# Kontinua trennen
temp = with(lax, C =="sVt")
a.df = lax[temp,]; b.df = lax[!temp,]

# GLMMs
a.lmer = lmer(cbind(P, Q) ~ Stim + (1+Stim|S), family=binomial, data=a.df)
b.lmer = lmer(cbind(P, Q) ~ Stim + (1+Stim|S), family=binomial, data=b.df)

# Koeffiziente
a.coef = as.matrix(coef(a.lmer)[[1]])
b.coef = as.matrix(coef(b.lmer)[[1]]) 
beide.coef = rbind(a.coef, b.coef) 
colnames(beide.coef) = c("k", "m")

# Vpn Namen
a.namen = rownames(a.coef)
b.namen = rownames(b.coef) 
V = factor(c(a.namen, b.namen))

# Umkipppunkte
um = -beide.coef[,1]/beide.coef[,2]

# Between factor
a.bet = between(a.namen, a.df, "S", "A")
b.bet = between(b.namen, b.df, "S", "A") 
B = factor(c(as.character(a.bet), as.character(b.bet)))

# Within factor
Kon = factor(c(rep("a", nrow(a.coef)), rep("b", nrow(b.coef))))

# Data frame der Koeffiziente
coef.df = data.frame(um, beide.coef, Vpn=V, Bet=B, Kontext=Kon, row.names=NULL)

# Mixed model
mm = lmer(um ~ Bet * Kontext + (1|Vpn), data=coef.df)
anova(mm)

# RM-Anova
um.t = Anova.prepare(coef.df[,-c(2,3)], c("d", "s", "b", "w"))