This R package accompanies a course and book on Bayesian data analysis (McElreath 2016. Statistical Rethinking. CRC Press.). It contains tools for conducting both MAP estimation and Hamiltonian Monte Carlo (through RStan - mc-stan.org). These tools force the user to specify the model as a list of explicit distributional assumptions. This is more tedious than typical formula-based tools, but it is also much more flexible and powerful.
For example, a simple Gaussian model could be specified with this list of formulas:
f <- alist(
y ~ dnorm( mu , sigma ),
mu ~ dnorm( 0 , 10 ),
sigma ~ dcauchy( 0 , 1 )
)
The first formula in the list is the likelihood; the second is the prior for mu; the third is the prior for sigma (implicitly a half-Cauchy, due to positive constraint on sigma).
You can find a manual with expanded installation and usage instructions here: http://xcelab.net/rm/software/
Here's the brief verison.
You'll need to install rstan first. Go to http://mc-stan.org and follow the instructions for your platform. The biggest challenge is getting a C++ compiler configured to work with your installation of R. The instructions at https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started are quite thorough. Obey them, and you'll likely succeed.
Then you can install rethinking from within R using:
install.packages(c("coda","mvtnorm","devtools","loo"))
library(devtools)
devtools::install_github("rmcelreath/rethinking")
If there are any problems, they likely arise when trying to install rstan, so the rethinking package has little to do with it. See the manual linked above for some hints about getting rstan installed. But always consult the RStan section of the website at mc-stan.org for the latest information on RStan.
To use maximum a posteriori (MAP) fitting:
library(rethinking)
f <- alist(
y ~ dnorm( mu , sigma ),
mu ~ dnorm( 0 , 10 ),
sigma ~ dcauchy( 0 , 1 )
)
fit <- map(
f ,
data=list(y=c(-1,1)) ,
start=list(mu=0,sigma=1)
)
The object fit holds the result. For a summary of marginal posterior distributions, use summary(fit) or precis(fit):
Mean StdDev 2.5% 97.5%
mu 0.00 0.59 -1.16 1.16
sigma 0.84 0.33 0.20 1.48
The same formula list can be compiled into a Stan (mc-stan.org) model:
fit.stan <- map2stan(
f ,
data=list(y=c(-1,1)) ,
start=list(mu=0,sigma=1)
)
The start list is optional, provided a prior is defined for every parameter. In that case, map2stan will automatically sample from each prior to get starting values for the chains. The chain runs automatically, provided rstan is installed. The plot method will display trace plots for the chains.
The Stan code can be accessed by using stancode(fit.stan):
data{
int<lower=1> N;
real y[N];
}
parameters{
real mu;
real<lower=0> sigma;
}
model{
mu ~ normal( 0 , 10 );
sigma ~ cauchy( 0 , 1 );
y ~ normal( mu , sigma );
}
generated quantities{
real dev;
dev <- 0;
dev <- dev + (-2)*normal_log( y , mu , sigma );
}
To run multiple chains in parallel on multiple cores, use the cores argument:
fit.stan <- map2stan(
f ,
data=list(y=c(-1,1)) ,
start=list(mu=0,sigma=1) ,
chains=4 , cores=4 , iter=2000 , warmup=1000
)
The parallel package is used here, relying upon mclapply (Mac, UNIX) or parLapply (Windows). It is best to run parallel operations in the Terminal/Command Prompt, as GUI interfaces sometimes crash when forking processes.
Both map and map2stan model fits can be post-processed to produce posterior distributions of any linear models and posterior predictive distributions.
link is used to compute values of any linear models over samples from the posterior distribution.
sim is used to simulate posterior predictive distributions, simulating outcomes over samples from the posterior distribution of parameters. See ?link and ?sim for details.
postcheck automatically computes posterior predictive (retrodictive?) checks for each case used to fit a model.
While map is limited to fixed effects models for the most part, map2stan can specify multilevel models, even quite complex ones. For example, a simple varying intercepts model looks like:
f2 <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + aj[group],
aj[group] ~ dnorm( 0 , sigma_group ),
a ~ dnorm( 0 , 10 ),
sigma ~ dcauchy( 0 , 1 ),
sigma_group ~ dcauchy( 0 , 1 )
)
And with varying slopes as well:
f3 <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + aj[group] + (b + bj[group])*x,
c(aj,bj)[group] ~ dmvnorm( 0 , Sigma_group ),
a ~ dnorm( 0 , 10 ),
b ~ dnorm( 0 , 1 ),
sigma ~ dcauchy( 0 , 1 ),
Sigma_group ~ inv_wishart( 3 , diag(2) )
)
The inv_wishart prior in the model just above is conventional, but not appealing. Since Stan does not use Gibbs sampling, there is no advantage to the inv_wishart prior.
