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<!DOCTYPE html>
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<title>chapter_7.knit</title>
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class: center, middle
<span style="font-size: 60px;">第七章</span> <br>
<span style="font-size: 50px;">如何进行基本的数据分析 (一) </span> <br>
<span style="font-size: 50px;">*t*-test和ANOVA</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;">7.1 *t*-test</span> <br>
---
# <h1 lang="zh-CN">回顾*t*检验基础知识</h1>
<br>
<center>
## One sample *t*-test：单样本*t*检验
</center>
<br>
<br>
### **Compare the sample mean against a fixed value.**
<br>
## The hypotheses:
* `\(H_0\)`: The current sample is sampled from a population with μ of the current value.
* `\(H_1\)`: The current sample is sampled from a population with μ that different from the current value.
* The significance level: `\(\alpha\)` = 0.05
---
# <h1 lang="zh-CN">回顾*t*检验基础知识 </h1>
<br>
## Typical research questions are:与某一特定值比较的三种情况（大于/小于或等于)
<br>
* whether the mean (m) of the sample is equal to the theoretical mean, `\(\mu\)`?
* whether the mean (m) of the sample is less than the theoretical mean, `\(\mu\)`?
* whether the mean (m) of the sample is greater than the theoretical mean, `\(\mu\)`?
---
# <h1 lang="zh-CN">回顾*t*检验基础知识</h1>
<br>
<h2 lang="en">In statistics, we can define the corresponding null hypothesis ($H_0$) as follow:</h2>
<br>
* `\(H_0\)`: m = μ
* `\(H_0\)`: m <= μ
* `\(H_0\)`: m => μ
<br>
<br>
<h2 lang="en">The corresponding alternative hypotheses (H1) are as follow:</h2>
<br>
<h3 lang="en">H1:m ≠ μ (not equal to)</h3>
<h3 lang="en">H1:m > μ (greater than)</h3>
<h3 lang="en">H1:m < μ (less than)</h3>
---
# <h1 lang="zh-CN">回顾*t*检验基础知识</h1>
<br>
<center>
<h2 lang="en">Two-sample *t*-test ：双样本*t*检验</h2>
</center>
<br>
<h2 lang="en">Dependent(paried) *t*-test</h2>
<h3 lang="en">Population of difference scores has a normal distribution [population normality]</h3>
<h3 lang="en">Correlation between pairs of scores [dependent data]</h3>
<br>
<br>
<h2 lang="en">Independent *t*-test</h2>
<br>
<h3 lang="en">Population of difference scores has a normal distribution [population normality]</h3>
<h3 lang="en">The variances in two populations are similar [homogeneity of variance]</h3>
---
# <h1 lang="zh-CN">回顾*t*检验基础知识</h1>
<br>
<h2 lang="en">Dependent (Paired) *t*-test：配对样本*t*检验</h2>
<h3 lang="en">The paired *t*-test is used to compare the means between two related groups of samples. In this case, you have two values (i.e., pair of values) for the same samples.</h3>
<br>
<br>
<br>
<h2 lang="en">Independent *t*-test:独立样本*t*检验</h2>
<h2 lang="en">The independent samples *t*-test is used to compare the mean of two independent groups.</h2>
<br>
<h2 lang="en">Typical research questions are:</h2>
<h2 lang="en">whether the mean of group A (μ) is equal to the mean of group B (μ)?</h2>
<h2 lang="en">whether the mean of group A (μ) is less than the mean of group B (μ)?</h2>
<h2 lang="en">whether the mean of group A (μ) is greater than the mean of group B (μ)?</h2>
---
class: center, middle
<span style="font-size: 60px;">7.2 *t*-test in R</span> <br>
---
# <h1 lang="zh-CN">几个假想的问题</h1>
<br>
<br>
<h3 lang="zh-CN">1. “在penguin数据中，参与者对于家的依恋水平是否小于/等于/大于均值水平(假设在总体水平上，人们对家庭的依恋水平(HOME)均值为3.5)？”</h3>
