tidystats_lmm.Rmd

# 多层线性模型 {#tidystats-lmm}



## 分组数据

在实验设计和数据分析中，我们可能经常会遇到分组的数据结构。所谓的分组，就是每一次观察，属于某个特定的组，比如考察学生的成绩，这些学生属于某个班级，班级又属于某个学校。有时候发现这种分组的数据，会给数据分析带来很多有意思的内容。



## 案例

我们从一个有意思的案例开始。

> 不同院系教职员工的收入

一般情况下，不同的院系，制定教师收入的依据和标准可能是不同的。我们假定有一份大学教职员的收入清单，这个学校包括信息学院、外国语学院、社会政治学、生物学院、统计学院共五个机构，我们通过数据建模，探索这个学校的**薪酬制定规则**。

```{r lmm-1}
create_data <- function() {
  df <- tibble(
    ids = 1:100,
    department = rep(c("sociology", "biology", "english", "informatics", "statistics"), 20),
    bases = rep(c(40000, 50000, 60000, 70000, 80000), 20) * runif(100, .9, 1.1),
    experience = floor(runif(100, 0, 10)),
    raises = rep(c(2000, 500, 500, 1700, 500), 20) * runif(100, .9, 1.1)
  )


  df <- df %>% mutate(
    salary = bases + experience * raises
  )
  df
}
```



```{r lmm-2}
library(tidyverse)
library(lme4)
library(modelr)
library(broom)
library(broom.mixed)

df <- create_data()
df
```



## 线性模型

**薪酬制定规则一**：假定教师收入主要取决于他从事工作的时间，也就说说工作时间越长收入越高。意味着，每个院系的起始薪酬（起薪）是一样的，并有相同的年度增长率。那么，这个收入问题就是一个简单线性模型：

$$\hat{y} = \alpha + \beta_1x_1 + ... + \beta_nx_n$$

具体到我们的案例中，薪酬模型可以写为
$$
\hat{salary_i} = \alpha + \beta * experience_i
$$

通过这个等式，可以计算出各个系数，即截距$\alpha$就是起薪，斜率$\beta$就是年度增长率。确定了斜率和截距，也就确定了每个教职员工的收入曲线。

```{r lmm-3}
# Model without respect to grouping
m1 <- lm(salary ~ experience, data = df)
m1
```

```{r lmm-4}
broom::tidy(m1)
```


```{r lmm-5}
df %>% modelr::add_predictions(m1)
```

```{r lmm-6}
# Model without respect to grouping
df %>%
  add_predictions(m1) %>%
  ggplot(aes(x = experience, y = salary)) +
  geom_point() +
  geom_line(aes(x = experience, y = pred)) +
  labs(x = "Experience", y = "Predicted Salary") +
  ggtitle("linear model Salary Prediction") +
  scale_colour_discrete("Department")
```


> 注意到，对每个教师来说，不管来自哪个学院的，系数$\alpha$和$\beta$是一样的，是**固定**的，因此这种简单线性模型也称之为**固定效应**模型。 


事实上，这种线性模型的方法太过于粗狂，构建的线性直线不能反映**收入随院系的变化**。




## 变化的截距

**薪酬制定规则二**，假定不同的院系起薪不同，但年度增长率是相同的。

这种统计模型，相比于之前的固定效应模型（简单线性模型）而言，加入了**截距会随所在学院不同而变化**的思想，统计模型写为

$$\hat{y_i} = \alpha_{j[i]} + \beta x_i$$

这个等式中，斜率$\beta$代表着年度增长率，是一个固定值，也就前面说的固定效应项，而截距$\alpha$代表着起薪，随学院变化，是五个值，因为一个学院对应一个，称之为**变化效应项**（也叫随机效应项）。这里模型中既有固定效应项又有变化效应项，因此称之为**混合效应模型**。

