Read Data
dta <- read.csv("C:/Users/ASUS/Desktop/data/visual_search.csv", header = T)
head(dta)
## Participant Dx Set.Size RT
## 1 9129 Control 1 786.50
## 2 9051 Control 1 935.83
## 3 9126 Control 1 750.83
## 4 9171* Control 1 1129.50
## 5 9176 Control 1 1211.33
## 6 9167 Control 1 1178.83
dta$Participant <- factor(dta$Participant)
dta$Dx <- factor(dta$Dx)
Visdualization 01
library(ggplot2)
ggplot(dta, aes(Set.Size, RT)) +
geom_line(aes(group = Participant), col = "black")+
geom_point(alpha=0.5)+
stat_smooth(aes(group=Dx), method="lm", formula= y ~ x)+
geom_smooth(method = "lm", se = F, lwd = .6)+
facet_wrap(~ Dx, nrow = 1) +
labs(x = "Set Size", y="Resonse Time")+
theme(axis.text.x = element_text(size = 6))
## `geom_smooth()` using formula 'y ~ x'
Analysis
library(lme4)
## Loading required package: Matrix
summary(m0 <- lmer(RT~Set.Size*Dx+(Set.Size|Participant), data=dta))
## boundary (singular) fit: see ?isSingular
## Linear mixed model fit by REML ['lmerMod']
## Formula: RT ~ Set.Size * Dx + (Set.Size | Participant)
## Data: dta
##
## REML criterion at convergence: 2186.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7577 -0.3130 -0.0750 0.3117 6.1372
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Participant (Intercept) 657489.0 810.86
## Set.Size 407.4 20.18 1.00
## Residual 772445.8 878.89
## Number of obs: 132, groups: Participant, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2078.75 270.97 7.672
## Set.Size 73.49 11.40 6.446
## DxControl -1106.05 366.89 -3.015
## Set.Size:DxControl -21.74 15.44 -1.408
##
## Correlation of Fixed Effects:
## (Intr) Set.Sz DxCntr
## Set.Size -0.071
## DxControl -0.739 0.053
## St.Sz:DxCnt 0.053 -0.739 -0.071
## convergence code: 0
## boundary (singular) fit: see ?isSingular
m0<- lmer(RT ~ Set.Size + Dx + Set.Size*Dx + (Set.Size|Participant), dta)
## boundary (singular) fit: see ?isSingular
summary(m0)
## Linear mixed model fit by REML ['lmerMod']
## Formula: RT ~ Set.Size + Dx + Set.Size * Dx + (Set.Size | Participant)
## Data: dta
##
## REML criterion at convergence: 2186.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7577 -0.3130 -0.0750 0.3117 6.1372
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Participant (Intercept) 657489.0 810.86
## Set.Size 407.4 20.18 1.00
## Residual 772445.8 878.89
## Number of obs: 132, groups: Participant, 33
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2078.75 270.97 7.672
## Set.Size 73.49 11.40 6.446
## DxControl -1106.05 366.89 -3.015
## Set.Size:DxControl -21.74 15.44 -1.408
##
## Correlation of Fixed Effects:
## (Intr) Set.Sz DxCntr
## Set.Size -0.071
## DxControl -0.739 0.053
## St.Sz:DxCnt 0.053 -0.739 -0.071
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Visdualization 02
ggplot(dta, aes(Set.Size, RT, shape=Dx, linetype=Dx)) +
geom_smooth(method='lm', formula= y~x, se=FALSE)+
stat_summary(fun="mean", geom="line") +
stat_summary(fun="mean", geom="point") +
stat_summary(fun.dta=mean_se,
geom="errorbar",
linetype="solid", width=0.5) +
scale_shape_manual(values = c(1, 2, 16)) +labs(x="Set.Size",
y="Reaction time",
linetype="Group", shape="Group") +
scale_x_continuous(breaks=seq(0,30,by=10))+
theme_minimal()+
theme(legend.justification=c(0.1, 1),
legend.position=c(0.1, 1),
legend.background=element_rect(fill="white",
color="black"))
## Warning: Ignoring unknown parameters: fun.dta
## No summary function supplied, defaulting to `mean_se()`
Visdualtion 3
plot(m0, resid(., type="pearson") ~ fitted(.)|Dx,
xlab="Fitted values", ylab= "Pearson residuals",
layout=c(2,1), pch=20, cex=.8,
abline=0, lty=3)
