Combining 3 separate line plots into one line plot display - r

Question

We are looking at pattern recognitions and making different variables
unusualsubjects <- rtaverages$subject_id[rtaverages$count < 5] # make a list of subjects without enough data.
rtaverages <- filter(rtaverages,!(subject_id %in% unusualsubjects)) # only include data from good subjects. ! = not. Put data from acceptable subjects right back in the same data frame
# Another example of filtering subjects: let's say we only wanted to analyze subjects with accuracies over 95%
accurateSubjects <- averages$subject_id[averages$accuracy > .95] #returns all of the subject_ids for subjects meeting an accuracy criterion
length(accurateSubjects) # tells us how many accurate subjects there are
goodSubjectdata<-filter(data,subject_id %in% accurateSubjects) # make a new data frame that contains only the data from accurate subjects
Code to conduct actual ANOVA of the Respone Times results
model<ezANOVA(data=Data,dv=rt,within=c(set_size,target_presence,task),wid=subject_id) # You need to fill in the XXXs with the correct variable names within the variable containing all of the correct RTs. conduct a repeated measures ANOVA - dv = dependent variable. within = a list of all of the within subject variables. wid = variable that is used to group data by subject
model # show results of the ANOVA model
table1 <- tapply(X=Data$rt,INDEX=list(Data$task,Data$set_size),FUN=mean,trim=0.1)#find breakdown just of setsize and task - less broken down than the above tapply code, obtained just by deleting one item from the INDEX list "INDEX=list(rtaverages$target_presence,rtaverages$set_size,rtaverages$task)" above
table1 #show means so that one can begin to interpret the data. You'll break down rtaverages in different ways to get the different mean RTs that you need for your report
par(mar = c(4,4,4,0),mfrow=c(1, 2) ) # mfrow=c(1,2) creates two plots side by side
lineplot.CI(data=filter(rtaverages,task=="conjunctive"),x.factor=set_size,group=target_presence,x.cont=TRUE,response=rt,ylim=c(0,4000),x.leg=2,xlab="Conjunctive Set Size",ylab="RT") # produces a line graph with confidence intervals
lineplot.CI(data=filter(rtaverages,task=="disjunctive"),x.factor=set_size,group=target_presence,x.cont=TRUE,response=rt,ylim=c(0,4000),x.leg=2,xlab="Disjunctive Set Size",ylab="RT") # produces a line graph with confidence intervals
Currently attempting to put 3 lines onto one plot the following way:
# The next bit of code is to reproduce Treisman and Gelade's Figure 1, including best lines of fit
rtaverages$set_size_num<-sizes[rtaverages$set_size] # added a new column to rtaverage data frame which is the numeric/continuous version of the nominal/categorical set_size factor which will be useful for predicting RT from set_size
bySetSize<-group_by(rtaverages,set_size_num,task,target_presence) #collapse even more, so all subjects' data are combined together
collapsed<-summarize(bySetSize,rt=mean(rt,trim=0.1)) # make RT summary
collapsed # show what collapsed data look like. Note that there are now only 4 (set sizes) X 2 (tasks) X 2 (present/absent trials)=16 rows
cp<-filter(collapsed,task=="conjunctive" & target_presence=="present") # plot each of the four lines separated, filtering by the right type each time
cpf<-lm(data=cp,rt ~ set_size_num) # use a linear model to predict RT from set size. Use this to get out best fitting slope (estimate for set size) and intercept
summary(cpf) # make a summary of the linear regression model. cpf stands for: conjunctive, present fit
cp3<-filter(collapsed,task=="conjunctive" & target_presence=="absent")
caf<-lm(data=cp3,rt ~ set_size_num)
summary(caf)
cp1<-filter(collapsed,task=="disjunctive" & target_presence=="present")
dpf<-lm(data=cp1,rt ~ set_size_num)
summary(dpf)
cp2<-filter(collapsed,task=="disjunctive" & target_presence=="absent")
daf<-lm(data=cp2,rt ~ set_size_num)
summary(daf)
plot(cp$set_size_num,cp$rt,ylim=c(0,4000),xlim=c(0,30),pch=19,col="green",xlab="Set Size",ylab="Response Time (msec.)") # use a big enough range to capture all of the data
abline(cpf, col="green") # add the line with the slope and intercept derived from linear model
lines(cp$set_size_num,cp3$rt,col="green")
abline(caf, col="green")
lines(cp1$set_size_num,cp1$rt,col="red")
abline(dpf, col="red")
lines(cp2$set_size_num,cp2$rt,col="red")
abline(daf, col="red")
legend(x=0,y=4000,pch=c(19,1,19,1),col=c("green","green","red","red"),cex=0.7,legend=c("Conjunctive present","Conjunctive absent","Disjunctive present","Disjunctive absent")) #Legend only should be plotted once, pch sets 4 symbols, and col sets 4 colors. cex < 1 so that legend box isn't too big
I got them to combine, but now the lines lost their format: