BayesSIM (version 1.0.0)

genBasic: Extract Residuals from BayesSIM

Description

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.

Usage

# S3 method for bsim
residuals(object, method = c("mean", "median"), ...)

Value

A numeric vector of residuals. (\(\mathbf{r} = \mathbf{Y} - \hat{\mathbf{Y}}\)) \(\hat{\mathbf{Y}}\) can be mean or median of MCMC samples.

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.

Examples

# \donttest{
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)

# }