bayes.table: Put results of MCMC sampling into a nicely formatted table...
In ananyevm/bayestable:

Description Usage Arguments Value Examples

View source: R/bayes_table.r

Put results of MCMC sampling into a nicely formatted table for Latex, HTML, or plaint text. It processes MCMC output and the outcome data. This function is built on top of Philip Leifeld's texreg package.

bayes.table(datalist, ylist, yreplist = NULL, include.nobs = TRUE,
  include.rsquared = FALSE, include.eff.size = TRUE,
  include.geweke = TRUE, custom.coef.map = NULL,
  custom.model.names = NULL, HPDI = TRUE, HPDI.prob = 0.95,
  output = "latex", caption = "")

`datalist`	a list of coda::mcmc.list objects from different models
`ylist`	a list of vectors of the instances of a dependent variable
`yreplist`	(optional) a list coda::mcmc.list object with the samples of predicted values
`include.nobs`	TRUE if number of observations should be included, FALSE otherwisr
`include.eff.size`	TRUE if effective size of MCMC chain should be included, FALSE otherwise
`include.geweke`	if TRUE, Geweke diagnostic is calculated for all the chains, and the largest value is included
`custom.coef.map`	list of new names for the variables, should be in the form `list("oldname1"="newname1", "oldname2"="newname2")`. Variables that are not in this mapping are not included. This is passed directly to `texreg` function
`custom.model.names`	a vector of model labels. This is passed directly to `texreg` function
`HPDI`	if TRUE, Highest Probability Density Intervals are shown instead of standard errors. Default is TRUE
`HPDI.prob`	probability for HPD intervals. Defauld is 0.95
`output`	output format: can be latex, html, or word. Default is latex
`caption`	a caption for the table. This is passed directly to `texreg` function
`include.rquared`	TRUE if quasi-bayesian r-squared should be included, FALSE otherwise

a character string that contains Late, HTML, or plain text for the rable

library(rjags)
library(coda)
library(texreg)
library(bayestable)

## Generating some fake data
N <- 100
x1 <- rnorm(N)
x2 <- rnorm(N)
beta <-c (0.3,-0.3)
y <- 0.1 + beta[1]*x1 + beta[2]*x2 + rnorm(N)

## Define three JAGS models
code1<-"
  model{
    for (i in 1:N){
      y[i] ~ dnorm(mu[i], sigma)
      mu[i] <- a
    }
   a ~ dnorm(0, 0.001)
   sigma <- pow(tau, -2)
   tau ~ dunif(0,100)
}"

code2<-"
 model{
   for (i in 1:N){
     y[i] ~ dnorm(mu[i], sigma)
     mu[i]<-a+beta1*x1[i]
   }
   a ~ dnorm(0, 0.001)
   beta1 ~ dnorm(0,0.001)
   sigma <- pow(tau, -2)
   tau ~ dunif(0,100)
}"

code3<-"
  model{
   for (i in 1:N){
    y[i] ~ dnorm(mu[i], sigma)
    mu[i]<-a + beta1*x1[i] + beta2*x2[i]
  }
  a ~ dnorm(0, 0.001)
  beta1 ~ dnorm(0,0.001)
  beta2 ~ dnorm(0,0.001)
  sigma <- pow(tau, -2)
  tau ~ dunif(0,100)
}"
## connecting to the models
model1.spec<-textConnection(code1)
model2.spec<-textConnection(code2)
model3.spec<-textConnection(code3)

## compiling the code into JAGS models
jags1<-jags.model(model1.spec, data = list('N'=N, 'y'=y), quiet = T)
jags2<-jags.model(model2.spec, data = list('x1'=x1,'N'=N, 'y'=y), quiet = T)
jags3<-jags.model(model3.spec, data = list('x1'=x1,'x2'=x2,'N'=N, 'y'=y), quiet = T)

## Sampling regression parameters
samples1<-coda.samples(jags1, variable.names = c("a"), n.iter=1000,nchain=4)
samples2<-coda.samples(jags2, variable.names = c("a", "beta1"), n.iter=1000,nchain=4)
samples3<-coda.samples(jags3, variable.names = c("a", "beta1", "beta2"),n.iter=1000,nchain=4)

## sampling fitted values
samples1.rep<-coda.samples(jags1, variable.names = c("mu"), n.iter=1000,nchain=4)
samples2.rep<-coda.samples(jags2, variable.names = c("mu"), n.iter=1000,nchain=4)
samples3.rep<-coda.samples(jags3, variable.names = c("mu"), n.iter=1000,nchain=4)

#' ## creating a list of samples from three different models
datalist<-list(samples1, samples2, samples3)

## creating a list of outcomes for different models (here we used the same set of outcomes)
ylist<-list(y,y,y)

## creating a list of samples of fitted values from different models
yreplist<-list(samples1.rep, samples2.rep, samples3.rep)

## generating table
bayes.table(datalist, ylist, yreplist,
           custom.coef.map = list("a" = "Intercept",
                                   "beta1" = "GDP",
                                   "beta2" = "Polity"),
           include.rsquared = T, HPDI.prob = 0.97)