mcmc_tidiers {broom} | R Documentation |
Tidying methods for MCMC (Stan, JAGS, etc.) fits
tidyMCMC(x, pars, estimate.method = "mean", conf.int = FALSE, conf.level = 0.95, conf.method = "quantile", droppars = "lp__", rhat = FALSE, ess = FALSE, ...) ## S3 method for class 'rjags' tidy(x, pars, estimate.method = "mean", conf.int = FALSE, conf.level = 0.95, conf.method = "quantile", ...) ## S3 method for class 'stanfit' tidy(x, pars, estimate.method = "mean", conf.int = FALSE, conf.level = 0.95, conf.method = "quantile", droppars = "lp__", rhat = FALSE, ess = FALSE, ...)
x |
an object of class ‘"stanfit"’ |
pars |
(character) specification of which parameters to include |
estimate.method |
method for computing point estimate ("mean" or median") |
conf.int |
(logical) include confidence interval? |
conf.level |
probability level for CI |
conf.method |
method for computing confidence intervals ("quantile" or "HPDinterval") |
droppars |
Parameters not to include in the output (such as log-probability information) |
rhat, ess |
(logical) include Rhat and/or effective sample size estimates? |
... |
unused |
## Not run: # Using example from "RStan Getting Started" # https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started model_file <- system.file("extdata", "8schools.stan", package = "broom") schools_dat <- list(J = 8, y = c(28, 8, -3, 7, -1, 1, 18, 12), sigma = c(15, 10, 16, 11, 9, 11, 10, 18)) if (requireNamespace("rstan", quietly = TRUE)) { set.seed(2015) rstan_example <- stan(file = model_file, data = schools_dat, iter = 100, chains = 2) } ## End(Not run) if (requireNamespace("rstan", quietly = TRUE)) { # the object from the above code was saved as rstan_example.rda infile <- system.file("extdata", "rstan_example.rda", package = "broom") load(infile) tidy(rstan_example) tidy(rstan_example, conf.int = TRUE, pars = "theta") td_mean <- tidy(rstan_example, conf.int = TRUE) td_median <- tidy(rstan_example, conf.int = TRUE, estimate.method = "median") library(dplyr) library(ggplot2) tds <- rbind(mutate(td_mean, method = "mean"), mutate(td_median, method = "median")) ggplot(tds, aes(estimate, term)) + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) + geom_point(aes(color = method)) }