This R package accompanies a course and book on Bayesian data analysis: McElreath 2020. Statistical Rethinking, 2nd edition, CRC Press. If you are using it with the first edition of the book, please see the notes at the bottom of this file.
It contains tools for conducting both quick quadratic approximation of the posterior distribution as well as Hamiltonian Monte Carlo (through RStan or cmdstanr - mc-stan.org). Many packages do this. The signature difference of this package is that it forces 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 and---most important---useful for teaching and learning. When students have to write out every detail of the model, they actually learn the model.
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 ~ dexp( 1 )
)
The first formula in the list is the probability of the outcome (likelihood); the second is the prior for mu; the third is the prior for 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.
There are some advantages to accessing Stan through cmdstanr rather than rstan. These advantages include faster updates and therefore quicker access to new features. If you want to access Stan using the cmdstanr package instead, then you may install that as well with
devtools::install_github("stan-dev/cmdstanr")
If you haven't installed cmdstan previously, you will also need to do that with install_cmdstan(). Then you need to add cmdstan=TRUE to any ulam code to use cmdstan instead of rstan.
Once rstan and cmdstan are installed (almost there), then you can install rethinking from within R using:
install.packages(c("coda","mvtnorm","devtools","loo","dagitty"))
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.
Almost any ordinary generalized linear model can be specified with quap. To use quadratic approximation:
library(rethinking)
f <- alist(
y ~ dnorm( mu , sigma ),
mu ~ dnorm( 0 , 10 ),
sigma ~ dexp( 1 )
)
fit <- quap(
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 sd 5.5% 94.5%
mu 0.00 0.59 -0.95 0.95
sigma 0.84 0.33 0.31 1.36
It also supports vectorized parameters, which is convenient for categories. See examples ?quap.
In the first edition of the textbook, this function was called map. It can still be used with that alias. It was renamed, because the name map was misleading. This function produces quadratic approximations of the posterior distribution, not just maximum a posteriori (MAP) estimates.
The same formula list can be compiled into a Stan (mc-stan.org) model using one of two tools: ulam or map2stan. For simple models, they are identical. ulam is the newer tool that allows for much more flexibility, including explicit variable types and custom distributions. map2stan is the original tool from the first edition of the package and textbook. Going forward, new features will be added to ulam.
ulam is named after Stanisław Ulam, who was one of the parents of the Monte Carlo method and is the namesake of the Stan project as well. It is pronounced something like [OO-lahm], not like [YOU-lamm].
Both tools take the same kind of input as quap:
fit_stan <- ulam( f , data=list(y=c(-1,1)) )
The chain runs automatically, provided rstan is installed. Chain diagnostics are displayed in the precis(fit_stan) output:
mean sd 5.5% 94.5% n_eff Rhat
sigma 1.45 0.72 0.67 2.84 145 1
mu 0.12 1.04 -1.46 1.59 163 1
For ulam models, plot displays the same information as precis and traceplot displays the chains.
extract.samples returns samples in a list. extract.prior samples from the prior and returns the samples in a list as well.
The stanfit object itself is in the @stanfit slot. Anything you'd do with a Stan model can be done with that slot directly.
The Stan code can be accessed by using stancode(fit_stan):
data{
real y[2];
}
parameters{
real<lower=0> sigma;
real mu;
}
model{
sigma ~ exponential( 1 );
mu ~ normal( 0 , 10 );
y ~ normal( mu , sigma );
}
Note that ulam doesn't care about R distribution names. You can instead use Stan-style names:
fit_stan <- ulam(
alist(
y ~ normal( mu , sigma ),
mu ~ normal( 0 , 10 ),
sigma ~ exponential( 1 )
), data=list(y=c(-1,1)) )
All quap, ulam, and map2stan objects can be post-processed to produce 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. sim can also be used to simulate prior predictives.
See ?link and ?sim for details.
postcheck automatically computes posterior predictive (retrodictive?) checks. It merely uses link and sim.