<br>
<h3 lang="zh-CN">2. “在penguin数据中，男女生在亲密关系经验(ECR)中的得分是否存在显著差异？”</h3>
<br>
<h3 lang="zh-CN">3. “在penguin数据中，参与者在不同时间的温度差异是否具有显著性？”</h3>
---
# <h1 lang="zh-CN">载入包 - 设置工作路径 - 导入原始数据</h1>
```r
# 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")
```
```
##
## bruceR (version 0.8.9)
## BRoadly Useful Convenient and Efficient R functions
##
## Packages also loaded:
## √ dplyr √ emmeans √ ggplot2
## √ tidyr √ effectsize √ ggtext
## √ stringr √ performance √ cowplot
## √ forcats √ lmerTest √ see
## √ data.table
##
## Main functions of `bruceR`:
## cc() Describe() TTEST()
## add() Freq() MANOVA()
## .mean() Corr() EMMEANS()
## set.wd() Alpha() PROCESS()
## import() EFA() model_summary()
## print_table() CFA() lavaan_summary()
##
## https://psychbruce.github.io/bruceR/
##
## NEWS: A new version of bruceR (0.8.10) is available on 2023-03-03!
## ***** Please Update *****
## install.packages("bruceR", dep=TRUE)
##
## These R packages are dependencies of `bruceR` but not installed:
## lmtest, vars, phia, GGally, GPArotation
## ***** Please Install All Dependencies *****
## install.packages("bruceR", dep=TRUE)
```
---
# <h1 lang="zh-CN">载入包 - 设置工作路径 - 导入原始数据</h1>
```r
# WD <- here::here()
df.pg.raw <- read.csv('./data/penguin/penguin_rawdata.csv',
header = T, sep=",", stringsAsFactors = FALSE)
```
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class=\"display fill-container\">\n <thead>\n <tr>\n <th> <\/th>\n <th>age<\/th>\n <th>ALEX1<\/th>\n <th>ALEX2<\/th>\n <th>ALEX3<\/th>\n <th>ALEX4<\/th>\n <th>ALEX5<\/th>\n <th>ALEX6<\/th>\n <th>ALEX7<\/th>\n <th>ALEX8<\/th>\n <th>ALEX9<\/th>\n <th>ALEX10<\/th>\n <th>ALEX11<\/th>\n <th>ALEX12<\/th>\n <th>ALEX13<\/th>\n <th>ALEX14<\/th>\n <th>ALEX15<\/th>\n <th>ALEX16<\/th>\n <th>anxiety<\/th>\n <th>artgluctot<\/th>\n <th>attachhome<\/th>\n <th>attachphone<\/th>\n <th>AvgHumidity<\/th>\n <th>avoidance<\/th>\n <th>cigs<\/th>\n <th>DIDF<\/th>\n <th>eatdrink<\/th>\n <th>ECR1<\/th>\n <th>ECR2<\/th>\n <th>ECR3<\/th>\n <th>ECR4<\/th>\n <th>ECR5<\/th>\n <th>ECR6<\/th>\n <th>ECR7<\/th>\n <th>ECR8<\/th>\n <th>ECR9<\/th>\n <th>ECR10<\/th>\n <th>ECR11<\/th>\n <th>ECR12<\/th>\n <th>ECR13<\/th>\n <th>ECR14<\/th>\n <th>ECR15<\/th>\n <th>ECR16<\/th>\n <th>ECR17<\/th>\n <th>ECR18<\/th>\n <th>ECR19<\/th>\n <th>ECR20<\/th>\n <th>ECR21<\/th>\n <th>ECR22<\/th>\n <th>ECR23<\/th>\n <th>ECR24<\/th>\n <th>ECR25<\/th>\n <th>ECR26<\/th>\n <th>ECR27<\/th>\n <th>ECR28<\/th>\n <th>ECR29<\/th>\n <th>ECR30<\/th>\n <th>ECR31<\/th>\n <th>ECR32<\/th>\n <th>ECR33<\/th>\n <th>ECR34<\/th>\n <th>ECR35<\/th>\n <th>ECR36<\/th>\n <th>endtime<\/th>\n <th>EOT<\/th>\n <th>exercise<\/th>\n <th>gluctot<\/th>\n <th>health<\/th>\n <th>HOME1<\/th>\n <th>HOME2<\/th>\n <th>HOME3<\/th>\n <th>HOME4<\/th>\n <th>HOME5<\/th>\n <th>HOME6<\/th>\n <th>HOME7<\/th>\n <th>HOME8<\/th>\n <th>HOME9<\/th>\n <th>KAMF1<\/th>\n <th>KAMF2<\/th>\n <th>KAMF3<\/th>\n <th>KAMF4<\/th>\n <th>KAMF5<\/th>\n <th>KAMF6<\/th>\n <th>KAMF7<\/th>\n <th>networksize<\/th>\n <th>nostalgia<\/th>\n <th>onlineid<\/th>\n <th>onlineid1<\/th>\n <th>onlineid2<\/th>\n <th>onlineid3<\/th>\n <th>onlineid4<\/th>\n <th>onlineid5<\/th>\n <th>onlineid6<\/th>\n <th>onlineid7<\/th>\n <th>onlineid8<\/th>\n <th>onlineid9<\/th>\n <th>onlineid10<\/th>\n <th>onlineide11<\/th>\n <th>phone1<\/th>\n <th>phone2<\/th>\n <th>phone3<\/th>\n <th>phone4<\/th>\n <th>phone5<\/th>\n <th>phone6<\/th>\n <th>phone7<\/th>\n <th>phone8<\/th>\n <th>phone9<\/th>\n <th>romantic<\/th>\n <th>scontrol1<\/th>\n <th>scontrol2<\/th>\n <th>scontrol3<\/th>\n <th>scontrol4<\/th>\n <th>scontrol5<\/th>\n <th>scontrol6<\/th>\n <th>scontrol7<\/th>\n <th>scontrol8<\/th>\n <th>scontrol9<\/th>\n <th>scontrol10<\/th>\n <th>scontrol11<\/th>\n <th>scontrol12<\/th>\n <th>scontrol13<\/th>\n <th>selfcontrol<\/th>\n <th>sex<\/th>\n <th>smoke<\/th>\n <th>SNI1<\/th>\n <th>SNI2<\/th>\n <th>SNI3<\/th>\n <th>SNI4<\/th>\n <th>SNI5<\/th>\n <th>SNI6<\/th>\n <th>SNI7<\/th>\n <th>SNI8<\/th>\n <th>SNI9<\/th>\n <th>SNI10<\/th>\n <th>SNI11<\/th>\n <th>SNI12<\/th>\n <th>SNI13<\/th>\n <th>SNI14<\/th>\n <th>SNI15<\/th>\n <th>SNI16<\/th>\n <th>SNI17<\/th>\n <th>SNI18<\/th>\n <th>SNI19<\/th>\n <th>SNI20<\/th>\n <th>SNI21<\/th>\n <th>SNI22<\/th>\n <th>SNI23<\/th>\n <th>SNI24<\/th>\n <th>SNI25<\/th>\n <th>SNI26<\/th>\n <th>SNI27<\/th>\n <th>SNI28<\/th>\n <th>SNI29<\/th>\n <th>SNI30<\/th>\n <th>SNI31<\/th>\n <th>SNI32<\/th>\n <th>SNS1<\/th>\n <th>SNS2<\/th>\n <th>SNS3<\/th>\n <th>SNS4<\/th>\n <th>SNS5<\/th>\n <th>SNS6<\/th>\n <th>SNS7<\/th>\n <th>socialdiversity<\/th>\n <th>socialembedded<\/th>\n <th>STRAQ_1<\/th>\n <th>STRAQ_2<\/th>\n <th>STRAQ_3<\/th>\n <th>STRAQ_4<\/th>\n <th>STRAQ_6<\/th>\n <th>STRAQ_7<\/th>\n <th>STRAQ_8<\/th>\n <th>STRAQ_9<\/th>\n <th>STRAQ_10<\/th>\n <th>STRAQ_11<\/th>\n <th>STRAQ_12<\/th>\n <th>STRAQ_19<\/th>\n <th>STRAQ_20<\/th>\n <th>STRAQ_21<\/th>\n <th>STRAQ_22<\/th>\n <th>STRAQ_23<\/th>\n <th>STRAQ_24<\/th>\n <th>STRAQ_25<\/th>\n <th>STRAQ_26<\/th>\n <th>STRAQ_27<\/th>\n <th>STRAQ_28<\/th>\n <th>STRAQ_29<\/th>\n <th>STRAQ_30<\/th>\n <th>STRAQ_31<\/th>\n <th>STRAQ_32<\/th>\n <th>STRAQ_33<\/th>\n <th>STRAQ_5<\/th>\n <th>STRAQ_13<\/th>\n <th>STRAQ_14<\/th>\n <th>STRAQ_15<\/th>\n <th>STRAQ_16<\/th>\n <th>STRAQ_17<\/th>\n <th>STRAQ_18<\/th>\n <th>STRAQ_34<\/th>\n <th>STRAQ_35<\/th>\n <th>STRAQ_36<\/th>\n <th>STRAQ_37<\/th>\n <th>STRAQ_38<\/th>\n <th>STRAQ_39<\/th>\n <th>STRAQ_40<\/th>\n <th>STRAQ_41<\/th>\n <th>STRAQ_42<\/th>\n <th>STRAQ_43<\/th>\n <th>STRAQ_44<\/th>\n <th>STRAQ_45<\/th>\n <th>STRAQ_46<\/th>\n <th>STRAQ_47<\/th>\n <th>STRAQ_48<\/th>\n <th>STRAQ_49<\/th>\n <th>STRAQ_50<\/th>\n <th>STRAQ_51<\/th>\n <th>STRAQ_52<\/th>\n <th>STRAQ_53<\/th>\n <th>STRAQ_54<\/th>\n <th>STRAQ_55<\/th>\n <th>STRAQ_56<\/th>\n <th>STRAQ_57<\/th>\n <th>stress<\/th>\n <th>stress1<\/th>\n <th>stress2<\/th>\n <th>stress3<\/th>\n <th>stress4<\/th>\n <th>stress5<\/th>\n <th>stress6<\/th>\n <th>stress7<\/th>\n <th>stress8<\/th>\n <th>stress9<\/th>\n <th>stress10<\/th>\n <th>stress11<\/th>\n <th>stress12<\/th>\n <th>stress13<\/th>\n <th>stress14<\/th>\n <th>Temperature_t1<\/th>\n <th>Temperature_t2<\/th>\n <th>thermotype<\/th>\n <th>avgtemp<\/th>\n <th>filter_.