list(), cov.reduce = mean, ...)

# S3 method for class 'mm_lmm'
mm_grid(fit, specs, by = NULL, at = list(), cov.reduce = mean, ...)

mm_predictions(
  fit,
  grid = NULL,
  specs = NULL,
  by = NULL,
  at = list(),
  method = c("auto", "satterthwaite", "kenward_roger", "bootstrap", "asymptotic", "none"),
  level = 0.95,
  target = c("population"),
  scale = c("response", "link"),
  ...
)

# S3 method for class 'mm_lmm'
mm_predictions(
  fit,
  grid = NULL,
  specs = NULL,
  by = NULL,
  at = list(),
  method = c("auto", "satterthwaite", "kenward_roger", "bootstrap", "asymptotic", "none"),
  level = 0.95,
  target = c("population"),
  scale = c("response", "link"),
  ...
)

mm_means(
  fit,
  specs,
  by = NULL,
  at = list(),
  grid = NULL,
  method = c("auto", "satterthwaite", "kenward_roger", "bootstrap", "asymptotic", "none"),
  level = 0.95,
  weights = c("equal", "proportional"),
  target = c("population"),
  scale = c("response", "link"),
  ...
)

# S3 method for class 'mm_lmm'
mm_means(
  fit,
  specs,
  by = NULL,
  at = list(),
  grid = NULL,
  method = c("auto", "satterthwaite", "kenward_roger", "bootstrap", "asymptotic", "none"),
  level = 0.95,
  weights = c("equal", "proportional"),
  target = c("population"),
  scale = c("response", "link"),
  ...
)

mm_comparisons(
  fit,
  specs,
  by = NULL,
  at = list(),
  grid = NULL,
  comparison = c("difference", "ratio", "odds_ratio"),
  method = c("auto", "satterthwaite", "kenward_roger", "bootstrap", "asymptotic", "none"),
  level = 0.95,
  weights = c("equal", "proportional"),
  target = c("population"),
  scale = c("response", "link"),
  ...
)

# S3 method for class 'mm_lmm'
mm_comparisons(
  fit,
  specs,
  by = NULL,
  at = list(),
  grid = NULL,
  comparison = c("difference", "ratio", "odds_ratio"),
  method = c("auto", "satterthwaite", "kenward_roger", "bootstrap", "asymptotic", "none"),
  level = 0.95,
  weights = c("equal", "proportional"),
  target = c("population"),
  scale = c("response", "link"),
  ...
)

Arguments

fit: A fitted mm_lmm.
specs: Character vector, or a one-sided formula such as ~ trt or ~ trt | group, naming the displayed marginal dimensions.
by: Optional character vector of grouping variables for marginal summaries or pairwise comparisons.
at: Named list of fixed-predictor values to force in the grid.
cov.reduce: Function used to reduce numeric fixed predictors that are not explicitly gridded.
...: Reserved for future methods.
grid: Optional object returned by mm_grid().
method: Requested inference method, passed to contrast().
level: Confidence level for intervals computed from contrast standard errors.
target: Prediction target. Only "population" is implemented.
scale: Prediction scale. Gaussian LMMs have identical "link" and "response" scales.
weights: Averaging weights for mm_means() and mm_comparisons(). "equal" weights reference-grid cells equally; "proportional" weights cells by observed fixed-factor frequencies.
comparison: Comparison scale. Only "difference" is implemented.

Value

mm_grid() returns an mm_grid object. The other helpers return an mm_marginal_quantity object with a contract-shaped table.

Details

The returned tables use the mixedmodels.marginal_quantity_table row contract. Inference is routed through contrast() so rows retain the same method, status, reliability, estimability, and reason fields as fixed-effect contrasts. Ordinary full-rank LMMs use the versioned mixedmodels.fixed_effect_covariance_matrix payload for fixed-effect uncertainty; rank-deficient or otherwise uncertified fits surface explicit unavailable status and reasons instead of partial covariance numbers.