While quap is limited to fixed effects models for the most part, ulam can specify multilevel models, even quite complex ones. For example, a simple varying intercepts model looks like:
# prep data
data( UCBadmit )
UCBadmit$male <- as.integer(UCBadmit$applicant.gender=="male")
UCBadmit$dept <- rep( 1:6 , each=2 )
UCBadmit$applicant.gender <- NULL
# varying intercepts model
m_glmm1 <- ulam(
alist(
admit ~ binomial(applications,p),
logit(p) <- a[dept] + b*male,
a[dept] ~ normal( abar , sigma ),
abar ~ normal( 0 , 4 ),
sigma ~ half_normal(0,1),
b ~ normal(0,1)
), data=UCBadmit )
The analogous varying slopes model is:
m_glmm2 <- ulam(
alist(
admit ~ binomial(applications,p),
logit(p) <- a[dept] + b[dept]*male,
c( a , b )[dept] ~ multi_normal( c(abar,bbar) , Rho , sigma ),
abar ~ normal( 0 , 4 ),
bbar ~ normal(0,1),
sigma ~ half_normal(0,1),
Rho ~ lkjcorr(2)
),
data=UCBadmit )
Another way to express the varying slopes model is with a vector of varying effects. This is made possible by using an explicit vector declaration inside the formula:
m_glmm3 <- ulam(
alist(
admit ~ binomial(applications,p),
logit(p) <- v[dept,1] + v[dept,2]*male,
vector[2]:v[dept] ~ multi_normal( c(abar,bbar) , Rho , sigma ),
abar ~ normal( 0 , 4 ),
bbar ~ normal(0,1),
sigma ~ half_normal(0,1),
Rho ~ lkjcorr(2)
),
data=UCBadmit )
That vector[2]:v[dept] means "declare a vector of length two for each unique dept". To access the elements of these vectors, the linear model uses multiple indexes inside the brackets: [dept,1].
This strategy can be taken one step further and the means can be declared as a vector as well:
m_glmm4 <- ulam(
alist(
admit ~ binomial(applications,p),
logit(p) <- v[dept,1] + v[dept,2]*male,
vector[2]:v[dept] ~ multi_normal( v_mu , Rho , sigma ),
vector[2]:v_mu ~ normal(0,1),
sigma[1] ~ half_normal(0,1),
sigma[2] ~ half_normal(0,2),
Rho ~ lkjcorr(2)
),
data=UCBadmit )
And a completely non-centered parameterization can be coded directly as well:
m_glmm5 <- ulam(
alist(
admit ~ binomial(applications,p),
logit(p) <- v_mu[1] + v[dept,1] + (v_mu[2] + v[dept,2])*male,
matrix[dept,2]: v <- t(diag_pre_multiply( sigma , L_Rho ) * z),
matrix[2,dept]: z ~ normal( 0 , 1 ),
vector[2]: v_mu[[1]] ~ normal(0,4),
vector[2]: v_mu[[2]] ~ normal(0,1),
vector[2]: sigma ~ half_normal(0,1),
cholesky_factor_corr[2]: L_Rho ~ lkj_corr_cholesky( 2 )
),
data=UCBadmit )
In the above, the varying effects matrix v is constructed from a matrix of z-scores z and a covariance structure contained in sigma and a Cholesky factor L_Rho. Note the double-bracket notation v_mu[[1]] allowing distinct priors for each index of a vector.
ulam can optionally return pointwise log-likelihood values. These are needed for computing WAIC and PSIS-LOO. The log_lik argument toggles this on:
m_glmm1 <- ulam(
alist(
admit ~ binomial(applications,p),
logit(p) <- a[dept] + b*male,
a[dept] ~ normal( abar , sigma ),
abar ~ normal( 0 , 4 ),
sigma ~ half_normal(0,1),
b ~ normal(0,1)
), data=UCBadmit , log_lik=TRUE )
WAIC(m_glmm1)
The additional code has been added to the generated quantities block of the Stan model (see this with stancode(m_glmm1)):
generated quantities{
vector[12] log_lik;
vector[12] p;
for ( i in 1:12 ) {
p[i] = a[dept[i]] + b * male[i];
p[i] = inv_logit(p[i]);
}
for ( i in 1:12 ) log_lik[i] = binomial_lpmf( admit[i] | applications[i] , p[i] );
}
ulam also supports if-then statements and custom distribution assignments. These are useful for coding mixture models, such as zero-inflated Poisson and discrete missing value models.