<\/th>\n <th>mintemp<\/th>\n <th>language<\/th>\n <th>langfamily<\/th>\n <th>Zanxiety<\/th>\n <th>Zavoidance<\/th>\n <th>Site<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
---
# <h1 lang="zh-CN">准备数据</h1>
# <h3 lang="zh-CN">对感兴趣问题所涉及题目求均值</h3>
```r
df.pg.mean <- df.pg.raw %>%
dplyr::filter(sex > 0 & sex < 3) %>% # 筛选出男性和女性的数据
dplyr::mutate(ECR_mean=rowMeans(select(., starts_with("ECR")), na.rm = T),
HOME_mean=rowMeans(select(., starts_with("HOME")), na.rm = T),
sex=as.factor(sex)
) %>%
dplyr::select(age, sex, romantic, ECR_mean, HOME_mean, Temperature_t1, Temperature_t2)
```
<div id="htmlwidget-ec9b6318d91b5cce956e" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-ec9b6318d91b5cce956e">{"x":{"filter":"none","vertical":false,"fillContainer":true,"data":[["1","2","3","4","5"],[1975,1995,1995,1988,1991],["1","1","1","2","1"],[2,2,1,1,2],[3.5,3.52777777777778,1.52777777777778,4.33333333333333,3.52777777777778],[3.11111111111111,4.88888888888889,1.33333333333333,3.88888888888889,4.66666666666667],[34.9,35.8,34,36.9,35.3],[35.9,36,36.2,37,36.1]],"container":"<table class=\"display fill-container\">\n <thead>\n <tr>\n <th> <\/th>\n <th>age<\/th>\n <th>sex<\/th>\n <th>romantic<\/th>\n <th>ECR_mean<\/th>\n <th>HOME_mean<\/th>\n <th>Temperature_t1<\/th>\n <th>Temperature_t2<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,3,4,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
---
# <h1 lang="zh-CN">TTEST()：bruceR中*t*检验函数的参数</h1>
---
# <h1 lang="en">bruceR::TTEST</h1>
## <h2 lang="zh-CN">问题1: 单样本*t*检验</h2>
```r
# bruceR::TTEST(df.pg.mean, "HOME_mean", test.value = 3.5, test.sided = ">")
# bruceR::TTEST(df.pg.mean, "HOME_mean", test.value = 3.5, test.sided = "<")
result.ttest <- capture.output({
bruceR::TTEST(data=df.pg.mean,
y="HOME_mean",
test.value = 3.5,
test.sided = "=",
file = "./output/chp7/one_sample_t.doc")
})
```
```
## t is large; approximation invoked.
```
```r
writeLines(result.ttest, "./output/chp7/single_t.md") # .md最整齐
```
---
# <h1 lang="en">bruceR::TTEST</h1>
## <h2 lang="en">OUTPUT</h2>
---
# <h1 lang="en">bruceR::TTEST</h1>
## <h2 lang="zh-CN">问题2: 独立样本*t*检验</h2>
```r
result.ttest <- capture.output({
bruceR::TTEST(data=df.pg.mean,
y="ECR_mean",
x="sex")
})
writeLines(result.ttest, "./output/chp7/inde_t.md")
```
---
# <h1 lang="en">bruceR::TTEST</h1>
## <h2 lang="en">OUTPUT</h2>
---
# <h1 lang="en">bruceR::TTEST</h1>
## <h2 lang="zh-CN">问题3: 配对样本*t*检验</h2>
```r
result.ttest <- capture.output({
bruceR::TTEST(data=df.pg.mean,
y = c("Temperature_t1",
"Temperature_t2"),
paired = T) #是否为配对样本*t*检验？默认是FALSE
})