To escape these conventional priors, map2stan supports decomposition of covariance matrices into vectors of standard deviations and a correlation matrix, such that priors can be specified independently for each:
f4 <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + aj[group] + (b + bj[group])*x,
c(aj,bj)[group] ~ dmvnorm2( 0 , sigma_group , Rho_group ),
a ~ dnorm( 0 , 10 ),
b ~ dnorm( 0 , 1 ),
sigma ~ dcauchy( 0 , 1 ),
sigma_group ~ dcauchy( 0 , 1 ),
Rho_group ~ dlkjcorr(2)
)
Here is a non-centered parameterization that moves the scale parameters in the varying effects prior to the linear model, which is often more efficient for sampling:
f4u <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + zaj[group]*sigma_group[1] +
(b + zbj[group]*sigma_group[2])*x,
c(zaj,zbj)[group] ~ dmvnorm( 0 , Rho_group ),
a ~ dnorm( 0 , 10 ),
b ~ dnorm( 0 , 1 ),
sigma ~ dcauchy( 0 , 1 ),
sigma_group ~ dcauchy( 0 , 1 ),
Rho_group ~ dlkjcorr(2)
)
Chapter 13 of the book provides a lot more detail on this issue.
We can take this strategy one step further and remove the correlation matrix, Rho_group, from the prior as well. map2stan facilitates this form via the dmvnormNC density, which uses an internal Cholesky decomposition of the correlation matrix to build the varying effects. Here is the previous varying slopes model, now with the non-centered notation:
f4nc <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + aj[group] + (b + bj[group])*x,
c(aj,bj)[group] ~ dmvnormNC( sigma_group , Rho_group ),
a ~ dnorm( 0 , 10 ),
b ~ dnorm( 0 , 1 ),
sigma ~ dcauchy( 0 , 1 ),
sigma_group ~ dcauchy( 0 , 1 ),
Rho_group ~ dlkjcorr(2)
)
Internally, a Cholesky factor L_Rho_group is used to perform sampling. It will appear in the returned samples, in addition to Rho_group, which is constructed from it.
It is possible to code simple Bayesian imputations. For example, let's simulate a simple regression with missing predictor values:
N <- 100
N_miss <- 10
x <- rnorm( N )
y <- rnorm( N , 2*x , 1 )
x[ sample(1:N,size=N_miss) ] <- NA
That removes 10 x values. Then the map2stan formula list just defines a distribution for x:
f5 <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + b*x,
x ~ dnorm( mu_x, sigma_x ),
a ~ dnorm( 0 , 100 ),
b ~ dnorm( 0 , 10 ),
mu_x ~ dnorm( 0 , 100 ),
sigma_x ~ dcauchy(0,2),
sigma ~ dcauchy(0,2)
)
m5 <- map2stan( f5 , data=list(y=y,x=x) )
What map2stan does is notice the missing values, see the distribution assigned to the variable with the missing values, build the Stan code that uses a mix of observed and estimated x values in the regression. See the stancode(m5) for details of the implementation.
A basic Gaussian process can be specified with the GPL2 distribution label. This implies a multivariate Gaussian with a covariance matrix defined by the ordinary L2 norm distance function:
k(i,j) = eta^2 * exp( -rho^2 * D(i,j)^2 ) + ifelse(i==j,sigma^2,0)
where D is a matrix of pairwise distances. To use this convention in, for example, a spatial autocorrelation model:
library(rethinking)
data(Kline2)
d <- Kline2
data(islandsDistMatrix)
d$society <- 1:10
mGP <- map2stan(
alist(
total_tools ~ dpois( mu ),
log(mu) <- a + aj[society],
a ~ dnorm(0,10),
aj[society] ~ GPL2( Dmat , etasq , rhosq , 0.01 ),
etasq ~ dcauchy(0,1),
rhosq ~ dcauchy(0,1)
),
data=list(
total_tools=d$total_tools,
society=d$society,
Dmat=islandsDistMatrix),
constraints=list(
etasq="lower=0",
rhosq="lower=0"
),
warmup=1000 , iter=5000 , chains=4 )
Note the use of the constraints list to pass custom parameter constraints to Stan. This example is explored in more detail in the book.
Both map and map2stan provide DIC and WAIC. Well, in most cases they do. In truth, both tools are flexible enough that you can specify models for which neither DIC nor WAIC can be correctly calculated. But for ordinary GLMs and GLMMs, it works. See the R help ?WAIC. A convenience function compare summarizes information criteria comparisons, including standard errors for WAIC.
ensemble computes link and sim output for an ensemble of models, each weighted by its Akaike weight, as computed from WAIC.