# S3 Methods: update The `update` function provides a unified and convenient interface to refresh or modify existing objects generated by the `LCPA` package. It allows users to re-run model fitting or data simulation with new parameter settings while preserving all other original configurations. Supported classes include: `LCA`, `LPA`, `LCPA`, `LTA`, `sim.LCA`, `sim.LPA`, and `sim.LTA`. ```r update(x, ...) ## S3 method for class 'LCA' update(x, ...) ## S3 method for class 'LPA' update(x, ...) ## S3 method for class 'LCPA' update(x, ...) ## S3 method for class 'LTA' update(x, ...) ## S3 method for class 'sim.LCA' update(x, ...) ## S3 method for class 'sim.LPA' update(x, ...) ## S3 method for class 'sim.LTA' update(x, ...) ``` ## Arguments - `x`: An object of one of the following classes: * `LCA` — Latent Class Analysis model. * `LPA` — Latent Profile Analysis model. * `LCPA` — Latent Class Prediction Analysis (with covariates). * `LTA` — Latent Transition Analysis model. * `sim.LCA` — Simulated LCA dataset. * `sim.LPA` — Simulated LPA dataset. * `sim.LTA` — Simulated LTA dataset. - `...`: Additional named arguments passed to override or extend the original call. Valid arguments depend on the class of `x`: - **‘LCA’**: `response`, `L`, `par.ini`, `method`, `is.sort`, `nrep`, `vis`, `control.EM`, `control.Mplus`, `control.NNE` - **‘LPA’**: `response`, `L`, `par.ini`, `constraint`, `method`, `is.sort`, `nrep`, `vis`, `control.EM`, `control.Mplus`, `control.NNE` - **‘LCPA’**: `response`, `L`, `ref.class`, `type`, `covariates`, `CEP.error`, `par.ini`, `params`, `is.sort`, `constraint`, `method`, `tol`, `maxiter`, `nrep`, `starts`, `maxiter.wa`, `vis`, `control.EM`, `control.Mplus`, `control.NNE` - **‘LTA’**: `responses`, `L`, `ref.class`, `type`, `covariates`, `CEP.timeCross`, `CEP.error`, `covariates.timeCross`, `par.ini`, `params`, `is.sort`, `constraint`, `method`, `tol`, `maxiter`, `nrep`, `starts`, `maxiter.wa`, `vis`, `control.EM`, `control.Mplus`, `control.NNE` - **‘sim.LCA’**: `N`, `I`, `L`, `poly.value`, `IQ`, `distribution`, `params`, `is.sort` - **‘sim.LPA’**: `N`, `I`, `L`, `constraint`, `distribution`, `mean.range`, `covs.range`, `params`, `is.sort` - **‘sim.LTA’**: `N`, `I`, `L`, `times`, `type`, `rate`, `constraint`, `distribution`, `mean.range`, `covs.range`, `poly.value`, `IQ`, `covariates`, `beta`, `gamma`, `params`, `is.sort` ## Returns An object of the same class as `x`, reconstructed using the original arguments updated with any provided in `...`. All unchanged parameters are preserved from the original call. ## Details Internally, each method extracts the stored `arguments` list from the input object `x`, merges it with user-provided `...` using `modifyList`, then re-invokes the corresponding constructor function (`LCA()`, `LPA()`, `LCPA()`, `LTA()`, `sim.LCA()`, etc.) with the merged argument list. This ensures that: * Only explicitly overridden parameters are changed. * Default values from the original call remain intact. * Complex nested structures (e.g., control lists) can be partially updated. Note: If an invalid argument is passed (e.g., `constraint` to `LCA`), it will be silently ignored unless the underlying constructor validates inputs. ## Methods (by class) * `update(LCA)`: Update method for `LCA` objects * `update(LPA)`: Update method for `LPA` objects * `update(LCPA)`: Update method for `LCPA` objects * `update(LTA)`: Update method for `LTA` objects * `update(sim.LCA)`: Update method for `sim.LCA` objects * `update(sim.LPA)`: Update method for `sim.LPA` objects * `update(sim.LTA)`: Update method for `sim.LTA` objects ## Examples ```r library(LCPA) # --- Update LCA --- data <- sim.LCA(N=500, I=5, L=3) lca.obj <- LCA(data$response, L=3) lca.updated <- update(lca.obj, method="EM", nrep=5) # --- Update LPA --- data2 <- sim.LPA(N=300, I=4, L=2) lpa.obj <- LPA(data2$response, L=2, constraint="VE") lpa.updated <- update(lpa.obj, constraint="VV") # --- Update Simulation Objects --- sim.obj1 <- sim.LCA(N=1000) sim.obj1_updated <- update(sim.obj1, N=2000, IQ=0.8) sim.obj2 <- sim.LPA(I=6) sim.obj2_updated <- update(sim.obj2, I=8, mean.range=c(-2,2)) sim.obj3 <- sim.LTA(N=200, I=5, L=2, times=3) sim.obj3_updated <- update(sim.obj3, N=300, times=4, constraint="ER") ```

update function