```
```
## t is large; approximation invoked.
```
```r
writeLines(result.ttest, "./output/chp7/pair_t.md")
```
---
# <h1 lang="en">bruceR::TTEST</h1>
## <h2 lang="en">OUTPUT</h2>
---
# <h1 lang="en">Reporting *t*-test results in APA</h1>
<br>
<br>
<h3 lang="en">The basic format for reporting the result of a *t*-test is the same in each case:</h3>
<h3 lang="en">*t*(degress of freedom) = the t-value , *p* = p-value, Cohen's *d* = effect size</h3>
<br>
<h4 lang="zh-CN">以上均与假想的问题无关，大家可结合自己的数据改写代码</h4>
---
class: center, middle
<span style="font-size: 60px;">7.3 anova</span> <br>
---
# <h1 lang="zh-CN">回顾ANOVA的基础知识</h1>
<br>
---
# <h1 lang="zh-CN">回顾ANOVA的基础知识</h1>
---
# <h1 lang="zh-CN">回顾ANOVA的基础知识</h1>
---
# <h1 lang="zh-CN">回顾ANOVA的基础知识</h1>
<br>
<h2 lang="zh-CN">多因素方差分析概述</h2>
<br>
<h3 lang="zh-CN">多因素方差分析原理与单因素方差分析基本一致，即利用方差比较的方法，通过假设检验的过程来判断多个因素是否对因变量产生显著性影响。当有两个及以上因素对因变量产生影响时，使用多因素方差分析。多因素方差分析不仅要考虑每个因素的主效应，往往还要考虑因素之间的交互效应。</h3>
<br>
---
class: center, middle
<span style="font-size: 60px;">7.4 anova in R</span> <br>
---
# <h1 lang="zh-CN">假想的问题</h1>
<br>
<br>
<h2 lang="zh-CN">1. “在match数据中，被试在匹配任务和不匹配任务上的反应时是否存在差异？”</h2>
<br>
<h2 lang="zh-CN">2. “在match数据中，被试在不同身份(self vs other)与不同效价(moral vs immoral)的条件组合下反应时是否存在差异？”</h2>
<br>
---
# <h1 lang="zh-CN">载入包</h1>
```r
pacman::p_load("tidyverse", "bruceR")
```
---
# <h1 lang="zh-CN">设置工作路径 - 导入数据与预处理</h1>
```r
df.match <- read.csv('./data/match/match_raw.csv',
header = T, sep=",", stringsAsFactors = FALSE) %>%
# 拆分单元格内字符串
tidyr::separate(col=Shape,
into=c("Valence","Identity"),
sep="(?<=moral|immoral)(?=Self|Other)") %>%
dplyr::select(Sub, Valence, Identity, everything()) %>%
dplyr::filter(ACC == 1) %>% # 只选择回答正确的数据
dplyr::filter(!is.na(RT)) %>% # 剔除缺失值
# remove outliers below and above 3rd sd
dplyr::filter(RT > quantile(RT, 0.0015) & RT < quantile(RT, 0.9985)) %>%
dplyr::mutate(RT = as.numeric(RT)) %>%
dplyr::mutate(Valence = as.factor(Valence),
Identity = as.factor(Identity))
```
---
<br>
<br>
```r
DT::datatable(head(df.match, 10),
fillContainer = TRUE, options = list(pageLength = 5))
```
<div id="htmlwidget-2d599b408819a5a6ba11" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-2d599b408819a5a6ba11">{"x":{"filter":"none","vertical":false,"fillContainer":true,"data":[["1","2","3","4","5","6","7","8","9","10"],[7302,7302,7302,7302,7302,7302,7302,7302,7302,7302],["moral","immoral","immoral","immoral","moral","moral","moral","immoral","moral","immoral"],["Other","Other","Self","Self","Other","Self","Other","Self","Other","Other"],["02-May-2018_14:23:08","02-May-2018_14:23:10","02-May-2018_14:23:15","02-May-2018_14:23:17","02-May-2018_14:23:19","02-May-2018_14:23:21","02-May-2018_14:23:24","02-May-2018_14:23:26","02-May-2018_14:23:28","02-May-2018_14:23:30"],["Exp","Exp","Exp","Exp","Exp","Exp","Exp","Exp","Exp","Exp"],[22,22,22,22,22,22,22,22,22,22],["female","female","female","female","female","female","female","female","female","female"],["R","R","R","R","R","R","R","R","R","R"],[1,1,1,1,1,1,1,1,1,1],[1,1,1,1,1,1,1,1,1,1],[2,3,5,6,7,8,9,10,11,12],["moralOther","immoralOther","immoralSelf","immoralSelf","moralOther","moralSelf","moralOther","immoralSelf","moralOther","immoralOther"],["mismatch","mismatch","match","match","match","match","mismatch","mismatch","mismatch","match"],["n","n","m","m","m","m","n","n","n","m"],["n","n","m","m","m","m","n","n","n","m"],[1,1,1,1,1,1,1,1,1,1],[0.7043,0.9903,0.8207,0.7547,0.5429,0.9009,0.9551,0.6952,0.7593,0.7135]],"container":"<table class=\"display fill-container\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Sub<\/th>\n <th>Valence<\/th>\n <th>Identity<\/th>\n <th>Date<\/th>\n <th>Prac<\/th>\n <th>Age<\/th>\n <th>Sex<\/th>\n <th>Hand<\/th>\n <th>Block<\/th>\n <th>Bin<\/th>\n <th>Trial<\/th>\n <th>Label<\/th>\n <th>Match<\/th>\n <th>CorrResp<\/th>\n <th>Resp<\/th>\n <th>ACC<\/th>\n <th>RT<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,6,9,10,11,16,17]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
---
# <h1 lang="zh-CN">CODE_问题1处理</h1>
# <h2 lang="zh-CN">计算被试匹配与不匹配任务上的平均反应时间及其标准差</h2>
```r
df.match.mean <- df.match %>%
dplyr::group_by(Sub, Match) %>%
dplyr::summarise(
n = n(),
rt_sd = sd(as.numeric(RT), na.rm = T),
rt_mean = mean(RT, na.rm = T), # 对于factor数据，先转成character，再变成numeric
) %>%
dplyr::ungroup()
