BayesSIM (version 1.0.0)

coef.bsim: Extract Index Vector Coefficients from BayesSIM

Description

Computes posterior summaries of the single-index model index vector from a fitted BayesSIM. Users may choose either the posterior mean or median as the point estimate.

Usage

# S3 method for bsim
coef(object, method = c("mean", "median"), se = FALSE, ...)

Value

A numeric vector or data.frame of estimated coefficient and standard error of the index vector.

Arguments

object

A fitted object of BayesSIM or individual model.

method

Character string indicating the summary statistic to compute. Options are "mean" or "median". Default is "mean".

se

Logical value whether computing standard error for index estimates. If method is "mean", standard deviation of index vector MCMC samples is gained. If method is "median", median absolute deviation of index vector MCMC samples is gained. FALSE is default.

...

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)

# }