#### Homework 12 Key ####

#1. 
d <- dfReadDat("HW12_Data.dat")
head(d)
varPlot(d$Baseline)
varPlot(d$Anticipation)
table(d$Group) 

varDescribeBy(d$Baseline, d$Group)
varDescribeBy(d$Anticipation, d$Group)

#2. 

d$Group = factor(d$Group, levels = c('Deprived','Satiated','Hater'))

#3. 

# Mello yello drinkers were randomly assigned into Deprived and Satiated groups
# BUT mello yello drinkers versus mello yellow haters were not randomly assigned (they came into 
# as preexisting mello yello groups) so the difference score ANOVA approach would be less biased
# than the ANCOVA approach 

#4. 

d$IrritationDiff = d$Anticipation - d$Baseline 
# increased from baseline

contrasts(d$Group) = varContrasts(d$Group, Type = 'DUMMY',
                                        RefLevel = 1) 
# Probably better to set up the other contrasts as we need them  Alternatively, 
# you could create a new variable for each dummy code 

#5. 
mOverall = lm(IrritationDiff ~ Group, data = d)
Anova(mOverall, type = 3)
# While this would be an appropriate check to see
# if we are justified in interpreting the simple effects,
# main effect of group is not significant (p = .521) so we cannot move forward
# at least without further correction for multiple comparisons

#6. 
# a) 


mDep = lm(IrritationDiff ~ Group, data = d)
modelSummary(mDep)
modelEffectSizes(mDep)
confint(mDep)

# Participants in the deprived Mello Yello group felt .42 units less irritated after anticipating
# drinking an ice cold Mello Yello, b = -0.422,[-0.839, -0.005]. t(129) = -2.004, pEta^2 = .030, p = .047.


# b) 
contrasts(d$Group) = varContrasts(d$Group, Type = 'DUMMY',
                                  RefLevel = 2)
mSat = lm(IrritationDiff ~ Group, data = d)
modelSummary(mSat)
modelEffectSizes(mSat)
confint(mSat)

# Participants in the satiated Mello Yello group only felt .07 units less irritated after anticipating
# drinking an ice cold Mello Yello and this change was not significant, t(129) = -0.332,
# p = .741.


# c) 

contrasts(d$Group) = varContrasts(d$Group, Type = 'DUMMY',
                                  RefLevel = 3)
mHat = lm(IrritationDiff ~ Group, data = d)
modelSummary(mHat)
modelEffectSizes(mHat)
confint(mHat)

# Participants in the  Mello Yello hater group only felt .07 units less irritated after anticipating
# drinking an ice cold Mello Yello and this change was not significant, t(129) = -1.252,
# p = .213.

#7. 

ps = numeric(length = 3)

ps[1] = modelSummary(mDep)$coefficients[1,4]
ps[2] = modelSummary(mSat)$coefficients[1,4]
ps[3] = modelSummary(mHat)$coefficients[1,4]

p.adjust(ps,'holm') # 0.141 0.741 0.426

#8. '10' is the only correct answer 

#9. See word key