In Section 16.3.2, McElreath fit a mixture of unobserved strategies. On page 534, we see his statistical model was:
He fit the model using rstan::stan() and already has his Stan model code saved in the rethinking package. Here's how to fit his model:
# load
library(rethinking)
# data
data(Boxes)
data(Boxes_model)
# prep data
dat_list <- list(
N = nrow(Boxes),
y = Boxes$y,
majority_first = Boxes$majority_first )
# run the sampler
m16.2 <- stan( model_code=Boxes_model , data=dat_list , chains=3 , cores=3 )
If you execute cat(Boxes_model), you can get McElreath's Stan code:
data{
int N;
int y[N];
int majority_first[N];
}
parameters{
simplex[5] p;
}
model{
vector[5] phi;
// prior
p ~ dirichlet( rep_vector(4,5) );
// probability of data
for ( i in 1:N ) {
if ( y[i]==2 ) phi[1]=1; else phi[1]=0; // majority
if ( y[i]==3 ) phi[2]=1; else phi[2]=0; // minority
if ( y[i]==1 ) phi[3]=1; else phi[3]=0; // maverick
phi[4]=1.0/3.0; // random
if ( majority_first[i]==1 ) // follow first
if ( y[i]==2 ) phi[5]=1; else phi[5]=0;
else
if ( y[i]==3 ) phi[5]=1; else phi[5]=0;
// compute log( p_s * Pr(y_i|s )
for ( j in 1:5 ) phi[j] = log(p[j]) + log(phi[j]);
// compute average log-probability of y_i
target += log_sum_exp( phi );
}
}
To my eye, it looks like one might be able to fit a model like this with brms if you make a custom likelihood (see here). However, that would be well beyond my current skill set.
In Section 16.3.2, McElreath fit a mixture of unobserved strategies. On page 534, we see his statistical model was:
He fit the model using
rstan::stan()and already has his Stan model code saved in the rethinking package. Here's how to fit his model:If you execute
cat(Boxes_model), you can get McElreath's Stan code:To my eye, it looks like one might be able to fit a model like this with brms if you make a custom likelihood (see here). However, that would be well beyond my current skill set.