Sys.setlocale("LC_ALL", "Czech")
options(encoding = "UTF-8")

library(bestNormalize)
library(sjPlot)
library(lmerTest)
library(ggplot2)

Load concession data, trim the response times 200 ms and below and 4000 and above, normalize the response times.

aqconc<-read.csv(file="aqconc.csv",fileEncoding = "UTF-16LE")

aqconc$cleanrt<-aqconc$rt
aqconc$cleanrt[aqconc$rt>4000|aqconc$rt<200]<-NA
aqconc$normclrt<-bestNormalize(aqconc$cleanrt,allow_orderNorm=F)$x.t

Load confrontation data, trim the response times 200 ms and below and 4000 and above, normalize the response times.

aqconf<-read.csv(file="aqconf.csv",fileEncoding = "UTF-16LE")

aqconf$cleanrt<-aqconf$rt
aqconf$cleanrt[aqconf$rt>4000|aqconf$rt<200]<-NA
aqconf$normclrt<-bestNormalize(aqconf$cleanrt,allow_orderNorm=F)$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)

Concession plot

aqconcsum<-summarySE(aqconc[aqconc$region1%in%c(6:10)&!is.na(aqconc$cleanrt),], measurevar="cleanrt", groupvars=c("condition","region1"))
outplot<-ggplot(aqconcsum, 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_x_discrete("Region",breaks=c("6", "7","8", "9", "10"),
        labels=c("\nPřesto\nYet", "Dál\ndál\nfurther","kupoval\nkupoval\nhe bought", "drahé\ndrahé\nexpensive","zbytečnosti\nzbytečnosti\nfutilities"))+
    theme_bw()+
    scale_linetype_manual(name="", 
                         labels = c("Explicit", 
                                   "Implicit"),
                                    values = c("0"="solid",
                                    "1"="dashed"))+
    labs(y="Reading time (ms)",x="")

outplot

Concession modeling

The following code shows the results of mixed models:

aqconc7<-subset(aqconc, region1=="7")
aqconc8<-subset(aqconc, region1=="8")
aqconc9<-subset(aqconc, region1=="9")
aqconc10<-subset(aqconc, region1=="10")

 xa<-lmer(normclrt~condition+(condition|subjv)+(1|item),data=aqconc7)
 xb<-lmer(normclrt~condition+(condition|subjv)+(1|item),data=aqconc8)
 xc<-lmer(normclrt~condition+(condition|subjv)+(1|item),data=aqconc9)
 xd<-lmer(normclrt~condition+(condition|subjv)+(1|item),data=aqconc10)
tab_model(xa,xb,xc,xd,show.std=T,show.ci=F,show.est=F)
  normclrt normclrt normclrt normclrt
Predictors std. Beta p std. Beta p std. Beta p std. Beta p
(Intercept) -0.02 <0.001 -0.02 <0.001 -0.04 <0.001 -0.02 0.430
condition 0.21 <0.001 -0.01 0.624 0.02 0.225 0.04 0.008
Random Effects
σ2 0.33 0.32 0.28 0.34
τ00 0.38 subjv 0.35 subjv 0.36 subjv 0.48 subjv
0.02 item 0.02 item 0.06 item 0.03 item
τ11 0.04 subjv.condition 0.02 subjv.condition 0.02 subjv.condition 0.01 subjv.condition
ρ01 -0.36 subjv 0.09 subjv -0.37 subjv -0.26 subjv
ICC 0.54 0.55 0.59 0.59
N 115 subjv 115 subjv 115 subjv 115 subjv
20 item 20 item 20 item 20 item
Observations 2024 2028 2026 2010
Marginal R2 / Conditional R2 0.043 / 0.556 0.000 / 0.547 0.000 / 0.589 0.002 / 0.591

Confrontation plot

aqconfsum<-summarySE(aqconf[aqconf$region1%in%c(6:10)&!is.na(aqconf$cleanrt),], measurevar="cleanrt", groupvars=c("condition","region1"))

outplot<-ggplot(aqconfsum, 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_x_discrete("Region",breaks=c("6", "7","8", "9", "10"),
      labels=c("\nZato\nBut", "V Praze\nv Praze\nin Prague","trvale\ntrvale\nsteadily", "mírně\nmírně\nslightly","stoupá\nstoupá\ngrows"))+
    theme_bw()+
    scale_linetype_manual(name="", 
                         labels = c("Explicit", 
                                   "Implicit"),
                                    values = c("0"="solid",
                                    "1"="dashed"))+
    labs(y="Reading time (ms)", x="")
    
outplot

Confrontation modeling

aqconf7<-subset(aqconf, region1=="7")
aqconf8<-subset(aqconf, region1=="8") 
aqconf9<-subset(aqconf, region1=="9") 
aqconf10<-subset(aqconf, region1=="10")
 
 xa<-lmer(normclrt~factor(condition)+(condition|subjv)+(condition|item),data=aqconf7)
 xb<-lmer(normclrt~factor(condition)+(condition|subjv)+(condition|item),data=aqconf8)
 xc<-lmer(normclrt~factor(condition)+(condition|subjv)+(condition|item),data=aqconf9)
 xd<-lmer(normclrt~factor(condition)+(1|subjv)+(condition|item),data=aqconf10)
tab_model(xa,xb,xc,xd,show.std=T,show.est=F,show.ci=F)
  normclrt normclrt normclrt normclrt
Predictors std. Beta p std. Beta p std. p std. Beta p std. Beta p
(Intercept) -0.23 <0.001 0.04 <0.001 0.680 -0.01 <0.001 -0.07 0.015
condition [1] 0.43 <0.001 -0.11 0.006 0.006 -0.02 0.630 0.10 0.003
Random Effects
σ2 0.33 0.33 0.30 0.41
τ00 0.40 subjv 0.32 subjv 0.28 subjv 0.47 subjv
0.04 item 0.05 item 0.03 item 0.03 item
τ11 0.05 subjv.condition 0.01 subjv.condition 0.02 subjv.condition 0.00 item.condition
0.01 item.condition 0.01 item.condition 0.01 item.condition  
ρ01 -0.48 subjv 0.09 subjv -0.05 subjv 0.03 item
-0.51 item -0.52 item -0.26 item  
ICC 0.57 0.53 0.51 0.55
N 115 subjv 115 subjv 115 subjv 115 subjv
20 item 20 item 20 item 20 item
Observations 2122 2118 2120 2104
Marginal R2 / Conditional R2 0.043 / 0.587 0.003 / 0.532 0.000 / 0.513 0.002 / 0.554