# Prepare smooth latent grids + model-based item curves (continuous, ordinal, or mixed) Unified, robust wrapper that constructs synthetic latent-score grids and model-based item curves for fitted `lavaan`/`blavaan` models. It tries both internal branches and returns whichever applies: * Continuous branch (for continuous indicators) and * Ordinal branch (for ordinal indicators). If both succeed (mixed models), their outputs are joined on shared ID columns. The function is designed to avoid manual handling of factor-score data or group columns; that logic is delegated to sub-functions. ```r prepare( fit, data = NULL, info = NULL, plan = c("auto", "none", "multisession", "multicore", "sequential", "cluster"), workers = NULL, cluster = NULL, ... ) ``` ## Arguments - `fit`: A fitted `lavaan`/`blavaan` object. - `data`: Optional factor-score table to reuse (either a single data frame or a per-group list) as typically returned by `lavPredict_parallel()`. If `NULL`, factor scores are obtained on demand by the sub-functions. - `info`: Optional list from `model_info(fit)`. If `NULL`, it is computed once and forwarded to both branches. - `plan`: Parallelization backend for the ordinal branch; one of `c("auto", "none", "multisession", "multicore", "sequential", "cluster")`. The continuous branch is fast and does not use futures. - `workers`: Optional integer; number of workers used by future backends (ignored unless `plan` uses a parallel backend). Forwarded to the ordinal branch. - `cluster`: Optional external cluster object created by `parallel::makeCluster()`; only used when `plan = "cluster"`. If supplied with a different `plan`, it is ignored. - `...`: Additional arguments passed unchanged to both sub-functions (e.g., `level`, `vcov_type`, `length.out`, `other_latents`, `latent_var_as_factor`, `se`, `se_summary`). ## Returns A tibble. For mixed models, it is the full join of the two branches using shared ID columns (see Merging ). For single-type models, it is the single applicable branch. ## Details Routing * Calls internal functions `.prepare_continuous()` and `.prepare_ordinal()` for continuous and ordinal items, respectively. * Reuses `info` (from `model_info()`) and `data` (from `lavPredict_parallel()`) when provided, so each is computed at most once. Merging * If only one branch applies, returns that result. * If both apply, performs a `dplyr::full_join()` using shared keys among `c(".rid", ".gid", ".group", ".latent_var")` when available, otherwise on the intersection of column names. * To prevent duplicated factor-score columns (e.g., `A.x`, `E.y`), the latent columns are kept from the continuous branch and dropped from the ordinal branch before the join. Column semantics * Leading ID columns: `.rid`, `.gid`, `.group`, `.latent_var`. * Latent columns follow directly after `.latent_var`. * Continuous/ordinal model-based outputs use the common `m_est_*`, `m_lwr_*`, `m_upr_*` naming convention. ## Parallelization * The function may set a safe fallback to `sequential` when executed inside a worker to prevent nested futures. External clusters are respected only with `plan = "cluster"`. `workers` is a hint for future backends and is ignored otherwise. ## Errors and warnings * If neither branch succeeds, the function aborts with a diagnostic message. * When continuous and ordinal outputs have no shared columns, a row bind is returned with a `.source` column and a warning is issued. ## Examples ```r # --- Continuous example -------------------------------------------------- HS.model <- 'visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9' fit <- lavaan::cfa(HS.model, data = lavaan::HolzingerSwineford1939, meanstructure = TRUE) prepare(fit) # --- Ordinal example (discretize by quantiles; 5 ordered categories) ----- ord_items <- paste0("x", 1:9) HS_ord <- lavaan::HolzingerSwineford1939 for (v in ord_items) { q <- stats::quantile(HS_ord[[v]], probs = seq(0, 1, length.out = 6), na.rm = TRUE) q <- unique(q) # guard against duplicate cut points HS_ord[[v]] <- as.ordered(cut(HS_ord[[v]], breaks = q, include.lowest = TRUE)) } fit_ord <- lavaan::cfa( HS.model, data = HS_ord, ordered = ord_items, estimator = "WLSMV", parameterization = "delta", meanstructure = TRUE ) prepare(fit_ord) # --- Mixed example (x1–x3 ordinal, others continuous) -------------------- mix_ord <- c("x1","x2","x3") HS_mix <- lavaan::HolzingerSwineford1939 for (v in mix_ord) { q <- stats::quantile(HS_mix[[v]], probs = seq(0, 1, length.out = 6), na.rm = TRUE) q <- unique(q) HS_mix[[v]] <- as.ordered(cut(HS_mix[[v]], breaks = q, include.lowest = TRUE)) } fit_mix <- lavaan::cfa( HS.model, data = HS_mix, ordered = mix_ord, estimator = "WLSMV", parameterization = "delta", meanstructure = TRUE ) prepare(fit_mix) ``` ## See Also Internal helpers .prepare_continuous() and .prepare_ordinal() (not exported); see also `model_info()` and `lavPredict_parallel()`. Other lavDiag-augmenters: `augment()`, `item_data()`

prepare function