Here's an example zero-inflated Poisson model.
# zero-inflated poisson
# gen data first - example from text
prob_drink <- 0.2 # 20% of days
rate_work <- 1 # average 1 manuscript per day
N <- 365
drink <- rbinom( N , 1 , prob_drink )
y <- as.integer( (1-drink)*rpois( N , rate_work ) )
x <- rnorm( N ) # dummy covariate
# now ulam code
m_zip <- ulam(
alist(
y|y==0 ~ custom( log_mix( p , 0 , poisson_lpmf(0|lambda) ) ),
y|y>0 ~ custom( log1m(p) + poisson_lpmf(y|lambda) ),
logit(p) <- ap,
log(lambda) <- al + bl*x,
ap ~ dnorm(0,1),
al ~ dnorm(0,10),
bl ~ normal(0,1)
) ,
data=list(y=y,x=x) )
The Stan code corresponding to the first two lines in the formula above is:
for ( i in 1:365 )
if ( y[i] > 0 ) target += log1m(p) + poisson_lpmf(y[i] | lambda[i]);
for ( i in 1:365 )
if ( y[i] == 0 ) target += log_mix(p, 0, poisson_lpmf(0 | lambda[i]));
What custom does is define custom target updates. And the | operator makes the line conditional. Note that log1m, log_mix, and poisson_lpmf are Stan functions.
The same custom distribution approach allows for marginalization over discrete missing values. Let's introduce some missing values in the UCBadmit data from earlier.
UCBadmit$male2 <- UCBadmit$male
UCBadmit$male2[1:2] <- (-1) # missingness code
UCBadmit$male2 <- as.integer(UCBadmit$male2)
Now the model needs to detect when male2 is missing (-1) and then compute a mixture over the unknown state.
m_mix <- ulam(
alist(
admit|male2==-1 ~ custom( log_mix(
phi_male ,
binomial_lpmf(admit|applications,p_m1) ,
binomial_lpmf(admit|applications,p_m0) ) ),
admit|male2>-1 ~ binomial( applications , p ),
logit(p) <- a[dept] + b*male2,
logit(p_m1) <- a[dept] + b*1,
logit(p_m0) <- a[dept] + b*0,
male2|male2>-1 ~ bernoulli( phi_male ),
phi_male ~ beta(2,2),
a[dept] ~ normal(0,4),
b ~ normal(0,1)
),
data=UCBadmit )
Note the addition of phi_male to average over the unknown state.
In principle, imputation of missing real-valued data is easy: Just replace each missing value with a parameter. In practice, this involves a bunch of annoying bookkeeping. ulam has a macro named merge_missing to simplify this.
UCBadmit$x <- rnorm(12)
UCBadmit$x[1:2] <- NA
m_miss <- ulam(
alist(
admit ~ binomial(applications,p),
logit(p) <- a + b*male + bx*x_merge,
x_merge ~ normal( 0 , 1 ),
x_merge <- merge_missing( x , x_impute ),
a ~ normal(0,4),
b ~ normal(0,1),
bx ~ normal(0,1)
),
data=UCBadmit )
What merge_missing does is find the NA values in x (whichever symbol is the first argument), build a vector of parameters called x_impute (whatever you name the second argument) of the right length, and piece together a vector x_merge that contains both, in the right places. You can then assign a prior to this vector and use it in linear models as usual.
The merging is done as the Stan model runs, using a custom function block. See the Stan code stancode(m_miss) for all the lovely details.
merge missing is an example of a macro, which is a way for ulam to use function names to trigger special compilation. In this case, merge_missing both inserts a function in the Stan model and builds the necessary index to locate the missing values during run time. Macros will get full documentation later, once the system is finalized.