```
```
## `summarise()` has grouped output by 'Sub'. You can override using the `.groups`
## argument.
```
---
<br>
<br>
```r
DT::datatable(head(df.match.mean, 10),
fillContainer = TRUE, options = list(pageLength = 5))
```
<div id="htmlwidget-9123a1bb6e93eaccd389" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-9123a1bb6e93eaccd389">{"x":{"filter":"none","vertical":false,"fillContainer":true,"data":[["1","2","3","4","5","6","7","8","9","10"],[7302,7302,7303,7303,7304,7304,7305,7305,7306,7306],["match","mismatch","match","mismatch","match","mismatch","match","mismatch","match","mismatch"],[160,149,148,133,245,194,274,226,270,252],[0.106612719566056,0.0994899939093511,0.115424425115395,0.0881816560548197,0.137261728375444,0.106883820747074,0.128272398592039,0.121570749496483,0.135007585074087,0.109344555569399],[0.66311875,0.743634228187919,0.704106756756757,0.756554887218045,0.681796326530612,0.751001546391753,0.604401094890511,0.719050442477876,0.708901481481481,0.77705]],"container":"<table class=\"display fill-container\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Sub<\/th>\n <th>Match<\/th>\n <th>n<\/th>\n <th>rt_sd<\/th>\n <th>rt_mean<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,3,4,5]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
---
# <h1 lang="zh-CN">CODE_问题1处理</h1>
# <h2 lang="zh-CN">单因素被试内设计</h2>
```r
#比较被试在匹配任务和不匹配任务上的反应时是否存在差异
#单因素被试内设计（长型数据）
result.anova <- capture.output({
df_within <- df.match.mean %>%
bruceR::MANOVA(subID="Sub", # 被试id
dv="rt_mean", # 因变量，因为是MANOVA，实际上因变量可以再后面设置很多个
within="Match") # 设置条件，因素分析的条件
})
```
```
##
## * Data are aggregated to mean (across items/trials)
## if there are >=2 observations per subject and cell.
## You may use Linear Mixed Model to analyze the data,
## e.g., with subjects and items as level-2 clusters.
```
```r
writeLines(result.anova, "./output/chp7/anova_1.md")
# 若不符合球形假设要加上：sph.correction = "GG"
```
---
# <h1 lang="en">bruceR::MANOVA</h1>
## <h2 lang="en">OUTPUT</h2>
---
# <h1 lang="zh-CN">ANOVA问题2</h1>
# <h2 lang="zh-CN">计算被试在不同身份和效价条件下的平均反应时间及其标准差</h2>
```r
df.match.mean <- df.match %>%
dplyr::group_by(Sub,Sex, Valence,Identity) %>%
dplyr::summarise(
n = n(),
rt_sd = sd(as.numeric(RT), na.rm = T),
rt_mean = mean(as.numeric(RT), na.rm = T)
) %>%
dplyr::ungroup()
```
```
## `summarise()` has grouped output by 'Sub', 'Sex', 'Valence'. You can override
## using the `.groups` argument.
```
---
<br>
<br>
```r
DT::datatable(head(df.match.mean, 10),
fillContainer = TRUE, options = list(pageLength = 5))
```
<div id="htmlwidget-f8aa113af01315c08456" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-f8aa113af01315c08456">{"x":{"filter":"none","vertical":false,"fillContainer":true,"data":[["1","2","3","4","5","6","7","8","9","10"],[7302,7302,7302,7302,7303,7303,7303,7303,7304,7304],["female","female","female","female","male","male","male","male","female","female"],["immoral","immoral","moral","moral","immoral","immoral","moral","moral","immoral","immoral"],["Other","Self","Other","Self","Other","Self","Other","Self","Other","Self"],[78,76,84,71,75,67,69,70,95,114],[0.106546391467748,0.10670443377723,0.114524632384628,0.103786896685185,0.0914622051219287,0.0996700756728033,0.118901185681732,0.109463312146144,0.110340817703657,0.118828048280786],[0.705970512820513,0.724085526315789,0.661688095238095,0.721443661971831,0.741282666666667,0.753758208955224,0.720326086956522,0.700415714285714,0.756267368421053,0.729615789473684]],"container":"<table class=\"display fill-container\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Sub<\/th>\n <th>Sex<\/th>\n <th>Valence<\/th>\n <th>Identity<\/th>\n <th>n<\/th>\n <th>rt_sd<\/th>\n <th>rt_mean<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"columnDefs":[{"className":"dt-right","targets":[1,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
---
