It is customary for empirical researchers to estimate many models and then show their results side-by-side in a table. This package provides this capability for the models, where inference is done through MCMC. Built on top of Philip Leifield's texreg package, bayestable provides a way to present summaries of several models in one LaTex/HTML/Word table.
You can install the package from GitHub using devtools:
## install prerequisites
install.packages(ั("texreg","coda"))
## install bayestable
devtools::install_github("ananyevm/bayestable")
This is still an early development version, so expect some adventures.
Here, we consider a simple use case. Let's begin with loading packages and generating some fake data:
## Loading packages
library(rjags)
library(bayestable)
## Generating 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)
Here we have a simple linear data generating process. Our goal is to estimate three models: an intercept-only model, a model with predictor x1, and a model with both x1 and x2.
Let's define these models with JAGS:
## Intercept-only model
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)
}"
## Bivariate linear model
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)
}"
## Linear model with two predictors
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)
}"
Now, let's connect and compile the models
## Connecting and compiling
model1.spec <- textConnection(code1)
model2.spec <- textConnection(code2)
model3.spec <- textConnection(code3)
jags1 <- jags.model(model1.spec, data = list('N' = N, 'y' = y))
jags2 <- jags.model(model2.spec, data = list('x1' = x1,'N' = N, 'y' = y))
jags3 <- jags.model(model3.spec, data = list('x1' = x1,'x2' = x2,'N' = N, 'y' = y))
After the models are compiled, we can sample the coefficients (a, beta1, and beta2 -- depending on the model), and fitted values mu:
## Sampling coefficients
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)
Now, let's show the results of three models in one table using function bayes.table
First, we need to prepare lists to pass to the function
### List of the parameter samples
datalist<-list(samples1, samples2, samples3)
## List of outcomes for different models (here we used the same set of outcomes)
ylist<-list(y,y,y)
Let's generate a table with default settings (only generating plain text table instead of LaTex table):
bayes.table(datalist, ylist, output="word")
As a result, we get a table that looks like this:
==========================================================
Model 1 Model 2 Model 3
----------------------------------------------------------
a 0.18 0.16 0.11
[-0.04; 0.39] [-0.05; 0.35] [-0.09; 0.31]
beta1 0.43 0.43
[ 0.23; 0.63] [ 0.23; 0.61]
beta2 -0.30
[-0.50; -0.11]
----------------------------------------------------------
Num. obs. 100 100 100
Eff. Size 1000.00 1000.00 814.71
Geweke Diag. 1.04 2.02 1.01
==========================================================
Means of posterior distributions are shown as point estimates, and 95-percent Highest Probability Density intervals are shown below the point estimates. Also, for every model, the table shows number of observations, effective sample size of MCMC draws, and maximum absolute value of Geweke diagnostic.
The package provides several options to customize the table. First, if we include the list of the draws of fitted values, we can calculate R-squared for each of the models. Secondly, we can give our variables meaningful names. Thirdly, we can show standard errors of posterior distributions of the coefficients instead of HPD intervals.
The code would like like this:
bayes.table(datalist, ylist, yreplist, HPDI=F,
custom.coef.map = list("a"="Intercept",
"beta1"="Ruggedness",
"beta2"="GDP"),
include.rsquared = T,
output="word")
The output would look like:
=========================================
Model 1 Model 2 Model 3
-----------------------------------------
Intercept 0.18 0.16 0.11
(0.11) (0.11) (0.10)
Ruggedness 0.43 0.43
(0.11) (0.10)
GDP -0.30
(0.10)
-----------------------------------------
Num. obs. 100 100 100
R^2 0.00 0.16 0.23
Eff. Size 1000.00 1000.00 814.71
Geweke Diag. 1.04 2.02 1.01
=========================================
Other things that can be customized: inclusion of any of the goodness-of-fit and convergence metric, names of the models, caption, probability for the HPD intervals. Please consult the manual pages for options.
If you fitted your model using Stan sampler, then you need to convert the output into coda::mcmc.list object first. This can be done using the code from Ben Goodrich
Consider the following example:
First, let's write a simple linear model with Stan .
stan_code<-"
data {
int N;
real y[N];
real x1[N];
real x2[N];
}
parameters {
real a;
real beta1;
real beta2;
real<lower=0> sigma;
}
transformed parameters {
vector[N] mu;
for (i in 1:N){
mu[i] = a + beta1*x1[i] + beta2*x2[i];
}
}
model {
y ~ normal(mu,sigma);
}
"
Let's compile the model and sample both the coefficients (a, beta1, and beta2) and fitted values (mu)
fit1<-stan(model_code = stan_code, data = list(N=100, y=y, x1=x1, x2=x2),
pars = c("a","beta1","beta2", "mu"))
Then, we need to transform stanfit object into coda::mcmc.list object.
## transforming stanfit object into mcmc.list object
library(coda)
samples<- mcmc.list(lapply(1:ncol(fit1), function(x) mcmc(as.array(fit1)[,x,])))
## because we have three parameters, and 100 observations,
## we know that first three chains are the chains for the parameters
coef.samples <- samples[,1:3]
## and chains from the 4 to 103 are the fitted values
fitted.samples <- samples[,4:103]
Now, we can call bayes.table
bayes.table(list(coef.samples), list(y), list(fitted.samples), include.rsquared=T, output="word")
You should get something like this:
============================
Model 1
----------------------------
a 0.14
[-0.09; 0.37]
beta1 0.30
[ 0.07; 0.51]
beta2 -0.41
[-0.67; -0.14]
----------------------------
Num. obs. 100
R^2 0.13
Eff. Size 4374.56
Geweke Diag. 2.13
============================
You can combine many models in one table in this way. You do not need to include samples of fitted values if you do not want r-squared to be included (it is not included by default).
That's it. Feel free to report issues an request new features!
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent