bayesian_t_test.Rmd

# 贝叶斯假设检验 {#bayesian-t-test}

```{r, include=FALSE}
knitr::opts_chunk$set(
   echo         = TRUE, 
   warning      = FALSE, 
   message      = FALSE,
   fig.showtext = TRUE
)
```

```{r}
library(tidyverse)
library(tidybayes)
library(rstan)

rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
theme_set(bayesplot::theme_default())
```



## 人们会给爱情片打高分？

这是一个关于电影评分的数据。我们想看下爱情片与动作片的平均得分是否存在显著不同？

```{r}
movies <- read_rds(here::here("demo_data", "movies.rds"))
movies
```



### 可视化探索

看下两种题材电影评分的分布

```{r}
movies %>%
  ggplot(aes(x = genre, y = rating, color = genre)) +
  geom_boxplot() +
  geom_jitter() +
  scale_x_discrete(
    expand = expansion(mult = c(0.5, 0.5))
  ) +
  theme(legend.position = "none") 
```



### 计算均值差

统计两种题材电影评分的均值

```{r}
group_diffs <- movies %>% 
  group_by(genre) %>% 
  summarize(avg_rating = mean(rating, na.rm = TRUE)) %>% 
  mutate(diff_means = avg_rating - lag(avg_rating))

group_diffs
```





### t检验

传统的t检验

```{r}
t.test(
  rating ~ genre,
  data = movies,
  var.equal = FALSE
) 
```


## stan 代码

### normal分布
先假定rating评分，服从正态分布，同时不同的电影题材分组考虑


$$
\begin{aligned}
\textrm{rating} & \sim \textrm{normal}(\mu_{\textrm{genre}}, \, \sigma _{\textrm{genre}}) \\
\mu  &\sim  \textrm{normal}(\textrm{mean_rating}, \, 2) \\
\sigma &\sim  \textrm{cauchy}(0, \, 1) 
\end{aligned} 
$$




```{r, warning=FALSE, message=FALSE}
stan_program <- '
data {
  int<lower=1> N;                            
  int<lower=2> n_groups;                     
  vector[N] y;                               
  int<lower=1, upper=n_groups> group_id[N];  
}
transformed data {
  real mean_y;
  mean_y = mean(y); 
}
parameters {
  vector[2] mu;                    
  vector<lower=0>[2] sigma;        
}
model {
  mu ~ normal(mean_y, 2);
  sigma ~ cauchy(0, 1);

  for (n in 1:N){
    y[n] ~ normal(mu[group_id[n]], sigma[group_id[n]]);
  }
}

generated quantities {
  real mu_diff;
  mu_diff = mu[2] - mu[1];
}

'

stan_data <- movies %>% 
  select(genre, rating, genre_numeric) %>% 
  tidybayes::compose_data(
    N        = nrow(.), 
    n_groups = n_distinct(genre), 
    group_id = genre_numeric, 
    y        = rating
  )

stan_best_normal <- stan(model_code = stan_program, data = stan_data)
```


```{r}
stan_best_normal
```


```{r, fig.width = 5, fig.height = 5}
stan_best_normal %>% 
  tidybayes::spread_draws(mu_diff) %>%
  ggplot(aes(x = mu_diff)) +
  tidybayes::stat_halfeye() +
  geom_vline(xintercept = 0)
```





```{r, fig.width = 5, fig.height = 3}
stan_best_normal %>% 
  tidybayes::spread_draws(mu_diff) %>%
  
	ggplot(aes(x = mu_diff)) +
  stat_eye(side = "right", 
           fill = "skyblue",
  		     point_interval = mode_hdi, 
  		     .width = c(0.5, 0.89),
    	     interval_colour = "red", 
    	     point_colour = "red",
  		     width = 15.5, 
  		     height = 0.1
  		     ) +
  geom_vline(xintercept = c(-0.1, 0.1), linetype = "dashed", size = 1) +

  coord_cartesian(xlim = c(-1, 2)) +
	labs(x = "mu_diff", y = NULL)
```



### 等效检验

我们一般会采用实用等效区间 region of practical equivalence [ROPE](https://easystats.github.io/bayestestR/articles/region_of_practical_equivalence.html)。实用等效区间，就是我们感兴趣值附近的一个区间，比如这里的均值差。频率学中的零假设是看均值差是否为0，贝叶斯则是看均值差有多少落入0附近的区间。具体方法就是，先算出后验分布的高密度区间，然后看这个高密度区间落在[-0.1, 0.1]的比例.




```{r}
lower <- -0.1*sd(movies$rating)
upper <-  0.1*sd(movies$rating)

stan_best_normal %>% 
  tidybayes::spread_draws(mu_diff) %>%
  filter(
    mu_diff > ggdist::mean_hdi(mu_diff, .width = c(0.89))$ymin,
    mu_diff < ggdist::mean_hdi(mu_diff, .width = c(0.89))$ymax
  ) %>%
  summarise(
    percentage_in_rope = mean(between(mu_diff, lower, upper))
  )
```