A simple Gaussian process, like the Oceanic islands example in Chapter 13 of the book, is done as:
data(Kline2)
d <- Kline2
data(islandsDistMatrix)
d$society <- 1:10
dat <- list(
y=d$total_tools,
society=d$society,
log_pop = log(d$population),
Dmat=islandsDistMatrix
)
m_GP1 <- ulam(
alist(
y ~ poisson( mu ),
log(mu) <- a + aj[society] + b*log_pop,
a ~ normal(0,10),
b ~ normal(0,1),
vector[10]: aj ~ multi_normal( 0 , SIGMA ),
matrix[10,10]: SIGMA <- cov_GPL2( Dmat , etasq , rhosq , 0.01 ),
etasq ~ exponential(1),
rhosq ~ exponential(1)
),
data=dat )
This is just an ordinary varying intercepts model, but all 10 intercepts are drawn from a single Gaussian distribution. The covariance matrix SIGMA is defined in the usual L2-norm. Again, cov_GPL2 is a macro that inserts a function in the Stan code to compute the covariance matrix as the model runs.
Fancier Gaussian processes require a different parameterization. And these can be built as well. Here's an example using 151 primate species and a phylogenetic distance matrix. First, prepare the data:
data(Primates301)
data(Primates301_distance_matrix)
d <- Primates301
d$name <- as.character(d$name)
dstan <- d[ complete.cases( d$social_learning, d$research_effort , d$body , d$brain ) , ]
# prune distance matrix to spp in dstan
spp_obs <- dstan$name
y <- Primates301_distance_matrix
y2 <- y[ spp_obs , spp_obs ]
# scale distances
y3 <- y2/max(y2)
Now the model, which is a non-centered L2-norm Gaussian process:
m_GP2 <- ulam(
alist(
social_learning ~ poisson( lambda ),
log(lambda) <- a + g[spp_id] + b_ef*log_research_effort + b_body*log_body + b_eq*log_brain,
a ~ normal(0,1),
vector[N_spp]: g <<- L_SIGMA * eta,
vector[N_spp]: eta ~ normal( 0 , 1 ),
matrix[N_spp,N_spp]: L_SIGMA <<- cholesky_decompose( SIGMA ),
matrix[N_spp,N_spp]: SIGMA <- cov_GPL2( Dmat , etasq , rhosq , 0.01 ),
b_body ~ normal(0,1),
b_eq ~ normal(0,1),
b_ef ~ normal(1,1),
etasq ~ exponential(1),
rhosq ~ exponential(1)
),
data=list(
N_spp = nrow(dstan),
social_learning = dstan$social_learning,
spp_id = 1:nrow(dstan),
log_research_effort = log(dstan$research_effort),
log_body = log(dstan$body),
log_brain = log(dstan$brain),
Dmat = y3
) ,
control=list(max_treedepth=15,adapt_delta=0.95) ,
sample=FALSE )
This model does not sample quickly, so I've set sample=FALSE. You can still inspect the Stan code with stancode(m_GP2).
Note that the covariance SIGMA is built the same way as before, but then we immediately decompose it to a Cholesky factor and build the varying intercepts g by matrix multiplication. The <<- operator tells ulam not to loop, but to do a direct assignment. So g <<- L_SIGMA * eta does the right linear algebra.
Using cmdstanr instead of rstan is currently the only way to use within-chain multithreading with rethinking. It also tends to compile models faster and is more intelligent about when models need to be re-compiled, so using cmdstanr is recommended, even if you don't want multithreading.
If you want ulam to access Stan using the cmdstanr package, then you may install that as well with
devtools::install_github("stan-dev/cmdstanr")
If you haven't installed cmdstan previously, you will also need to do that with install_cmdstan().
