fixed_effects_from_average_outcome: Calculate Fixed-Effects Coefficients from Mean Outcomes

In PowRPriori: Power Analysis via Data Simulation for (Generalized) Linear Mixed Effects Models

Calculate Fixed-Effects Coefficients from Mean Outcomes

Description

A user-friendly helper function to translate expected outcomes (e.g., cell means, probabilities, or rates) into the regression coefficients required by the simulation. This is often more intuitive than specifying coefficients directly.

Usage

fixed_effects_from_average_outcome(
  formula,
  outcome,
  center = TRUE,
  family = "gaussian"
)

Arguments

formula	The fixed-effects part of the model formula (e.g., y ~ group * time).
outcome	A data frame containing columns for all predictor variables and exactly one column for the expected outcome values.
center	If TRUE (default), predictors in the design grid are effect-coded (i.e. the contrast coding is changed so that they are treated as being centered) to yield a grand-mean intercept. Additionally, the output is flagged to instruct power_sim() to automatically center the simulated data. If set to FALSE, the predictors are treated as not centered and no alterations to the contrast coding are applied.
family	The model family ("gaussian", "binomial", "poisson"). The outcome values should be means for gaussian, probabilities (0-1) for binomial, and non-negative rates/counts for poisson.

Details

By default, this function applies effect coding (orthogonal contrasts) to the predictors in your design (center = TRUE). Because the predictors are treated as being mean-centered this way, the intercept of the calculated coefficients represents the grand mean of your specified outcomes.

Importantly, setting center = TRUE also adds an attribute to the resulting coefficient list. When the coefficients are later passed to power_sim(), the simulation engine will detect this attribute and automatically apply the corresponding mean-centering to the generated sample data (grand-mean centering for between-subject variables, and within-cluster / person-mean centering for within-subject variables).

The choices for these respective centering techniques have been made to ensure the most robust fixed / random effects and power estimates. For more details on the issue of centering, refer to the vignette: vignette("Workflow-Example", package = "PowRPriori")

Value

A named list of coefficients suitable for the fixed_effects argument in power_sim().

Examples

outcome_means <- tidyr::expand_grid(
  group = c("Control", "Treatment"),
  time = c("pre", "post")
)
outcome_means$mean <- c(10, 10, 12, 15) # Specify expected means

#Per default, the predictors are effect-coded (centered) here
fixed_effects_from_average_outcome(
  formula = score ~ group * time,
  outcome = outcome_means
)

PowRPriori documentation built on May 12, 2026, 5:06 p.m.