library(bestNormalize)
library(sjPlot)
library(lmerTest)
library(ggplot2)
 
Sys.setlocale("LC_ALL", "Czech")
options(encoding = "UTF-8")
aqsync<-read.csv(file="aqsync.csv",fileEncoding = "UTF-16LE")

aqsync$cleanrt<-aqsync$rt
aqsync$cleanrt[aqsync$rt>4000|aqsync$rt<100]<-NA
aqsync$normclrt<-bestNormalize(aqsync$cleanrt,allow_orderNorm=F)$x.t
aqprec<-read.csv(file="aqprec.csv",fileEncoding = "UTF-16LE")

aqprec$cleanrt<-aqprec$rt
aqprec$cleanrt[aqprec$rt>4000|aqprec$rt<100]<-NA
aqprec$normclrt<-bestNormalize(aqprec$cleanrt,allow_orderNorm=F)$x.t

Auxiliary function

The following function calculates cell means and standard errors to be used in plots.

summarySE<- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    library(plyr)

    # New version of length which can handle NA's: if na.rm==T, don't count them
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }

    # This does the summary. For each group's data frame, return a vector with
    # N, mean, and sd
    datac <- ddply(data, groupvars, .drop=.drop,
      .fun = function(xx, col) {
        c(N    = length2(xx[[col]], na.rm=na.rm),
          mean = mean   (xx[[col]], na.rm=na.rm),
          sd   = sd     (xx[[col]], na.rm=na.rm)
        )
      },
      measurevar
    )

    # Rename the "mean" column    
    datac <- rename(datac, c("mean" = measurevar))

    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult

    return(datac)
}

pd <- position_dodge(0.1)

Synchrony plot

aqsyncsum<-summarySE(aqsync[aqsync$region1%in%c(6:9)&aqsync$subj%in%factor(c(1:30))&aqsync$is_correct==1&!is.na(aqsync$cleanrt),], measurevar="cleanrt", groupvars=c("condition","region1"))
outplot<-ggplot(aqsyncsum, aes(x=factor(region1), y=cleanrt, group=factor(condition),linetype=factor(condition))) + 
    geom_errorbar(aes(ymin=cleanrt-se, ymax=cleanrt+se), width=.1,position=pd) +
    geom_line(position=pd) +
    geom_point(position=pd) +
    scale_y_continuous("Reading time (ms)")+
    scale_x_discrete("Region",breaks=c("6", "7","8", "9"),
        labels=c("\nMezitím\nMeanwhile", "Z rozhlasu\nz rozhlasu\nfrom radio","vyhrávala\nvyhrávala\nplayed", "hudba\nhudba\nmusic"))+
    scale_linetype_manual(name="", 
                         labels = c("Explicit", 
                                   "Implicit"),
                                    values = c("0"="solid",
                                    "1"="dashed"))+
    theme_bw()

outplot

Synchrony modeling

aqsync7<-aqsync[aqsync$region1%in%c(7),]
aqsync8<-aqsync[aqsync$region1%in%c(8),]
aqsync9<-aqsync[aqsync$region1%in%c(9),]

x<-lmer(normclrt~condition+(1|subjv)+(1|item),data=aqsync7)
xa<-lmer(normclrt~condition+(1|subjv)+(1|item),data=aqsync8)
xb<-lmer(normclrt~condition+(1|subjv)+(1|item),data=aqsync9)
tab_model(x,xa,xb,show.std=T,show.est=F,show.ci=F)
  normclrt normclrt normclrt
Predictors std. Beta p std. Beta p std. Beta p
(Intercept) 0.01 <0.001 0.01 <0.001 -0.01 0.343
condition 0.13 <0.001 -0.06 <0.001 -0.02 0.235
Random Effects
σ2 0.31 0.25 0.35
τ00 0.41 subjv 0.39 subjv 0.62 subjv
0.02 item 0.01 item 0.04 item
ICC 0.58 0.61 0.65
N 109 subjv 109 subjv 109 subjv
20 item 20 item 20 item
Observations 1902 1901 1881
Marginal R2 / Conditional R2 0.017 / 0.584 0.004 / 0.616 0.000 / 0.650

Precedence plot

aqprecsum<-summarySE(aqprec[aqprec$region1%in%c(6:9)&aqprec$subj%in%factor(c(1:30))&aqprec$is_correct==1&!is.na(aqprec$cleanrt),], measurevar="cleanrt", groupvars=c("condition","region1"))
outplot<-ggplot(aqprecsum, aes(x=factor(region1), y=cleanrt, group=factor(condition),linetype=factor(condition))) + 
    geom_errorbar(aes(ymin=cleanrt-se, ymax=cleanrt+se), width=.1,position=pd) +
    geom_line(position=pd) +
    geom_point(position=pd) +
    scale_y_continuous("Reading time (ms)")+
    scale_x_discrete("Region",breaks=c("6", "7","8", "9"),
        labels=c("\nPotom\nThen", "Do třídy\ndo třídy\nin classroom","přišel\npřišel\ncame", "ředitel\nředitel\ndirector"))+
    scale_linetype_manual(name="", 
                         labels = c("Explicit", 
                                   "Implicit"),
                                    values = c("0"="solid",
                                    "1"="dashed"))+
    theme_bw()

outplot

# Precedence modeling

aqprec7<-aqprec[aqprec$region1%in%c(7),]
aqprec8<-aqprec[aqprec$region1%in%c(8),]
aqprec9<-aqprec[aqprec$region1%in%c(9),]

x<-lmer(normclrt~condition+(1|subjv)+(1|item),data=aqprec7)
xa<-lmer(normclrt~condition+(1|subjv)+(1|item),data=aqprec8)
xb<-lmer(normclrt~condition+(1|subjv)+(1|item),data=aqprec9)
tab_model(x,xa,xb,show.std=T,show.est=F,show.ci=F)
  normclrt normclrt normclrt
Predictors std. Beta p std. Beta p std. Beta p
(Intercept) 0.01 <0.001 0.01 <0.001 0.00 0.528
condition 0.14 <0.001 -0.07 <0.001 -0.00 0.912
Random Effects
σ2 0.28 0.24 0.23
τ00 0.60 subjv 0.52 subjv 0.67 subjv
0.01 item 0.00 item 0.01 item
ICC 0.68 0.69 0.75
N 109 subjv 109 subjv 108 subjv
20 item 20 item 20 item
Observations 1950 1954 1927
Marginal R2 / Conditional R2 0.021 / 0.691 0.005 / 0.690 0.000 / 0.747