#  MTC-Stuff
rm(list=ls())

# LME
library(lme4)
library(ggplot2)
source("nested_lmer.R")

load("MM.rda")

# Select data: not Exp 5; not zero and single MS
ix <- which(d$EXP == "VVb" | d$TYP == "no MS" | d$TYP == "single MS")
d <- d[-ix, ]
d[] <- lapply(d,function(x) x[drop=TRUE])
d$TYP <- relevel(d$TYP, ref="last MS")

# Add new variables
# ...  convert condition labels also to factors
d <- cbind(d, colsplit(d$EXP, names=c("Cue", "Target")))
d$Cue <- relevel(d$Cue, 2)
d$Target <- relevel(d$Target, 2)

# Generate means and figure based on all trials, allow for missing design cells
# Table 2 (used in manuscript)
d.rt <- melt(d, id=c("ID", "EXP", "CV", "MTC", "TYP"), measure=c("rt"))

tab0 <- cast(d.rt, EXP + CV + MTC ~ TYP, function(x) c(M=round(mean(x)), SE= round(sd(x)/sqrt(length(x))), N=length(x)))

# Figure 2 
# ... set up data
tab <- cast(d.rt, EXP +  CV + MTC + TYP ~ ., function(x) c(M=round(mean(x)), SE=round(sd(x)/sqrt(length(x))), N=length(x),
                                                   lower=round(mean(x)-(sd(x)/sqrt(length(x)))),
                                                   upper=round(mean(x)+(sd(x)/sqrt(length(x)))) ))   #
names(tab)[[5]] <- "MeanRT"
tab

# ... plot
p <- qplot(MTC, MeanRT, data=tab, group=CV, shape=CV, facets = TYP ~ EXP, 
                       xlab="MS Target Congruency",  
                       ylab = "Reaction Time [ms]")
p + geom_point(size=3) + geom_line(size=1) + geom_errorbar(aes(max=upper, min=lower, width=0.1), size=1)


# Overall LME
source("contrasts.R")
print(m <- lmer(rt ~ EXP2*cv.e*mtc.e + (1|ID), d, subset= TYP=="last MS"), cor=FALSE)   # Use the effect coding for manuscript

d$design <- factor(paste(d$CV, d$MTC, sep=""))
d$design <- C(d$design, matrix(c(-.5, +.5,   0,   0,  # (1)  mtc | cv=1 - invalid
	                               0,   0, -.5, +.5,  # (2)  mtc | cv=2  - valid
	                             -.5, -.5, +.5, +.5   # (3)  cv  
	                                  ), 4, 3), 3)
contrasts(d$design)

d$design2 <- factor(paste(d$MTC, d$CV, sep=""))
d$design2 <- C(d$design2, matrix(c(-.5, +.5,   0,   0,  # (1)  cv | mtc=1 - inc
	                                 0,   0, -.5, +.5,  # (2)  cv | mtc=2  - con
	                               -.5, -.5, +.5, +.5   # (3)  mtc 
	                                  ), 4, 3), 3)
contrasts(d$design2)


# LAST MS
print(m.VV <- lmer(rt ~ design + (1|ID), d, subset= EXP=="VV" & TYP=="last MS"), cor=FALSE)  
print(m.VA <- lmer(rt ~ design + (1|ID), d, subset= EXP=="VA" & TYP=="last MS"), cor=FALSE) 
print(m.AV <- lmer(rt ~ design + (1|ID), d, subset= EXP=="AV" & TYP=="last MS"), cor=FALSE)  
print(m.AA <- lmer(rt ~ design + (1|ID), d, subset= EXP=="AA" & TYP=="last MS"), cor=FALSE)  
                                                                                             
# FIRST MS                                                                                   
print(m.VV <- lmer(rt ~ design + (1|ID), d, subset= EXP=="VV" & TYP=="first MS"), cor=FALSE) 
print(m.VA <- lmer(rt ~ design + (1|ID), d, subset= EXP=="VA" & TYP=="first MS"), cor=FALSE) 
print(m.AV <- lmer(rt ~ design + (1|ID), d, subset= EXP=="AV" & TYP=="first MS"), cor=FALSE) 
print(m.AA <- lmer(rt ~ design + (1|ID), d, subset= EXP=="AA" & TYP=="first MS"), cor=FALSE) 


# Experiment 1+2
d$exp.e <- as.numeric(d$EXP)-1.5
print(m.VV <- lmer(rt ~ design*exp.e + (1|ID), d, subset= (EXP=="VV" | EXP=="VA") & TYP=="last MS"), cor=FALSE)