mcmcCoefPlot {BayesPostEst} | R Documentation |
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, |
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 |
sort |
logical indicating whether to sort the point estimates to produce
a caterpillar or dot plot; default |
plot |
logical indicating whether to return a |
regex |
use regular expression matching with |
Value
a ggplot
object or a tidy DataFrame.
Author(s)
Rob Williams, jayrobwilliams@gmail.com
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)
}