# <h1 lang="zh-CN">MANOVA()：多因素被试间/被试内/混合设计方差分析ANOVA</h1>
# <h2 lang="zh-CN">主要参数</h2>
---
# <h1 lang="zh-CN">问题2的R语法</h1>
# <h2 lang="zh-CN">长数据转换为宽数据</h2>
```r
df.match.within <- df.match.mean %>%
dplyr::select(-c(n, rt_sd)) %>%
dplyr::mutate(Valence = paste("A_", Valence, sep = ""),#将变量的名称进行修改转换，不生产新的变量
Identity = paste("B_", Identity, sep = "")) %>%
# 将morality和identity组合名称起来生成一个新的变量"Conds"
tidyr::unite("Conds", Valence:Identity, sep = "&",remove=TRUE) %>%
tidyr::pivot_wider(names_from = Conds,
values_from = rt_mean)
head(df.match.within)
```
```
## # A tibble: 6 × 6
## Sub Sex `A_immoral&B_Other` `A_immoral&B_Self` `A_moral&B_Other` A_mora…¹
## <int> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 7302 female 0.706 0.724 0.662 0.721
## 2 7303 male 0.741 0.754 0.720 0.700
## 3 7304 female 0.756 0.730 0.757 0.628
## 4 7305 male 0.685 0.659 0.641 0.638
## 5 7306 male 0.778 0.763 0.660 0.771
## 6 7307 female 0.751 0.677 0.716 0.728
## # … with abbreviated variable name ¹`A_moral&B_Self`
```
```r
#str(df.match.within)
```
---
# <h1 lang="zh-CN">问题2的R语法</h1>
# <h2 lang="zh-CN">两因素被试内设计_宽数据</h2>
```r
#被试在不同身份(self vs other)与不同效价(moral vs moral)的条件组合下反应时是否存在差异）
result.anova <- capture.output({
res_rmANOVA_1 <- bruceR::MANOVA(data=df.match.within,
dvs="A_immoral&B_Other:A_moral&B_Self",
dvs.pattern="A_(.+)B_(.+)",#正则表达式
within=c("A_","B_"))#表示2个主条件
})
```
```
##
## Note:
## dvs="A_immoral&B_Other:A_moral&B_Self" is matched to variables:
## A_immoral&B_Other, A_immoral&B_Self, A_moral&B_Other, A_moral&B_Self
```
```r
writeLines(result.anova, "./output/chp7/anova_2.md")
```
---
# <h1 lang="en">bruceR::MANOVA</h1>
## <h2 lang="en">OUTPUT</h2>
---
# <h1 lang="en">bruceR::MANOVA</h1>
## <h2 lang="en">OUTPUT</h2>
---
# <h1 lang="zh-CN">问题2的R语法</h1>
# <h2 lang="zh-CN">两因素被试内设计_长数据</h2>
```r
#被试在不同身份(self vs other)与不同效价(good vs bad)的条件组合下反应时是否存在差异）
#head(df.match.mean)
result.anova <- capture.output({
res_rmANOVA_2 <- bruceR::MANOVA(data=df.match.mean,
dv="rt_mean",
within=c("Valence","Identity"),
subID="Sub")
})
```
```
##
## * Data are aggregated to mean (across items/trials)
## if there are >=2 observations per subject and cell.
## You may use Linear Mixed Model to analyze the data,
## e.g., with subjects and items as level-2 clusters.
```
```r
writeLines(result.anova, "./output/chp7/anova_3.md")
```
---
# <h1 lang="zh-CN">简单效应检验与多重比较</h1>
# <h2 lang="zh-CN">主要参数</h2>
---
# <h1 lang="zh-CN">简单效应检验与多重比较</h1>
# <h2 lang="zh-CN">问题2的简单效应比较</h2>
```r
result.check <- capture.output({
sim_eff_1 <- res_rmANOVA_1 %>%
bruceR::EMMEANS("A_", by="B_")#简单效应分析
})
writeLines(result.check, "./output/chp7/check.md")
#sim_eff_1 <- res_rmANOVA_1 %>%
#EMMEANS("B_", by="A_") 和上一个同理，只是转换了不同的形式
```
---
# <h1 lang="zh-CN">被试间设计的语法实现</h1>
# <h2 lang="en">性别与亲密关系对ECR得分的效应</h2>
```r
# MANOVA(data, dv="SCORE", between=c("A", "B"))
# data 代表两因素的数据类型
# dv 因变量
# between 被试间因素的名称，此处表示两因素被试间设计，如果只有一个就是单因素被试间设计
res_ANOVA_btw <- df.pg.mean %>%
bruceR::MANOVA(.,
dv="ECR_mean",
between = c("sex", "romantic"))
```
```
##
## ====== ANOVA (Between-Subjects Design) ======
##
## Descriptives:
## ───────────────────────────────────
## "sex" "romantic" Mean S.D. n
## ───────────────────────────────────
## sex1 romantic1 3.006 (0.843) 236
## sex1 romantic2 3.686 (0.651) 206
## sex2 romantic1 2.867 (0.845) 594
## sex2 romantic2 3.747 (0.742) 454
## ───────────────────────────────────
## Total sample size: N = 1490
##
## ANOVA Table:
## Dependent variable(s): ECR_mean
## Between-subjects factor(s): sex, romantic
## Within-subjects factor(s): –
## Covariate(s): –
## ─────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI of η²p] η²G
## ─────────────────────────────────────────────────────────────────────────────────
## sex 0.457 0.623 1 1486 0.733 .392 .000 [.000, .004] .000
## romantic 187.454 0.623 1 1486 300.682 <.001 *** .168 [.141, .228] .168
## sex * romantic 3.091 0.623 1 1486 4.959 .026 * .003 [.000, .010] .003
## ─────────────────────────────────────────────────────────────────────────────────
## MSE = mean square error (the residual variance of the linear model)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## ω²p = partial omega-squared = (F - 1) * df1 / (F * df1 + df2 + 1)
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## Cohen’s f² = η²p / (1 - η²p)
