Chapter 12 - Monsters and Mixtures

This chapter introduced several new types of regression, all of which are generalizations of generalized linear models (GLMs). Ordered logistic models are useful for categorical outcomes with a strict ordering. They are built by attaching a cumulative link function to a categorical outcome distribution. Zero-inflated models mix together two different outcome distributions, allowing us to model outcomes with an excess of zeros. Models for overdispersion, such as beta-binomial and gamma-Poisson, draw the expected value of each observation from a distribution that changes shape as a function of a linear model.

Place each answer inside the code chunk (grey box). The code chunks should contain a text response or a code that completes/answers the question or activity requested. Problems are labeled Easy (E), Medium (M), and Hard(H).

Finally, upon completion, name your final output .html file as: YourName_ANLY505-Year-Semester.html and publish the assignment to your R Pubs account and submit the link to Canvas. Each question is worth 5 points.

Questions

12E1. What is the difference between an ordered categorical variable and an unordered one? Define and then give an example of each.

#Ordered Categorical variable can be defined as a variable that is part of series of a set that defines the level of the variable. For example, ratings of restaurant from 1 to 5. 
# Unordered categorical variable can be defined as a variable that has a type or part of label category. For example either male or female.

12E2. What kind of link function does an ordered logistic regression employ? How does it differ from an ordinary logit link?

#Ordered logistic regression employs a cumulative log-odds.
#The ordered logistic regression is based on the cumulative probabilities of the response variable instead of a discrete probability of a single event.

12E3. When count data are zero-inflated, using a model that ignores zero-inflation will tend to induce which kind of inferential error?

#When count data are zero-inflated, using a model that ignores zero-inflation will tend to induce multuple counts of zero then other results as 0 would be from another process.

12E4. Over-dispersion is common in count data. Give an example of a natural process that might produce over-dispersed counts. Can you also give an example of a process that might produce underdispersed counts?

#Over dispersion in a natural process can be witnessed in an example like the produce or fruits generated in farm.

#Underdispersed count example could be the increase in purchase of electronics during black friday event.

12M1. At a certain university, employees are annually rated from 1 to 4 on their productivity, with 1 being least productive and 4 most productive. In a certain department at this certain university in a certain year, the numbers of employees receiving each rating were (from 1 to 4): 12, 36, 7, 41. Compute the log cumulative odds of each rating.

employees <- c(12,36,7,41,0)

for(i in 1:4){
  print(log(sum(employees[1:i])/sum(employees[(i+1):5])))
}
## [1] -1.94591
## [1] 0
## [1] 0.2937611
## [1] Inf

12M2. Make a version of Figure 12.5 for the employee ratings data given just above.

prop= employees/sum(employees)

prop=sapply(1:4,function(i){sum(prop[1:i])})

plot(y=prop,x=1:4,type="b", ylim=c(0,1))

segments(1:4,0,1:4,prop)

for(i in 1:4){segments(i+0.05,c(0,prop)[i],i+0.05,prop[i], col = "blue")}

12M3. Can you modify the derivation of the zero-inflated Poisson distribution (ZIPoisson) from the chapter to construct a zero-inflated binomial distribution?

# Binomial probability :Pr(y | N, p) = px(1-p) n-x

# X is the number of successes.

# (1- p_not_work) * p_success^n_success (1-p_success)^n_trials-n_success

12H1. In 2014, a paper was published that was entitled “Female hurricanes are deadlier than male hurricanes.”191 As the title suggests, the paper claimed that hurricanes with female names have caused greater loss of life, and the explanation given is that people unconsciously rate female hurricanes as less dangerous and so are less likely to evacuate. Statisticians severely criticized the paper after publication. Here, you’ll explore the complete data used in the paper and consider the hypothesis that hurricanes with female names are deadlier. Load the data with:

data(Hurricanes)

m11H1.map <- map(
  alist(
    deaths ~ dpois( lambda ),
    log(lambda) <- a + bF*femininity,
    a ~ dnorm(0,10),
    bF ~ dnorm(0,5)
  ) ,
  data=Hurricanes)

m11H1.map2 <- map(
  alist(
    deaths ~ dpois( lambda ),
    log(lambda) <- a ,
    a ~ dnorm(0,10)
  ) ,
  data=Hurricanes)

compare(m11H1.map,m11H1.map2)
##                WAIC       SE    dWAIC     dSE     pWAIC       weight
## m11H1.map  4417.355 1001.802  0.00000      NA 128.26627 1.000000e+00
## m11H1.map2 4457.871 1079.229 40.51633 143.053  84.80982 1.592178e-09
y <- sim(m11H1.map)

y.mean <- colMeans(y)
y.PI <- apply(y, 2, PI)

plot(y=Hurricanes$deaths, x=Hurricanes$femininity, col=rangi2, ylab="deaths", xlab="femininity", pch=16)
points(y=y.mean, x=Hurricanes$femininity, pch=1)
segments(x0=Hurricanes$femininity, x1= Hurricanes$femininity, y0=y.PI[1,], y1=y.PI[2,])

lines(y= y.mean[order(Hurricanes$femininity)],  x=sort(Hurricanes$femininity))
lines( y.PI[1,order(Hurricanes$femininity)],  x=sort(Hurricanes$femininity), lty=2 )
lines( y.PI[2,order(Hurricanes$femininity)],  x=sort(Hurricanes$femininity), lty=2 )

#This above results show deaths and feminity. Feminity of names much higher

Acquaint yourself with the columns by inspecting the help ?Hurricanes. In this problem, you’ll focus on predicting deaths using femininity of each hurricane’s name. Fit and interpret the simplest possible model, a Poisson model of deaths using femininity as a predictor. You can use quap or ulam. Compare the model to an intercept-only Poisson model of deaths. How strong is the association between femininity of name and deaths? Which storms does the model fit (retrodict) well? Which storms does it fit poorly?