P = c(30, 18, 15, 13, 4, 2)
Q = c(5, 21, 26, 20, 32, 34)
Jahr = c(1950, 1960, 1971, 1980, 1993, 2005)
prop = P/(P+Q)
lost = data.frame(P, Q, prop, Jahr)
ylim = c(0,1)
plot(prop ~ Jahr, data = lost, ylim=ylim)

plot(log(P/Q) ~ Jahr, data = lost)

##########
g = glm(cbind(P, Q) ~ Jahr, binomial, data = lost)
abline(g)

anova(g, test="Chisq")
# ============================================
par(mfrow=c(1,2))
plot(log(P/Q) ~ Jahr, data = lost, ylab="Log-odds (Hoch)")
abline(g)
plot(prop ~ Jahr, data = lost, ylab="Proportion (Hoch)")
m = coef(g)[2]
k = coef(g)[1]
curve(exp(m*x + k)/(1+ exp(m*x+k)), add=T, col=2)


# ============================================
P = c(110, 82)
Q = c(90, 108)
prop = P/(P+Q)
Gender = factor(c("m", "w"))
glot = data.frame(P, Q, prop, Gender)
ylim = c(0, 1)
plot(prop ~ Gender, data = glot, ylim=ylim)

library(lattice)
barchart(prop ~ Gender, data = glot, ylim=ylim)

g = glm(cbind(P, Q) ~ Gender, binomial, data=glot)
anova(g, test="Chisq")

# ============================================
# Zur Info
logodds = with(glot, log(P/Q))
x = with(glot, contrasts(Gender))
plot(x, logodds)
abline(g)
# Berechnung ohne Faktor (nur mit Intercept)
g2 = glm(cbind(P, Q) ~ 1, binomial, data = glot)
abline(g2, col="red", lty=2)

plot(x, glot$prop,  ylim=c(0, 1))
# plot(x, glot$prop, xlim = c(-10, 10), ylim=c(0, 1))
m = coef(g)[2]
k = coef(g)[1]
curve(exp(m*x + k)/(1+ exp(m*x+k)), add=T, col=2)
# ============================================
antwort = c("z", "z", "z", "z", "s", "s", "z", "s", "z", "z", "z", "s", "z", "s", "s", "s", "z", "s", "s", "s")
dialekt = c(rep("SH", 11), rep("BY", 9))
lang.df = data.frame(Urteil = factor(antwort), D = factor(dialekt))

# P, Q, prop berechnen für Bayern und SH
tab = table(dialekt, antwort)
temp = antwort=="z"
a = aggregate(temp, list(dialekt), sum)
b = aggregate(!temp, list(dialekt), sum)
prop = a$x/(a$x + b$x)
kurz.df = data.frame(P=a$x, Q=b$x,  D = a$Group.1)
# ============================================
g1 = glm(Urteil ~ D, binomial, data=lang.df)
g2 = glm(cbind(P, Q) ~ D, binomial, data=kurz.df)

# Abbildungen
tab = with(lang.df, table(D, Urteil))
barchart(tab, auto.key=T, horizontal=F)

prop = with(kurz.df, P/(P+Q))
barchart(prop ~ D, data = kurz.df, ylab="Proportion /z/")


# ============================================

kj = read.delim(file.path(pfad, "kj.txt"))

# ============================================

F2 = c(2311, 2176, 2023, 1885, 1770, 1667, 1548, 1437, 1351, 1269)
P = c(rep(0, 6), 2, 4, 5, 5)
Q = c(rep(5, 6), 3, 1, 0, 0)
prop = P/(P+Q)
ui = data.frame(P, Q, prop, F2)

g = glm(cbind(P, Q) ~ F2, binomial, ui)
k = coef(g)[1]
m = coef(g)[2]
U = -k/m
plot(prop ~ F2, data = ui, ylab = "Proportion /u/-Urteile")
curve(exp(m*x + k)/(1+ exp(m*x+k)), add=T, col=2)
abline(v = U)
abline(h = .5, lty=2)


# ============================================
gq = glm(cbind(P, Q) ~ Jahr, quasibinomial, data = lost)
anova(gq, test="F")