# Extract Residuals from `BayesSIM` Returns the model residuals based on the posterior fitted values of a `BayesSIM`. Residuals can be computed using either the posterior mean or median fitted values. ```r ## S3 method for class 'bsim' residuals(object, method = c("mean", "median"), ...) ``` ## Arguments - `object`: A fitted object of `BayesSIM` or individual model. - `method`: Character string specifying the summary statistic used to compute the fitted values. Options are `"mean"` or `"median"`. Default is `"mean"`. - `...`: Additional arguments passed to other methods. ## Returns A numeric vector of residuals. ($\mathbf{r} = \mathbf{Y} - \hat{\mathbf{Y}}$) $\hat{\mathbf{Y}}$ can be mean or median of MCMC samples. ## 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) ```

genBasic function