# call software (data will be included)

library(pedigreemm)

# data pre-processing

milk <- within(milk, sdMilk <- milk / sd(milk))

# look at the data

str(milk)
head(milk)
tail(milk)

# run model lactation as covariate
system.time(
fm1 <- pedigreemm(sdMilk ~ lact + log(dim) + (1|id) + (1|herd), data = milk, pedigree = list(id = pedCowsR))
)

summary(fm1)

anova(fm1)

# compare with model excluding log(dim)
system.time(
fm2 <- pedigreemm(sdMilk ~ lact + (1|id) + (1|herd), data = milk, pedigree = list(id = pedCowsR))
)

summary(fm2)

anova(fm1, fm2)

# run model lactation as factor

fm1c <- pedigreemm(sdMilk ~ factor(lact) + log(dim) + (1|id) + (1|herd), data = milk, pedigree = list(id = pedCowsR))

summary(fm1c)


# including permament environmental effects
# potential confunding with genetic effects due to low information...

milk <- within(milk, idPE <- id)

system.time(
fm3 <- pedigreemm(sdMilk ~ lact + log(dim) + (1|id) + (1|idPE) + (1|herd), data = milk, pedigree = list(id = pedCowsR))
)

summary(fm3)

# run model lactation as factor

fm4 <- pedigreemm(sdMilk ~ factor(lact) + log(dim) + (1|id) + (1|herd), data = milk, pedigree = list(id = pedCowsR))

summary(fm4)