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chapter_8.Rmd
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---
title: ""
subtitle: ""
author: ""
institute: ""
date: ""
output:
xaringan::moon_reader:
css: [default, css/Custumed_Style.css, css/zh-CN.css]
lib_dir: libs
nature:
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
---
class: center, middle
<span style="font-size: 60px;">第八章</span> <br>
<span style="font-size: 50px;">如何进行基本的数据分析 (二) </span> <br>
<span style="font-size: 50px;">相关和回归</span> <br>
<br>
<br>
<span style="font-size: 30px;">胡传鹏</span> <br>
<span style="font-size: 30px;">2023/04/10</span> <br>
---
class: center, middle
<span style="font-size: 60px;">8.1 相关分析</span> <br>
---
# <h1 lang="zh-CN">回顾相关分析基础知识</h1>
<br>
<center>
## What is correlation?
</center>
<br>
##
Correlation analysis is a statistical technique used to measure the strength and direction of the linear relationship between two variables.It involves calculating the correlation coefficient, which is a value that ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.
---
# <h1 lang="zh-CN">常用的相关方法 </h1>
<br>
# Pearson(皮尔逊)相关
<br>
## Pearson相关系数是最常用的方法之一，用于衡量两个变量之间的线性相关程度，取值范围为-1到1之间，其值越接近于1或-1表示两个变量之间的线性相关程度越强，而越接近于0则表示两个变量之间线性相关程度越弱或不存在线性相关性。
<br>
---
# <h1 lang="zh-CN">常用的相关方法 </h1>
<br>
# Spearman(斯皮尔曼)相关
<br>
## Spearman等级相关系数用于衡量两个变量之间的关联程度，但不要求变量呈现线性关系，而是通过对变量的等级进行比较来计算它们之间的相关性。
<br>
---
# <h1 lang="zh-CN">常用的相关方法 </h1>
<br>
# Kendall(肯德尔)相关
<br>
## Kendall秩相关系数也用于衡量两个变量之间的关联程度，其计算方式与Spearman等级相关系数类似，但它是基于每个变量的秩的比较来计算它们之间的相关性。
<br>
---
class: center, middle
<span style="font-size: 60px;">8.2 Correlation in R</span> <br>
---
# <h1 lang="zh-CN">假想问题</h1>
<br>
<br>
<h2 lang="zh-CN"> “在penguin数据中，参与者压力和自律水平的相关水平？”</h2>
<br>
---
# <h1 lang="zh-CN">载入包</h1>
```{r pacakge, message=FALSE}
# 检查是否已安装 pacman
if (!requireNamespace("pacman", quietly = TRUE)) {
install.packages("pacman") } # 如果未安装，则安装包
# 使用p_load来载入需要的包
pacman::p_load("tidyverse", "bruceR", "performance")
# 或者直接使用 easystats这个系列
pacman::p_load("tidyverse", "bruceR", "easystats")
```
---
# <h1 lang="zh-CN">检查工作路径 - 导入原始数据</h1>
```{r WD & df.mt.raw}
# 检查工作路径
getwd()
#读取数据
df.pg.raw <- read.csv('./data/penguin/penguin_rawdata.csv',
header = T,
sep=",",
stringsAsFactors = FALSE)
```
---
# <h1 lang="zh-CN">准备数据</h1>
# <h3 lang="zh-CN">数据预处理</h3>
```{r df.pg.mean}
df.pg.corr <- df.pg.raw %>%
dplyr::filter(sex > 0 & sex < 3) %>% # 筛选出男性和女性的数据
dplyr::select(sex,
starts_with("scontrol"),
starts_with("stress")) %>% # 筛选出需要的变量
dplyr::mutate(across(c(scontrol2, scontrol3,scontrol4,
scontrol5,scontrol7, scontrol9,
scontrol10,scontrol12,scontrol13,
stress4, stress5, stress6,stress7,
stress9, stress10,stress13),
~ case_when(. == '1' ~ '5',
. == '2' ~ '4',
. == '3' ~ '3',
. == '4' ~ '2',
. == '5' ~ '1',
TRUE ~ as.character(.)))
) %>% # 反向计分修正
dplyr::mutate(across(starts_with("scontrol")
| starts_with("stress"),
~ as.numeric(.))
