hw1

1 load and see

pacman::p_load(WWGbook, tidyverse, lme4, nlme, merTools)
dta <- read.csv("visual_search.csv")
str(dta)

## 'data.frame':    132 obs. of  4 variables:
##  $ Participant: Factor w/ 33 levels "0042","0044",..: 19 16 18 25 28 22 24 23 26 27 ...
##  $ Dx         : Factor w/ 2 levels "Aphasic","Control": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Set.Size   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ RT         : num  786 936 751 1130 1211 ...

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

#change form
dta$Participant <- factor(dta$Participant)
dta$Dx <- factor(dta$Dx)
str(dta)

## 'data.frame':    132 obs. of  4 variables:
##  $ Participant: Factor w/ 33 levels "0042","0044",..: 19 16 18 25 28 22 24 23 26 27 ...
##  $ Dx         : Factor w/ 2 levels "Aphasic","Control": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Set.Size   : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ RT         : num  786 936 751 1130 1211 ...

2 plot

theme_set(theme_bw(base_size = 10))

#
ggplot(dta, aes(Set.Size, RT)) +
 geom_line(aes(group = Participant), alpha = .5) +
 stat_smooth(aes(group = Dx), method = "lm", formula = y ~ x) +
 geom_point(alpha = 0.5) +
 facet_wrap( ~ Dx) +
 labs(x = "Set size", y = "Reaction Time (ms)")

# set dodge amount
pd <- position_dodge(width=.2)

# As same sa teacher
ggplot(dta, aes(Set.Size, RT, shape = Dx, linetype = Dx)) +
  
 stat_smooth(method='lm', formula= y~x, geom = "line", position = pd) +
 stat_summary(fun = mean, geom = "point", position = pd) +
 stat_summary(fun.data = mean_se, geom = "errorbar", 
              position = pd, linetype = "solid", width = 0.5) +
 scale_shape_manual(values = c(1, 2, 16)) +
 labs(x = "Set size", y = "Reaction Time (ms)", linetype = "Group", shape = "Group") +
 theme(legend.justification = c(0, 1), legend.position = c(0, 1),
       legend.background = element_rect(fill = "white", color = "black"))

# model

summary(m1 <- lmer(RT ~ Set.Size | Participant, data = dta))

## Linear mixed model fit by REML ['lmerMod']
## Formula: RT ~ Set.Size | Participant
##    Data: dta
## 
## REML criterion at convergence: 2260.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5482 -0.4199 -0.0351  0.2632  6.0022 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev. Corr
##  Participant (Intercept) 909394   953.6        
##              Set.Size      3982    63.1    0.61
##  Residual                810733   900.4        
## Number of obs: 132, groups:  Participant, 33
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   1301.7      200.8   6.482

3 Residuals plots

plot(m1, resid(., type = "pearson") ~ fitted(.) | Dx, 
     layout = c(2,1), aspect = 2, abline = 0, pch = 20, cex = .8, 
     xlab = "Fitted values", ylab = "Pearson residuals")