RStan-error报文中变截获模型的建立

我试图在RStan中建立一个不同的截距模型，试图根据年龄、儿童数量以及是否居住在城市地区预测印度某个地区的女性避孕药具使用情况，截距因地区而异

下面是我的代码：

stan_list <- list(N = nrow(train), contra = (train$contraceptive_use),
                  district = train$district,
                  urban = train$urban,
                  children = train$living.children,
                  age = train$age_mean)
str(stan_list)

code <- '
data {                          
int<lower=0> N;                              // number of observations
int<lower=1, upper=60> district[N];          // number of districts (J = 60)
vector[N] contra;                            // dependent variable
vector[N] urban;                             // independent variable 1
vector[N] children;                          // independent variable 2
vector[N] age;                               // independent variable 3
}

parameters {
vector[60] beta_0;                       // intercept estimated with 60 districts   
real mu_beta_0;                          // mean for intercepts
real sigma_beta_0;                       // standard error for the intercept
real beta_1;
real beta_2;                              
real beta_3;
}

transformed parameters {
vector[N] contra_hat;                        // create one variable with predictions for each observation
for (i in 1:N) {                              // loop for all cases
     contra_hat[i] <- beta_0[district[i]] + beta_1 * urban[i] + beta_2 * children[i] + beta_3 * age[i];
}
}

model {
mu_beta_0 ~ normal(0, 100);
sigma_beta_0 ~ exponential(0.1);
beta_0 ~ normal(mu_beta_0, sigma_beta_0);
beta_1 ~ normal(4, sigma_beta_0);
beta_2 ~ normal(-3, sigma_beta_0);
beta_3 ~ normal(-2, sigma_beta_0);

// Likelihood
contra ~ bernoulli_logit(contra_hat);
}
'

# Translate the model into C++
model1 <- stan(model_code = code, data = stan_list, iter = 2000, chains = 4)

---

stan\u list结果必须在数据块中声明为整数数组，以便由
bernoulli\u logit
使用，如

int<lower=0,upper=1> contra[N];

int-contra[N]；

int<lower=0,upper=1> contra[N];