) %>% # 将数据类型转化为numeric
dplyr::mutate(stress_mean =
rowMeans(select(.,starts_with("stress")),
na.rm = T),
scontrol_mean =
rowMeans(select(., starts_with("scontrol")),
na.rm = T)
) %>% # 根据子项目求综合平均
dplyr::select(sex, stress_mean, scontrol_mean)
```
---
# <h1 lang="zh-CN">准备数据</h1>
# <h3 lang="zh-CN">数据预处理</h3>
```{r df.pg.mean DT, echo=FALSE}
# check 5st five rows
# head(df.pg.mean, 5)
DT::datatable(head(df.pg.corr, 5),
fillContainer = TRUE, options = list(pageLength = 5))
```
---
# <h1 lang="zh-CN">bruceR::Corr()相关分析函数的参数</h1>
---
# <h1 lang="en">bruceR::Corr()</h1>
## <h2 lang="zh-CN">问题解决</h2>
```{r result.Corr}
results.Corr <- capture.output({
bruceR::Corr(data = df.pg.corr[,c(2,3)],
file = "./output/chp8/Corr.doc")
})
writeLines(results.Corr, "./output/chp8/Corr.md") # .md最整齐
```
---
# <h1 lang="en">bruceR::Corr()</h1>
## <h2 lang="zh-CN">更直观的查看相关性</h2>
```{r corr2}
#绘制相关散点图
pairs(df.pg.corr[,c(2,3)])
```
---
# <h1 lang="en">bruceR::Corr()</h1>
## <h2 lang="zh-CN">95% Confidence Intervals</h2>
<h4 lang="zh-CN">很好，毫不相关，大家可以用代码自己探索些相关关系了</h4>
---
class: center, middle
<span style="font-size: 60px;">8.3 回归分析</span> <br>
---
# <h1 lang="zh-CN">回顾回归分析基础知识</h1>
<br>
<center>
## What is regression?
</center>
<br>
- Regression models are used to describe relationships between variables by fitting a line to the observed data.
- Regression allows you to estimate how a dependent variable changes as the independent variable(s) change.
---
# <h1 lang="zh-CN">回顾回归分析基础知识</h1>
<br>
<center>
## Multiple linear regression formula
</center>
<br>
$$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_p x_p + \epsilon$$
---
# <h1 lang="zh-CN">回顾回归分析基础知识</h1>
<br>
<center>
## How to perform a multiple linear regression
</center>
<br>
### Multiple linear regression is used to estimate the relationship between two or more independent variables and one dependent variable.
<br>
When can we use it?
<br>
- How strong the relationship is between two or more independent variables and one dependent variable?
- The value of the dependent variable at a certain value of the independent variables.
<br>
## 使用lm()函数构建回归模型，步骤将在下文体现
---
# <h1 lang="zh-CN">bruceR::model_summary()的运用</h1>
<br>
## <h2 lang="zh-CN"> 灵活使用model_summary()函数汇总回归模型结果
---
# <h1 lang="zh-CN">假想的问题</h1>
<br>
<br>
<h2 lang="zh-CN"> “在penguin数据中，我们希望找到参与者压力和自律水平关于性别的回归？”</h2>
<br>
---
# <h1 lang="zh-CN">CODE_问题处理</h1>
# <h2 lang="zh-CN">数据预处理</h2>
```{r df.pg.lm}
# 数据预处理
df.pg.lm <- df.pg.corr %>%
# 将sex变量转化为factor类型
mutate(sex = as.factor(sex)) %>%
# 自变量为scontrol和sex
group_by(scontrol_mean,sex) %>%
# 根据分组获得stress的平均值，。groups属性保留了之前的group_by
summarise(stress_Mean = mean(stress_mean),.groups = 'keep')
```
---
# <h1 lang="zh-CN">CODE_问题处理</h1>
# <h2 lang="zh-CN">绘制散点图，检查是否存在回归关系</h2>
```{r plot.lm}
# 使用ggplot()画图
ggplot(df.pg.lm, aes(x = scontrol_mean, y = stress_Mean, color = sex)) +
geom_point() +
geom_smooth(method = "lm") +
scale_color_discrete(name = "Gender",
labels = c("Female", "Male")) +
theme_minimal()
```
---
# <h1 lang="zh-CN">CODE_问题处理</h1>
# <h2 lang="zh-CN">建立回归模型</h2>
```{r mod}
# 建立回归模型
mod <- lm(stress_Mean ~ scontrol_mean + sex + scontrol_mean:sex, df.pg.lm)
# 使用bruceR::model_summary()输出结果
result.lm <- capture.output({
model_summary(mod,
std = T,
file = "./output/chp8/Lm.doc")
})
writeLines(result.lm, "./output/chp8/Lm.md")
```
---
# <h1 lang="en">bruceR::lm()</h1>
## <h2 lang="zh-CN">显著性检验</h2>
---
# <h1 lang="en">CODE_问题处理</h1>
## <h2 lang="zh-CN">进一步检查模型</h2>
```{r check.model}
# 检查安装performance包
performance::check_model(mod)
```
---
# <h1 lang="zh-CN">课堂练习</h1>
<br>
<br>
<h2 lang="zh-CN">1. “在Penguin数据中，被试的依恋水平（ECR）和压力程度（stress）以及在线情况（onlineid）是否相关？”</h2>
<br>
<h2 lang="zh-CN">2. “对上述数据建立可靠的回归模型”</h2>
<br>