# Mediated Causal Excursion Effects (configurable nuisance models) Like `mcee`, but each nuisance function is configured explicitly via `config_*` objects (formula/method/family or known). ```r mcee_general( data, id, dp, outcome, treatment, mediator, availability = NULL, time_varying_effect_form, config_p, config_q, config_eta, config_mu, config_nu, weight_per_row = NULL, verbose = TRUE ) ``` ## Arguments - `data`: A data.frame in long format (one row per id-by-decision point). - `id`: Character. Column name for subject identifier. - `dp`: Character. Column name for decision point index (must increase strictly within subject). - `outcome`: Character. Column name for distal outcome (constant within subject). - `treatment`: Character. Column name for treatment (coded 0/1). - `mediator`: Character. Column name for mediator. - `availability`: Optional character. Column name for availability (0/1). If `NULL`, all rows are treated as available. - `time_varying_effect_form`: RHS-only formula for the basis $f(t)$ (e.g., `~ 1`, `~ dp`, `~ poly(dp,2)`). - `config_p, config_q, config_eta, config_mu, config_nu`: Lists created by `mcee_config_maker()` or helpers (e.g., `mcee_config_glm()`, `mcee_config_known()`, `mcee_config_ranger()`, etc.). - `weight_per_row`: Optional numeric vector of row weights (nonnegative, length `nrow(data)`). If `NULL`, uniform within-id weights are used. - `verbose`: Logical; print progress messages. ## Returns An `"mcee_fit"` object; see `mcee`. ## Details Use this wrapper for observational studies (estimate `p`) or when you want different learners per nuisance. The same data requirements as `mcee` apply. ## Examples ```r set.seed(1) n <- 10 T <- 4 id <- rep(1:n, each = T) dp <- rep(1:T, times = n) A <- rbinom(n * T, 1, 0.5) M <- rbinom(n * T, 1, plogis(-0.2 + 0.3 * A + 0.1 * dp)) Y <- ave(0.5 * A + 0.6 * M + 0.1 * dp + rnorm(n * T), id) dat <- data.frame(id, dp, A, M, Y) cfg <- list( p = mcee_config_known("p", 0.5), q = mcee_config_glm("q", ~ dp + M), eta = mcee_config_glm("eta", ~dp), mu = mcee_config_glm("mu", ~ dp + M), nu = mcee_config_glm("nu", ~dp) ) fit_gen <- mcee_general(dat, "id","dp","Y","A","M", time_varying_effect_form = ~ dp, config_p=cfg$p, config_q=cfg$q, config_eta=cfg$eta, config_mu=cfg$mu, config_nu=cfg$nu) ``` ## See Also `mcee`, `mcee_userfit_nuisance`, `mcee_config_maker`

mcee_general function