mcmcCoefPlot {BayesPostEst}

Coefficient Plots for MCMC Output

Description

Coefficient plots for MCMC output using ggplot2

Usage

mcmcCoefPlot(
  mod,
  pars = NULL,
  pointest = "mean",
  ci = 0.95,
  hpdi = FALSE,
  sort = FALSE,
  plot = TRUE,
  regex = FALSE
)

Arguments

mod

Bayesian model object generated by R2jags, rjags, R2WinBUGS, R2OpenBUGS, MCMCpack, rstan, rstanarm, and brms.

pars

a scalar or vector of the parameters you wish to include in the table. By default, mcmcCoefPlot includes all parameters saved in a model object. If a model has lots of samples and lots of saved parameters, not explicitly specifying a limited number of parameters to include via pars may take a long time or produce an unreadable plot. pars can either be a vector with the specific parameters to be included in the table e.g. pars = c("beta[1]", "beta[2]", "beta[3]"), or they can be partial names that will be matched using regular expressions e.g. pars = "beta" if regex = TRUE. Both of these will include beta[1], beta[2], and beta[3] in the plot. If pars is left blank, mcmcCoefPlot will exclude auxiliary parameters such as deviance from JAGS or lp__ from Stan.

pointest

a character indicating whether to use the mean or median for point estimates in the table.

ci

a scalar indicating the confidence level of the uncertainty intervals.

hpdi

a logical indicating whether to use highest posterior density intervals or equal tailed credible intervals to capture uncertainty; default FALSE.

sort

logical indicating whether to sort the point estimates to produce a caterpillar or dot plot; default FALSE.

plot

logical indicating whether to return a ggplot object or the underlying tidy DataFrame; default TRUE.

regex

use regular expression matching with pars?

Value

a ggplot object or a tidy DataFrame.

Author(s)

Rob Williams, [e-mail]

Examples

if (interactive()) {
## simulating data
set.seed(123456)
b0 <- 0.2 # true value for the intercept
b1 <- 0.5 # true value for first beta
b2 <- 0.7 # true value for second beta
n <- 500 # sample size
X1 <- runif(n, -1, 1)
X2 <- runif(n, -1, 1)
Z <- b0 + b1 * X1 + b2 * X2
pr <- 1 / (1 + exp(-Z)) # inv logit function
Y <- rbinom(n, 1, pr)
df <- data.frame(cbind(X1, X2, Y))

## formatting the data for jags
datjags <- as.list(df)
datjags$N <- length(datjags$Y)

## creating jags model
model <- function()  {
  
  for(i in 1:N){
    Y[i] ~ dbern(p[i])  ## Bernoulli distribution of y_i
    logit(p[i]) <- mu[i]    ## Logit link function
    mu[i] <- b[1] +
      b[2] * X1[i] +
      b[3] * X2[i]
  }
  
  for(j in 1:3){
    b[j] ~ dnorm(0, 0.001) ## Use a coefficient vector for simplicity
  }
}

params <- c("b")
inits1 <- list("b" = rep(0, 3))
inits2 <- list("b" = rep(0, 3))
inits <- list(inits1, inits2)

## fitting the model with R2jags
set.seed(123)
fit <- R2jags::jags(data = datjags, inits = inits,
                    parameters.to.save = params, n.chains = 2, n.iter = 2000,
                    n.burnin = 1000, model.file = model)

## generating coefficient plot with all non-auxiliary parameters
mcmcCoefPlot(fit)
}