Then you need to add cmdstan=TRUE to the ulam code. The threads argument controls the number of threads per chain. Example:
N <- 1e4
x <- rnorm(N)
m <- 1 + rpois(N,2)
y <- rbinom( N , size=m , prob=inv_logit(-3+x) )
dat <- list( y=y , x=x , m=m )
# single thread
m1 <- ulam(
alist(
y ~ binomial_logit( m , logit_p ),
logit_p <- a + b*x,
a ~ normal(0,1.5),
b ~ normal(0,0.5)
) , data=dat ,
cmdstan=TRUE , threads=2 , refresh=1000 )
There are models that cannot be automaticaly multithreaded this way, because of the complexity of the code. In those cases, you can write the code directly in Stan. See this guide. Writing multithreaded models direct in Stan can also be more efficient, since you can make detailed choices about which variables to pass and which pieces of the model to multithread.
ulam is still in development, but mostly feature complete. It will remain primarily a teaching tool, exposing the statistical details of the model while hiding some of the programming details necessary in Stan.
The older map2stan function makes stronger assumtions about the formulas it will see. This allows is to provide some additional automation and it has some special syntax as a result. ulam in contrast supports such features through its macros library.
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.
Binary (0/1) variables with missing values present a special obstacle, because Stan cannot sample discrete parameters. So instead of imputing binary missing values, map2stan can average (marginalize) over them. As in the above case, when map2stan detects missing values in a predictor variable, it will try to find a distribution for the variable containing them. If this variable is binary (0/1), then it will construct a mixture model in which each term is the log-likelihood conditional on the variables taking a particular combination of 0/1 values.
Following the example in the previous section, we can simulate missingness in a binary predictor:
N <- 100
N_miss <- 10
x <- rbinom( N , size=1 , prob=0.5 )
y <- rnorm( N , 2*x , 1 )
x[ sample(1:N,size=N_miss) ] <- NA
The model definition is analogous to the previous, but also requires some care in specifying constraints for the hyperparameters that define the distribution for x:
f6 <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + b*x,
x ~ bernoulli( phi ),
a ~ dnorm( 0 , 100 ),
b ~ dnorm( 0 , 10 ),
phi ~ beta( 1 , 1 ),
sigma ~ dcauchy(0,2)
)
m6 <- map2stan( f6 , data=list(y=y,x=x) , constraints=list(phi="lower=0,upper=1") )
The algorithm works, in theory, for any number of binary predictors with missing values. For example, with two predictors, each with missingness:
N <- 100
N_miss <- 10
x1 <- rbinom( N , size=1 , prob=0.5 )
x2 <- rbinom( N , size=1 , prob=0.1 )
y <- rnorm( N , 2*x1 - x2 , 1 )
x1[ sample(1:N,size=N_miss) ] <- NA
x2[ sample(1:N,size=N_miss) ] <- NA
f7 <- alist(
y ~ dnorm( mu , sigma ),
mu <- a + b1*x1 + b2*x2,
x1 ~ bernoulli( phi1 ),
x2 ~ bernoulli( phi2 ),
a ~ dnorm( 0 , 100 ),
c(b1,b2) ~ dnorm( 0 , 10 ),
phi1 ~ beta( 1 , 1 ),
phi2 ~ beta( 1 , 1 ),
sigma ~ dcauchy(0,2)
)
m7 <- map2stan( f7 , data=list(y=y,x1=x1,x2=x2) ,
constraints=list(phi1="lower=0,upper=1",phi2="lower=0,upper=1") )
While the unobserved values for the binary predictors are usually not of interest, they can be computed from the posterior distribution. Adding the argument do_discrete_imputation=TRUE instructs map2stan to perform these calculations automatically. Example:
m6 <- map2stan( f6 , data=list(y=y,x=x) , constraints=list(phi="lower=0,upper=1") ,
do_discrete_imputation=TRUE )
precis( m6 , depth=2 )
The output contains samples for each case with imputed probilities that x takes the value 1.
