mcmcReg {BayesPostEst}R Documentation

LaTeX or HTML regression tables for MCMC Output


This function creates LaTeX or HTML regression tables for MCMC Output using the texreg function from the texreg R package.


  pars = NULL,
  pointest = "mean",
  ci = 0.95,
  hpdi = FALSE,
  sd = FALSE,
  pr = FALSE,
  coefnames = NULL,
  gof = numeric(0),
  gofnames = character(0),
  format = "latex",
  regex = FALSE,



Bayesian model object generated by R2jags, rjags, R2WinBUGS, R2OpenBUGS, MCMCpack, rstan, rstanarm, and brms, or a list of model objects of the same class.


a scalar or vector of the parameters you wish to include in the table. By default, mcmcReg 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. 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 table. When combining models with different parameters in one table, this argument also accepts a list the length of the number of models.


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


a scalar indicating the confidence level of the uncertainty intervals.


a logical indicating whether to use highest posterior density intervals instead of equal tailed credible intervals to capture uncertainty (default FALSE).


a logical indicating whether to report the standard deviation of posterior distributions instead of an uncertainty interval (default FALSE). If TRUE, overrides ci, hpdi, and pr.


a logical indicating whether to report the probability that a coefficient is in the same direction as the point estimate for that coefficient (default FALSE). If TRUE, overrides ci and hpdi.


an optional vector or list of vectors containing parameter names for each model. If there are multiple models, the list must have the same number of elements as there are models, and the vector of names in each list element must match the number of parameters. If not supplied, the function will use the parameter names in the model object(s). Note that this replaces the standard custom.coef.names argument in texreg because there is no extract method for MCMC model objects, and many MCMC model objects do not have unique parameter names.


a named list of goodness of fit statistics, or a list of such lists.


an optional vector or list of vectors containing goodness of fit statistic names for each model. Like coefnames in this function (which replaces the custom.coef.names argument in texreg), gofnames replaces the standard custom.gof.names argument in texreg. If there are multiple models, the list must have the same number of elements as there are models, and the vector of names in each list element must match the number of goodness of fit statistics.


a character indicating latex or html output.


optional file name to write table to file instead of printing to console.


use regular expression matching with pars?


optional arguments to texreg.


If using with more than one model, you should rename the parameters in the model objects to ensure that different parameters with the same subscript are not conflated by texreg e.g. beta[1] could represent age in one model and income in another, and texreg would combine the two if you do not rename beta[1] to more informative names in the model objects.

If mod is a brmsfit object or list of brmsfit objects, note that the default brms names for coefficients are b_Intercept and b, so both of these should be included in par if you wish to include the intercept in the table.


A formatted regression table in LaTeX or HTML format.


Rob Williams,


if (interactive()) {
## simulating data
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
fit <- R2jags::jags(data = datjags, inits = inits,
           = params,
                    n.chains = 2,
                    n.iter = 2000, n.burnin = 1000,
                    model.file = model)

## generating regression table with all parameters

## generating regression table with only betas and custom coefficent names
mcmcReg(fit, pars = c('b'), coefnames = c('Variable 1',
                                          'Variable 2',
                                          'Variable 3'),
        regex = TRUE)
## generating regression tables with all betas and custom names
mcmcReg(fit, coefnames = c('Variable 1', 'Variable 2',
                           'Variable 3', 'deviance'))

[Package BayesPostEst version 0.3.2 Index]