# create a fake data set with a covariate
trans_cov <- transform(transplant, country = cut(1:11, c(0, 5, 8, Inf), c("CH", "US", "DE")))
set.seed(34246)
map <- gMAP(cbind(r, n - r) ~ 1 + country | study,
  data = trans_cov,
  tau.dist = "HalfNormal",
  tau.prior = 1,
  # Note on priors: we make the overall intercept weakly-informative
  # and the regression coefficients must have tighter sd as these are
  # deviations in the default contrast parametrization
  beta.prior = rbind(c(0, 2), c(0, 1), c(0, 1)),
  family = binomial,
  ## ensure fast example runtime
  thin = 1, chains = 1
)

# posterior predictive distribution for each input data item (shrinkage estimates)
pred_cov <- predict(map)
pred_cov

# extract sample as matrix
samp <- as.matrix(pred_cov)

# predictive distribution for each input data item (if the input studies were new ones)
pred_cov_pred <- predict(map, trans_cov)
pred_cov_pred


# a summary function returns the results as matrix
summary(pred_cov)

# obtain a prediction for new data with specific covariates
pred_new <- predict(map, data.frame(country = "CH", study = 12))
pred_new
