Fit a specified mediation model without simulation

Usage

wsmed_fit(
  model,
  data,
  missing = c("error", "listwise", "fiml", "mi"),
  mi = list(),
  fixed.x = FALSE,
  verbose = FALSE,
  standardized = FALSE
)

Arguments

model: A wsmed_model object.
data: A wide data frame with one row per participant.
missing: Missing-data strategy: error, listwise, fiml, or mi.
mi: MI controls: m, method, seed, or completed. Supply completed as a list of at least two completed wide data frames or a mice mids object to use externally generated imputations. m is then inferred (or must match). Omit method and seed with completed data. Inference has its own seed.
fixed.x: Whether exogenous moments are fixed in the SEM.
verbose: Show progress messages.
standardized: Logical default for effect extraction, summaries and plots. Does not change model fitting; inference inherits this setting.

Value

A wsmed_fit containing all fits and pooled estimates for MI.

Details

This release preserves the existing per-imputation centering and first-imputation reporting reference. It does not implement MI-bootstrap. Factor outcome, mediator, and within-subject covariate columns are unsupported. See wsmed_categorical for reference coding, numeric 0/1 predictors, and restrictions on missing categorical predictors. mi$method specifies the method for numeric variables; factors use logistic or multinomial regression. Default imputation uses main effects and need not preserve substantive-model interactions. For moderated models, consider model-compatible external imputation (e.g., SMC-FCS) and pass its completed datasets. Supplied datasets must preserve participant order, row names, observed values and factor levels, and complete all analysis variables. Difference scores and products are rebuilt separately in every dataset, then the same Rubin/MC engine is used. Validation does not establish imputation-model compatibility. Save the external imputer's settings, convergence diagnostics and software versions separately. Internal MI with moderator interactions signals one wsmed_mi_compatibility_warning per fit and records it in fit diagnostics. Numeric 0/1 moderators remain continuous in this interface; a message explains this choice. For categorical predictors in either interface, use unordered factors with explicit levels; the first level is the reference group. Type messages are retained as diagnostics$input_notes and do not change estimates or random-number state. Diagnostics include dataset-level convergence/admissibility, covariance checks, case counts, warnings, and fitted category counts. Complete-case matrix rank is descriptive, not an identification test for FIML. Counts below five receive a descriptive small-category flag, not a universal validity cutoff. Constant transformed variables are named in pre-fit errors. Non-converged MI datasets stop pooling; known inadmissible fits cannot enter inference.