Voters_Polarize.Rmd

# 谋杀事件对选民的影响 {#voters-polarize}

```{r, message=FALSE, warning=FALSE}
library(tidyverse)
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores()) 
```





## 缘起

Bischof and Wagner (2019)的文章[Do Voters Polarize When Radical Parties Enter Parliament?](https://onlinelibrary.wiley.com/doi/abs/10.1111/ajps.12449)的
数据放[American Journal of Political Science Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DZ1NFG)

我们现在重复他们 Table 1, Model 4


> 原文的分析使用普通最小二乘估计来衡量2002年荷兰议会选举前民粹主义激进右翼政治家皮姆·福图因(被暗杀)对微观意识形态(两极分化)的影响。为此，文章研究分析了2002年荷兰议会选举研究选前浪潮中1551名受访者。结果变量包含受访者(左右自我定位)与所有受访者选前(自我定位中位数)的平方距离。主要的预测因素是一个二元指标，即采访是在福图因遇刺之前还是之后进行的。


原文报道了点估计结果：截距1.644 (0.036)，斜率 -0.112 (0.076) ，括号里是（标准误）



## 数据读取

```{r}
## Retrieve and manage data
df <-
	read.table("./data/3_Netherlands2002.tab",
		header = TRUE,
		stringsAsFactors = FALSE,
		sep = "\t",
		fill = TRUE
	) %>% 
	select(wave, fortuyn, polarization) %>% ### select relevant variables
	subset(wave == 1) %>%                   ### subset to pre-election wave
	na.omit()                               ### drop incomplete rows

df
```





## stan模型

我们这里使用贝叶斯模型

```{r, warning=FALSE, message=FALSE}
stan_program <- '
//
// This Stan program defines a simple model, with a
// vector of values y modeled as normally distributed
// with mean mu and standard deviation sigma.
//
// Learn more about model development with Stan at:
//
//    http://mc-stan.org/users/interfaces/rstan.html
//    https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
//

functions {
  vector pw_norm(vector y, vector mu, real sigma) {
    return -0.5 * (log(2 * pi() * square(sigma)) + 
                     square((y - mu) / sigma));
  }
}


data {
  int<lower=1> N;             
  int<lower=1> K;             
  matrix[N, K] x;               
  vector[N] y;                 
}

parameters {
  vector[K] beta;      
  real<lower=0> sigma; 
}


transformed parameters {
  vector[N] mu;  
  mu = x * beta; 
}

model {
  // priors
  beta ~ normal(0, 10);  
  sigma ~ cauchy(0, 5);  
  
  // log-likelihood
  target += normal_lpdf(y | mu, sigma);
}

'


stan_data <- df %>%
  tidybayes::compose_data(
   N = nrow(.),
   y = polarization,
   K = 2,
   x = model.matrix(~ 1 + fortuyn, data = .)
  )

mod <- stan(model_code = stan_program, data = stan_data,            
				    pars = c("beta", "sigma")
				    )
```




```{r}
print(mod, pars = c("beta", "sigma"), digits_summary = 3L)
```