setwd("C:/Users/Owner/desktop")
library(lme4)
## Warning: パッケージ 'lme4' はバージョン 4.1.1 の R の下で造られました
##  要求されたパッケージ Matrix をロード中です

Including Plots

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library(lmerTest)
## Warning: パッケージ 'lmerTest' はバージョン 4.1.1 の R の下で造られました
## 
##  次のパッケージを付け加えます: 'lmerTest'
##  以下のオブジェクトは 'package:lme4' からマスクされています: 
## 
##      lmer
##  以下のオブジェクトは 'package:stats' からマスクされています: 
## 
##      step
data=read.delim("1103.txt", header=TRUE)
lmerTest.limit = 10000
fit_RT = lmer(RT ~ condition + (1|participant) + (1|item), data = data, control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit_RT)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RT ~ condition + (1 | participant) + (1 | item)
##    Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 18610.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7254 -0.5564 -0.1710  0.3009  6.6038 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  item        (Intercept)  27553   166.0   
##  participant (Intercept) 111490   333.9   
##  Residual                185637   430.9   
## Number of obs: 1235, groups:  item, 48; participant, 29
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)  1106.14      82.26   58.89  13.446   <2e-16 ***
## conditionB    118.73      76.28   42.59   1.556   0.1270    
## conditionC     67.38      76.44   42.89   0.881   0.3830    
## conditionD    255.37      76.22   42.39   3.350   0.0017 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) cndtnB cndtnC
## conditionB -0.466              
## conditionC -0.465  0.501       
## conditionD -0.466  0.503  0.501
library(ggplot2)
## Warning: パッケージ 'ggplot2' はバージョン 4.1.1 の R の下で造られました
ggplot(data=read.delim("1103.txt",header=TRUE),aes(x=condition,y=RT,fill=condition),show.legend=FALSE)+geom_boxplot()+stat_summary(fun=mean,geom="point",size=4,fill="white",pch=21)+ xlab("condition")+ ylab("RT")

fit_front = lmer(front ~ condition + (1|participant) + (1|item), data = data, control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit_front)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: front ~ condition + (1 | participant) + (1 | item)
##    Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 314.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8337 -0.5649 -0.1414  0.3456  6.2439 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  item        (Intercept) 0.01705  0.1306  
##  participant (Intercept) 0.03534  0.1880  
##  Residual                0.06410  0.2532  
## Number of obs: 1235, groups:  item, 48; participant, 29
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.52725    0.05344 67.72927   9.866 9.87e-15 ***
## conditionB   0.09732    0.05715 43.19691   1.703   0.0958 .  
## conditionC   0.05320    0.05723 43.40527   0.930   0.3577    
## conditionD   0.12583    0.05713 43.09980   2.203   0.0330 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) cndtnB cndtnC
## conditionB -0.536              
## conditionC -0.535  0.501       
## conditionD -0.536  0.502  0.501
library(ggplot2)
ggplot(data=read.delim("1103.txt",header=TRUE),aes(x=condition,y=front,fill=condition),show.legend=FALSE)+geom_boxplot()+stat_summary(fun=mean,geom="point",size=4,fill="white",pch=21)+ xlab("condition")+ ylab("AOI front")

fit_back = lmer(front ~ condition + (1|participant) + (1|item), data = data, control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit_back)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: front ~ condition + (1 | participant) + (1 | item)
##    Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 314.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8337 -0.5649 -0.1414  0.3456  6.2439 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  item        (Intercept) 0.01705  0.1306  
##  participant (Intercept) 0.03534  0.1880  
##  Residual                0.06410  0.2532  
## Number of obs: 1235, groups:  item, 48; participant, 29
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.52725    0.05344 67.72927   9.866 9.87e-15 ***
## conditionB   0.09732    0.05715 43.19691   1.703   0.0958 .  
## conditionC   0.05320    0.05723 43.40527   0.930   0.3577    
## conditionD   0.12583    0.05713 43.09980   2.203   0.0330 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) cndtnB cndtnC
## conditionB -0.536              
## conditionC -0.535  0.501       
## conditionD -0.536  0.502  0.501
ggplot(data=read.delim("1103.txt",header=TRUE),aes(x=condition,y=back,fill=condition),show.legend=FALSE)+geom_boxplot()+stat_summary(fun=mean,geom="point",size=4,fill="white",pch=21)+ xlab("condition")+ ylab("AOI back")

fit_sacade = lmer(front ~ condition + (1|participant) + (1|item), data = data, control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit_back)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: front ~ condition + (1 | participant) + (1 | item)
##    Data: data
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 314.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8337 -0.5649 -0.1414  0.3456  6.2439 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  item        (Intercept) 0.01705  0.1306  
##  participant (Intercept) 0.03534  0.1880  
##  Residual                0.06410  0.2532  
## Number of obs: 1235, groups:  item, 48; participant, 29
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)  0.52725    0.05344 67.72927   9.866 9.87e-15 ***
## conditionB   0.09732    0.05715 43.19691   1.703   0.0958 .  
## conditionC   0.05320    0.05723 43.40527   0.930   0.3577    
## conditionD   0.12583    0.05713 43.09980   2.203   0.0330 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) cndtnB cndtnC
## conditionB -0.536              
## conditionC -0.535  0.501       
## conditionD -0.536  0.502  0.501
ggplot(data=read.delim("1103.txt",header=TRUE),aes(x=condition,y=sacade,fill=condition),show.legend=FALSE)+geom_boxplot()+stat_summary(fun=mean,geom="point",size=4,fill="white",pch=21)+ xlab("condition")+ ylab("sacade")