## check if the lmerTest package is already installed/attached
sessionInfo()

#if not yet installed then install it
install.packages("lmerTest")

# if not ye
library("lmerTest")

################################################################################
###### Example 1. TVbo data
################################################################################


#summarize the TVbo data
str(TVbo)

#specification of the "full" model
M_TV <- lmer(Sharpnessofmovement ~ TVset*Picture +
            (1|Assessor:TVset) + (1|Assessor:Picture) +
            (1|Assessor:Picture:TVset) + (1|Repeat) + 
            (1|Repeat:Picture) +
            (1|Repeat:TVset) + (1|Repeat:TVset:Picture) + 
            (1|Assessor) , data=TVbo)

# automated elimination process
s_TV <- step(M_TV)

#print the elimination process: analysis of random part and analysis of the fixed part
print(s_TV)

#print analysis of the random part
print(s_TV$rand.table)

#print analysis of the fixed part
print(s_TV$anova.table)

#plot the post-hoc analysis
plot(s_TV)

#plot the post-hoc analysis of only main effects
plot(difflsmeans(s_TV$model, test.effs = c("TVset", "Picture")))

################################################################################
###### Example 2. carrots data
################################################################################



#summarize the carrots data
str(carrots)

#specification of the "full" model
M_carrots <- lmer(Preference ~ sens2*sens1*Homesize+
            (1|product)+(1 + sens1 + sens2|Consumer), data=carrots)

## look at the summary of the model
summary(M_carrots)


# test random effects
rand(M_carrots)


# automated elimination process
s_carrots <- step(M_carrots)

#print the elimination process: analysis of random part and analysis of the fixed part
print(s_carrots)

#print analysis of the random part
print(s_carrots$rand.table)

#print analysis of the fixed part
print(s_carrots$anova.table)

#plot the post-hoc analysis
plot(s_carrots)

## reduced model
M_red <- lmer(Preference ~ sens2 + Homesize +
                (1 | product) + (1 + sens2  |Consumer), 
              data = carrots)

summary(M_red)