> 教师$i$，他所在的学院$j$，记为$j[i]$，那么教师$i$所在学院$j$对应的$\alpha$，很自然的记为$\alpha_{j[i]}$



 
```{r lmm-7}
# Model with varying intercept
m2 <- lmer(salary ~ experience + (1 | department), data = df)
m2
```


```{r lmm-8}
broom.mixed::tidy(m2, effects = "fixed")
broom.mixed::tidy(m2, effects = "ran_vals")
```




```{r lmm-9}
df %>%
  add_predictions(m2) %>%
  ggplot(aes(
    x = experience, y = salary, group = department,
    colour = department
  )) +
  geom_point() +
  geom_line(aes(x = experience, y = pred)) +
  labs(x = "Experience", y = "Predicted Salary") +
  ggtitle("Varying Intercept Salary Prediction") +
  scale_colour_discrete("Department")
```



这种模型，我们就能看到院系不同  带来的员工收入的差别。



## 变化的斜率

**薪酬制定规则三**，不同的院系起始薪酬是相同的，但年度增长率不同。

与薪酬模型规则二的统计模型比较，我们只需要把**变化的截距**变成**变化的斜率**，那么统计模型可写为

$$\hat{y_i} = \alpha + \beta_{j[i]}x_i$$


这里，截距($\alpha$)对所有教师而言是固定不变的，而斜率($\beta$)会随学院不同而变化，5个学院对应着5个斜率。


```{r lmm-10}
# Model with varying slope
m3 <- lmer(salary ~ experience + (0 + experience | department), data = df)
m3
```

```{r lmm-11}
broom.mixed::tidy(m3, effects = "fixed")
broom.mixed::tidy(m3, effects = "ran_vals")
```





```{r lmm-12}
df %>%
  add_predictions(m3) %>%
  ggplot(aes(
    x = experience, y = salary, group = department,
    colour = department
  )) +
  geom_point() +
  geom_line(aes(x = experience, y = pred)) +
  labs(x = "Experience", y = "Predicted Salary") +
  ggtitle("Varying slope Salary Prediction") +
  scale_colour_discrete("Department")
```









## 变化的斜率 + 变化的截距


**薪酬制定规则四**，不同的学院起始薪酬和年度增长率也不同。

这可能是最现实的一种情形了，它实际上是规则二和规则三的一种组合，要求截距和斜率都会随学院的不同变化，数学上记为


$$\hat{y_i} = \alpha_{j[i]} + \beta_{j[i]}x_i$$
具体来说，教师$i$，所在的学院$j$, 他的入职的起始收入表示为 ($\alpha_{j[i]}$)，年度增长率表示为($\beta_{j[i]}$).
            


            
```{r lmm-13}
# Model with varying slope and intercept
m4 <- lmer(salary ~ experience + (1 + experience | department), data = df)
m4
```

```{r lmm-14}
broom.mixed::tidy(m4, effects = "fixed")
broom.mixed::tidy(m4, effects = "ran_vals")
```






```{r lmm-15}
df %>%
  add_predictions(m4) %>%
  ggplot(aes(
    x = experience, y = salary, group = department,
    colour = department
  )) +
  geom_point() +
  geom_line(aes(x = experience, y = pred)) +
  labs(x = "Experience", y = "Predicted Salary") +
  ggtitle("Varying Intercept and Slopes Salary Prediction") +
  scale_colour_discrete("Department")
```




## 信息池

### 提问
问题：**薪酬制定规则四**中，不同的院系起薪不同，年度增长率也不同，我们得出了5组不同的截距和斜率，那么是不是可以等价为，**先按照院系分5组，然后各算各的截距和斜率**? 比如

```{r lmm-16}
df %>%
  group_by(department) %>%
  group_modify(
    ~ broom::tidy(lm(salary ~ 1 + experience, data = .))
  )
```


> 分组各自回归，与这里的（变化的截距+变化的斜率）模型，不是一回事。


### 信息共享

- 完全共享
 
```{r lmm-17}
broom::tidy(m1)
```


```{r lmm-18}
complete_pooling <-
  broom::tidy(m1) %>%
  dplyr::select(term, estimate) %>%
  tidyr::pivot_wider(
    names_from = term,
    values_from = estimate
  ) %>%
  dplyr::rename(Intercept = `(Intercept)`, slope = experience) %>%
  dplyr::mutate(pooled = "complete_pool") %>%
  dplyr::select(pooled, Intercept, slope)

complete_pooling
```
 