在做假设检验的时候，我们内心是期望，后验概率的**高密度区间**落在**实际等效区间**的比例越小越小，如果小于2.5%，我们就可以拒绝零假设了；如果大于97.5%，我们就接受零假设。


```{r}
stan_best_normal %>% 
  tidybayes::spread_draws(mu_diff) %>%
  pull(mu_diff) %>% 
  bayestestR::rope(x,
    range = c(-0.1, 0.1)*sd(movies$rating),
    ci = 0.89,
    ci_method = "HDI"
  )

```




### Student-t 分布

> 标准正态分布是t分布的极限分布

```{r}
for (nu in c(1, seq(5, 50, by = 10))) {
 p <- tibble(x = seq(-5, 5, by=0.1)) %>% 
    ggplot(aes(x)) + 
    stat_function(fun = dnorm, color = 'gray') + 
    stat_function(fun = dt, args = list(df = nu), color = 'blue') +
    theme_classic() + 
    ylab("Density") + 
    xlab('Value') + 
    ggtitle(paste("df =", nu))
 
  print(p)
}
```




假定rating评分服从student-t分布，

$$
\begin{aligned}
\textrm{rating} & \sim \textrm{student_t}(\nu, \,\mu_{\textrm{genre}}, \, \sigma _{\textrm{genre}}) \\
\mu  &\sim  \textrm{normal}(\textrm{mean_rating}, \, 2) \\
\sigma &\sim  \textrm{cauchy}(0, \, 1) \\
\nu  &\sim \textrm{exponential}(1.0/29)
\end{aligned} 
$$



```{r, warning=FALSE, message=FALSE}
stan_program <- '
data {
  int<lower=1> N;                            
  int<lower=2> n_groups;                     
  vector[N] y;                               
  int<lower=1, upper=n_groups> group_id[N];  
}
transformed data {
  real mean_y;
  mean_y = mean(y); 
}
parameters {
  vector[2] mu;                    
  vector<lower=0>[2] sigma;        
  real<lower=0, upper=100> nu;     
}
model {
  mu ~ normal(mean_y, 2);
  sigma ~ cauchy(0, 1);
  nu ~ exponential(1.0/29);

  for (n in 1:N){
    y[n] ~ student_t(nu, mu[group_id[n]], sigma[group_id[n]]);
  }
}

generated quantities {
  real mu_diff;
  mu_diff = mu[2] - mu[1];
}

'

stan_data <- movies %>% 
  select(genre, rating, genre_numeric) %>% 
  tidybayes::compose_data(
    N        = nrow(.), 
    n_groups = n_distinct(genre), 
    group_id = genre_numeric, 
    y        = rating
  )

stan_best_student <- stan(model_code = stan_program, data = stan_data)
```


```{r}
stan_best_student
```

```{r, fig.width = 5, fig.height = 5}
stan_best_student %>% 
  tidybayes::spread_draws(mu_diff) %>%
  ggplot(aes(x = mu_diff)) +
  tidybayes::stat_halfeye() +
  geom_vline(xintercept = 0)
```



```{r}
stan_best_student %>%
  as.data.frame() %>% 
  head()
```


```{r}
stan_best_student %>%
  as.data.frame() %>%
  ggplot(aes(x = `mu[1]`)) +
  geom_density()

```




```{r}
stan_best_student %>%
  tidybayes::gather_draws(mu[i], sigma[i]) %>%
  tidybayes::mean_hdi(.width = 0.89)
```




## 小结

```{r, out.width = '80%', fig.align = 'center', echo = FALSE}
knitr::include_graphics(here::here("images", "diagram_robust_Bayesian_estimation.png"))
```

## 作业

- 将上一章线性模型的stan代码应用到电影评分数据中

$$
\begin{aligned}
\textrm{rating} & \sim \textrm{Normal}(\mu, \, \sigma) \\
\mu & = \alpha + \beta \, \textrm{genre} \\
\alpha &\sim  \textrm{Normal}(0, \, 5) \\
\beta  &\sim  \textrm{Normal}(0, \, 1) \\
\sigma &\sim  \textrm{Exponential}(1) \\
\end{aligned} 
$$

```{r, warning=FALSE, message=FALSE}
stan_program <- '
data {
  int<lower=1> N;            
  vector[N] y;        
  vector[N] x;           
}
parameters {
  real<lower=0> sigma;    
  real alpha;                     
  real beta;                     
}

model {
  y ~ normal(alpha + beta * x, sigma);  
  
  alpha ~ normal(0, 5);
  beta ~ normal(0, 1);
  sigma ~ exponential(1);  
}

'

stan_data <- list(
    N   = nrow(movies), 
    x   = as.numeric(movies$genre),  
    y   = movies$rating
  )

stan_linear <- stan(model_code = stan_program, data = stan_data)
```


```{r}
stan_linear
```

```{r, fig.width = 5, fig.height = 5}
stan_linear %>% 
  tidybayes::spread_draws(beta) %>%
  ggplot(aes(x = beta)) +
  tidybayes::stat_halfeye() +
  geom_vline(xintercept = 0)
```



```{r}
stan_linear %>%
  tidybayes::gather_draws(beta) %>%
  tidybayes::mean_hdi(.width = 0.89)
```



```{r, echo = F, message = F, warning = F, results = "hide"}
pacman::p_unload(pacman::p_loaded(), character.only = TRUE)
```