##
## Levene’s Test for Homogeneity of Variance:
## ───────────────────────────────────────────
## Levene’s F df1 df2 p
## ───────────────────────────────────────────
## DV: ECR_mean 12.755 3 1486 <.001 ***
## ───────────────────────────────────────────
```
---
# <h1 lang="zh-CN">混合设计的语法实现</h1>
# <h2 lang="en">CODE</h2>
<h3 lang="zh-CN">以2个被试间1个被试内的混合设计形式为例</h3>
```r
# 假设数据集是mixed.3_2b1w
# A、C是被试间变量，B是被试内变量
# MANOVA(mixed.3_2b1w, dvs="B1:B2", dvs.pattern="B(.)",
#between=c("A", "C"), within="B")
res_ANOVA_mix <- df.match.mean %>%
dplyr::mutate(Sex = ifelse(Sex =="1", "male",
ifelse(Sex == "2", "female", Sex))) %>%
bruceR::MANOVA(.,
subID = "Sub",
dv="rt_mean",
between = c("Sex"),
within = c("Valence", "Identity"))
```
```
##
## * Data are aggregated to mean (across items/trials)
## if there are >=2 observations per subject and cell.
## You may use Linear Mixed Model to analyze the data,
## e.g., with subjects and items as level-2 clusters.
```
```
##
## ====== ANOVA (Mixed Design) ======
##
## Descriptives:
## ─────────────────────────────────────────────
## "Sex" "Valence" "Identity" Mean S.D. n
## ─────────────────────────────────────────────
## female immoral Other 0.717 (0.065) 27
## female immoral Self 0.733 (0.073) 27
## female moral Other 0.709 (0.077) 27
## female moral Self 0.677 (0.063) 27
## male immoral Other 0.721 (0.050) 17
## male immoral Self 0.736 (0.043) 17
## male moral Other 0.704 (0.049) 17
## male moral Self 0.689 (0.044) 17
## ─────────────────────────────────────────────
## Total sample size: N = 44
##
## ANOVA Table:
## Dependent variable(s): rt_mean
## Between-subjects factor(s): Sex
## Within-subjects factor(s): Valence, Identity
## Covariate(s): –
## ───────────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI of η²p] η²G
## ───────────────────────────────────────────────────────────────────────────────────────
## Sex 0.000 0.012 1 42 0.042 .839 .001 [.000, .053] .001
## Valence 0.042 0.001 1 42 46.148 <.001 *** .524 [.346, .649] .062
## Sex * Valence 0.000 0.001 1 42 0.000 .990 .000 [.000, .000] .000
## Identity 0.001 0.001 1 42 0.626 .433 .015 [.000, .123] .001
## Sex * Identity 0.001 0.001 1 42 0.459 .502 .011 [.000, .113] .001
## Valence * Identity 0.015 0.001 1 42 12.169 .001 ** .225 [.065, .394] .023
## Sex * Valence * Identity 0.001 0.001 1 42 0.605 .441 .014 [.000, .122] .001
## ───────────────────────────────────────────────────────────────────────────────────────
## MSE = mean square error (the residual variance of the linear model)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## ω²p = partial omega-squared = (F - 1) * df1 / (F * df1 + df2 + 1)
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## Cohen’s f² = η²p / (1 - η²p)
##
## Levene’s Test for Homogeneity of Variance:
## ───────────────────────────────────────────────
## Levene’s F df1 df2 p
## ───────────────────────────────────────────────
## DV: immoral_Other 0.691 1 42 .410
## DV: immoral_Self 1.866 1 42 .179
## DV: moral_Other 2.753 1 42 .105
## DV: moral_Self 1.679 1 42 .202
## ───────────────────────────────────────────────
##
## Mauchly’s Test of Sphericity:
## The repeated measures have only two levels. The assumption of sphericity is always met.
```
---
# <h1 lang="zh-CN">课堂练习</h1>
<br>
<br>
<h2 lang="zh-CN">1. “在Penguin数据中，不同站点(“Site”)的被试在主观压力上是否有差异？”</h2>
<br>
<h2 lang="zh-CN">2. “在match数据中，如何分析 *d'*”</h2>
<br>
</textarea>
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