- 部分共享

```{r lmm-19, eval=FALSE}
fix_effect <- broom.mixed::tidy(m4, effects = "fixed")
fix_effect
fix_effect$estimate[1]
fix_effect$estimate[2]
```


```{r lmm-20, eval=FALSE}
var_effect <- broom.mixed::tidy(m4, effects = "ran_vals")
var_effect
```



```{r lmm-21, eval=FALSE}
# random effects plus fixed effect parameters
partial_pooling <- var_effect %>%
  dplyr::select(level, term, estimate) %>%
  tidyr::pivot_wider(
    names_from = term,
    values_from = estimate
  ) %>%
  dplyr::rename(Intercept = `(Intercept)`, estimate = experience) %>%
  dplyr::mutate(
    Intercept = Intercept + fix_effect$estimate[1],
    estimate = estimate + fix_effect$estimate[2]
  ) %>%
  dplyr::mutate(pool = "partial_pool") %>%
  dplyr::select(pool, level, Intercept, estimate)

partial_pooling
```

```{r lmm-22}
partial_pooling <-
  coef(m4)$department %>%
  tibble::rownames_to_column() %>%
  dplyr::rename(level = rowname, Intercept = `(Intercept)`, slope = experience) %>%
  dplyr::mutate(pooled = "partial_pool") %>%
  dplyr::select(pooled, level, Intercept, slope)

partial_pooling
```




- 不共享

```{r lmm-23}
no_pool <- df %>%
  dplyr::group_by(department) %>%
  dplyr::group_modify(
    ~ broom::tidy(lm(salary ~ 1 + experience, data = .))
  )
no_pool
```


```{r lmm-24}
un_pooling <- no_pool %>%
  dplyr::select(department, term, estimate) %>%
  tidyr::pivot_wider(
    names_from = term,
    values_from = estimate
  ) %>%
  dplyr::rename(Intercept = `(Intercept)`, slope = experience) %>%
  dplyr::mutate(pooled = "no_pool") %>%
  dplyr::select(pooled, level = department, Intercept, slope)

un_pooling
```





### 可视化 

```{r lmm-25}
library(ggrepel)

un_pooling %>%
  dplyr::bind_rows(partial_pooling) %>%
  ggplot(aes(x = Intercept, y = slope)) +
  purrr::map(
    c(seq(from = 0.1, to = 0.9, by = 0.1)),
    .f = function(level) {
      stat_ellipse(
        geom = "polygon", type = "norm",
        size = 0, alpha = 1 / 10, fill = "gray10",
        level = level
      )
    }
  ) +
  geom_point(aes(group = pooled, color = pooled)) +
  geom_line(aes(group = level), size = 1 / 4) +
  # geom_point(data = complete_pooling, size = 4, color = "red") +
  geom_text_repel(
    data = . %>% filter(pooled == "no_pool"),
    aes(label = level)
  ) +
  scale_color_manual(
    name = "information pool",
    values = c(
      "no_pool" = "black",
      "partial_pool" = "red" # ,
      # "complete_pool" = "#A65141"
    ),
    labels = c(
      "no_pool" = "no share",
      "partial_pool" = "partial share" # ,
      # "complete_pool" = "complete share"
    )
  ) #+
# theme_classic()
```


## 更多

- 解释模型的含义
```{r lmm-26, eval=FALSE}
lmer(salary ~ 1 + (0 + experience | department), data = df)
# vs
lmer(salary ~ 1 + experience + (0 + experience | department), data = df)
```


```{r lmm-27}
lmer(salary ~ 1 + (1 + experience | department), data = df)
# vs
lmer(salary ~ 1 + (1 | department) + (0 + experience | department), data = df)
```

- 课后阅读[文献](https://peerj.com/articles/4794/)，读完后大家一起分享
- 课后阅读 [Understanding mixed effects models through data simulation](https://osf.io/3cz2e/)，



```{r lmm-28, echo = F}
# remove the objects
# rm(list=ls())
rm(complete_pooling, create_data, df, m1, m2, m3, m4, no_pool, partial_pooling, un_pooling)
```

```{r lmm-29, echo = F, message = F, warning = F, results = "hide"}
pacman::p_unload(pacman::p_loaded(), character.only = TRUE)
```