The algorithm works by constructing a list of mixture terms that are needed to to compute the probability of each observed y value. In the simplest case, with only one predictor with missing values, the implied mixture likelihood contains two terms:
Pr(y[i]) = Pr(x[i]=1)Pr(y[i]|x[i]=1) + Pr(x[i]=0)Pr(y[i]|x[i]=0)
In the parameters of our example model m6 above, this is:
Pr(y[i]) = phi*N(y[i]|a+b,sigma) + (1-phi)*N(y[i]|a,sigma)
It is now a simple matter to loop over cases i and compute the above for each. Similarly the posterior probability of that x[i]==1 is given as:
Pr(x[i]==1|y[i]) = phi*N(y[i]|a+b,sigma) / Pr(y[i])
When only one predictor has missingness, then this is simple. What about when there are two or more? In that case, all the possible combinations of missingness have to be accounted for. For example, suppose there are two predictors, x1 and x2, both with missingness on case i. Now the implied mixture likelihood is:
Pr(y[i]) = Pr(x1=1)Pr(x2=1)*Pr(y[i]|x1=1,x2=1) + Pr(x1=1)Pr(x2=0)Pr(y[i]|x1=1,x2=0) + Pr(x1=0)Pr(x2=1)Pr(y[i]|x1=0,x2=1) + Pr(x1=0)Pr(x2=0)Pr(y[i]|x1=0,x2=0)
There are four combinations of unobserved values, and so four terms in the mixture likelihood. When x2 is instead observed, we can substitute the observed value into the above, and then the mixture simplifies readily to our previous two-term likelihood:
Pr(y[i]|x2[i]==1) = Pr(x1=1)Pr(x2=1)Pr(y[i]|x1=1,x2=1) + Pr(x1=1)Pr(x2=0)Pr(y[i]|x1=1,x2=1) + Pr(x1=0)Pr(x2=1)Pr(y[i]|x1=0,x2=1) + Pr(x1=0)Pr(x2=0)Pr(y[i]|x1=0,x2=1)
= [Pr(x1=1)Pr(x2=1)+Pr(x1=1)Pr(x2=0)]Pr(y[i]|x1=1,x2=1)
+ [Pr(x1=0)Pr(x2=1)+Pr(x1=0)Pr(x2=0)]Pr(y[i]|x1=0,x2=1)
= Pr(x1=1)Pr(y[i]|x1=1,x2=1) + Pr(x1=0)Pr(y[i]|x1=0,x2=1)
This implies that if we loop over cases i and insert any observed values into the general mixture likelihood, we can compute the relevant mixture for the specific combination of missingness on each case i. That is what map2stan does. The general mixture terms can be generated algorithmically. The code below generates a matrix of terms for n binary variables with missingness.
ncombinations <- 2^n
d <- matrix(NA,nrow=ncombinations,ncol=n)
for ( col_var in 1:n )
d[,col_var] <- rep( 0:1 , each=2^(col_var-1) , length.out=ncombinations )
Rows of d contain terms, columns contain variables, and the values in each column are the corresponding values of each variable. The algorithm builds a linear model for each row in this matrix, composes the mixture likelihood as the sum of these rows, and performs proper substitutions of observed values. All calculations are done on the log scale, for precision.
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$society,
Dmat=islandsDistMatrix),
constraints=list(
etasq="lower=0",
rhosq="lower=0"
),
warmup=1000 , iter=5000 , chains=4 )
Note the use of the constraints list to pass custom parameter constraints to Stan. This example is explored in more detail in the book.
Both map and map2stan provide DIC and WAIC. Well, in most cases they do. In truth, both tools are flexible enough that you can specify models for which neither DIC nor WAIC can be correctly calculated. But for ordinary GLMs and GLMMs, it works. See the R help ?WAIC. A convenience function compare summarizes information criteria comparisons, including standard errors for WAIC.
ulam supports WAIC calculation with the optional log_lik=TRUE argument, which returns the kind of log-likelihood vector needed by the loo package.
ensemble computes link and sim output for an ensemble of models, each weighted by its Akaike weight, as computed from WAIC.
A small change to link has broken two examples in the first edition of the book, in Chapter 7.
mu.Africa.mean <- apply( mu.Africa , 2 , mean ) Error in apply(mu.Africa, 2, mean) : dim(X) must have a positive length
This occurs because link() now returns all linear models. So mu.Africa is a list containing mu and gamma. To fix, use:
mu.Africa.mean <- apply( mu.Africa$mu , 2 , mean )
Use a similar fix in the other apply() calls in the same section.
Similar problem as for R code 7.10. Use mu.ruggedlo$mu in place of mu.ruggedlo.