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32 lines
700 B
Plaintext
32 lines
700 B
Plaintext
data {
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int<lower=0> n_dogs;
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int<lower=0> n_trials;
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int<lower=0,upper=1> y[n_dogs,n_trials];
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}
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parameters {
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vector[3] beta;
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}
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transformed parameters {
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matrix[n_dogs,n_trials] n_avoid;
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matrix[n_dogs,n_trials] n_shock;
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matrix[n_dogs,n_trials] p;
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for (j in 1:n_dogs) {
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n_avoid[j,1] <- 0;
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n_shock[j,1] <- 0;
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for (t in 2:n_trials) {
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n_avoid[j,t] <- n_avoid[j,t-1] + 1 - y[j,t-1];
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n_shock[j,t] <- n_shock[j,t-1] + y[j,t-1];
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}
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for (t in 1:n_trials)
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p[j,t] <- beta[1] + beta[2] * n_avoid[j,t] + beta[3] * n_shock[j,t];
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}
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}
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model {
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beta ~ normal(0, 100);
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for (i in 1:n_dogs) {
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for (j in 1:n_trials)
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y[i,j] ~ bernoulli_logit(p[i,j]);
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}
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}
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