# Plot Method for `BayesSIM` Produce diagnostic plots for a fitted Bayesian single-index model. ```r ## S3 method for class 'bsim' plot(x, method = c("mean", "median"), interval = TRUE, alpha = 0.95, ...) ## S3 method for class 'bsimPred' plot(x, ...) ``` ## Arguments - `x`: A fitted object of `BayesSIM` or individual model. - `method`: Character string specifying the summary used for the posterior fitted values. Options are `"mean"` or `"median"`. Default is `"mean"`. - `interval`: A logical value indicating whether a credible interval is included in the fitted plot. Default is `TRUE`. - `alpha`: Numeric value between 0 and 1 specifying the credible level. By default, `alpha = 0.95` produces a 95% credible interval. - `...`: Additional arguments passed to underlying plotting functions. ## Returns The output consists of two plots: 1. Observed vs Predicted plot : a diagnostic scatter plot comparing actual outcomes with posterior fitted values to visually assess model fit. 2. Fitted curve plot : posterior $\hat{y}$ as a function of the estimated single index, optionally with $100 \times \alpha$ % credible intervals. ## Details The function internally calls `predict()` on the fitted model object to obtain posterior summaries of $\hat{y}$. Predicted value of $y$ is $\hat{f}(X'\hat{\theta})$. * If `interval = TRUE`, the function requests posterior credible intervals and overlays them on the fitted curve. * If `interval = FALSE`, only the posterior mean or median curve is drawn. ## Examples ```r simdata2 <- data.frame(DATA1$X, y = DATA1$y) # 1. One tool version fit_one <- BayesSIM(y ~ ., data = simdata2, niter = 5000, nburnin = 1000, nchain = 1) # Check median index vector estimates with standard errors coef(fit_one, method = "median", se = TRUE) # Fitted index values of median prediction fitted(fit_one, type = "linpred", method = "median") # Residuals of median prediction residuals(fit_one, method = "median") # Summary of the model summary(fit_one) # Convergence diagnostics nimTraceplot(fit_one) # Goodness of fit GOF(fit_one) # Fitted plot plot(fit_one) # Prediction with 95% credible interval at new data newx <- data.frame(X1 = rnorm(10), X2 = rnorm(10), X3 = rnorm(10), X4 = rnorm(10)) pred <- predict(fit_one, newdata = newx, interval = "credible", level = 0.95) plot(pred) # 2. Split version models <- BayesSIM_setup(y ~ ., data = simdata2) Ccompile <- compileModelAndMCMC(models) nimSampler <- get_sampler(Ccompile) initList <- getInit(models) mcmc.out <- runMCMC(nimSampler, niter = 5000, nburnin = 1000, thin = 1, nchains = 1, setSeed = TRUE, inits = initList, summary = TRUE, samplesAsCodaMCMC = TRUE) # "fit_split" becomes exactly the same as the class of "fit_one" object and apply generic functions. fit_split <- as_bsim(models, mcmc.out) ``` ## See Also `predict.bsim()`, `summary.bsim()`

plot function