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STA442 Homework 1
Question 1: Flies
In this experiment, we want to investigate the effect of thorax length and mating activity has
on the expected lifespan of fruitflies. Fruitflies are placed in solitary, with one virgin female
and eight virgin females. The experiment also contains two control groups, which are kept
with either one pregnant female or eight pregnant females. The thorax lengths of fruitflies are
also measured since it is known to affect the lifespan of fruitflies.
We use Gamma GLM to model the lifetimes as a function of the thorax length and mating
activity. In the model, we have centred the predictor variable of thorax length by subtraction
of 0.84 from every value in the variable. We have also scaled the variables using exp().
We get the estimated parameters below:
Fig 1. Estimated Parameters of the Gamma GLM
From Figure 1, we can see that:
● For the increase of every one unit length of thorax length, there will be an increase of
approximately 14.70 days.
● Comparing flies kept in isolation, the flies kept with one virgin female have their
lifespan reduced by 11%, whereas the flies kept with eight virgin females have their
lifespan reduced by 34%.
I attach the modelled and empirical distribution below, to indicate the Gamma GLM is a good
fit for the data:
Fig 2. Empirical Distribution of the Data and the Model Fit
In conclusion, the lifespan of fruitflies is indeed affected by the presence and frequency of
their mating activities. Fruitflies with no mating activities have a higher lifespan comparing to
fruitflies with mating activities. Fruitflies with a lower frequency of mating activities also have
a higher lifespan comparing wit fruitflies with a higher frequency of mating activities.
Question 2: Smoking
Summary
We have analyzed the 2014 American National Youth Tobacco survey, to see if race and
gender are factors contributing to the increase in the use of substances such as chewing
tobacco and hookah in the American youth population. From the analysis, we have found
that race is a significant contributing factor in the use of chewing tobacco. The regular use of
chewing tobacco, snuff or dip is around 2 times and 5 times more common amongst
Americans of European ancestry than Hispanic Americans and African Americans
respectively. On the other hand, gender is not a significant contributing factor in the use of
hookah. The use of Hookah among women is only 4% more than men.
Introduction
In this report, we will be analyzing the 2014 American National Youth Tobacco survey, to
investigate if demographics have effects on the smoking habits of the American population.
The first topic we want to investigate is whether race is a factor contributing to the regular
use of chewing tobacco, snuff or dip, regular use meaning the youths have used these
substances on 1 or more days in the past 30 days. We are especially interested in seeing
the comparison between Americans or European ancestry, Hispanic-Americans and
African-Americans. The second topic we want to investigate is whether gender is a factor
affecting the likelihood of having used a hookah or waterpipe on at least one occasion, given
the other demographic characteristics are similar.
Methods
We will be using the logistic regression in this research, as in both topics, we want to model
probabilities. For the variables in our model, we will be including age, gender, race and living
area (urban or rural) of the youth.
In the first topic, the “Odds” refers to the odds of regular use of chewing tobacco, snuff or
dip. Since we want to see if the race is a significant factor contributing to that odds, the null
hypothesis of this topic is:
In the second topic, the “Odds” refers to the odds of having used a hookah or waterpipe on
at least one occasion. Given that all demographic characteristics except gender are similar,
and we want to see if gender is a significant factor contributing to that odds, the null
hypothesis of this topic is:
Then, using the likelihood ratio test, we compare the likelihood of the data under the full
model against the likelihood of the data under a model without race or gender respectively.
From that, we can test whether the observed difference in model fit is statistically significant.
Results
Fig 3. Likelihood Ratio Test for Regular Use of Chewing Tobacco, Snuff or Dip
From the LRT in Figure 3, given the p-value <0.05, we can reject the null hypothesis, and
conclude that race is a significant factor contributing to the odd of regular use of chewing
tobacco.
Fig 4. Odds of Regular Use of Chewing Tobacco, Snuff or Dip
To compare the regular use of these substances amongst Americans of European ancestry,
Hispanic-Americans and African-Americans, we can look at the coefficients of our model in
Figure 4. The odds of regular use of these substances among Hispanic-Americans and
African-Americans are 0.22 and 0.48 times, the odds of that among Americans of European
ancestry respectively. It also seems that youth living in rural areas tends to use these
substances more regularly than youth living in the urban area.
Fig 5. Likelihood Ratio Test for Having Used Hookah or Waterpipe
From the LRT in Figure 4, given the p-value 0.05, which is statistically not significant, and
hence we cannot reject the null hypothesis. Hence it seems that the reduced model without
the gender variable is adequate in fitting the data.
Fig 6. Odds of Having Used Hookah or Waterpipe
From Figure 6, we can see that the odds of having used Hookah or Waterpipe for a female is
1.04 times than that of a male, given all other demographic characteristics stay the same.
But again given p-value 0.05, we cannot determine whether or not gender is a significant
factor contributing to the odds of having used hookah or water pipes.
STA442 Homework 1 Appendix
Question 1
data(fruitfly, package=faraway)
throaxC = fruitfly$thorax – 0.84 # Use quantile(fruitfly$thorax) to center variable
mod1 = glm(longevity ~ throaxC + activity, family=Gamma(link = log), data=fruitfly)
mod1coeff = summary(mod1)$coef
mod1coeff[,1] = round(exp(mod1coeff[,1]),2) # Use to rescale variables
knitr::kable(rbind(mod1coeff,shape=c(1/summary(mod1)$dispersion, NA, NA, NA)), digits=4,
caption = “Estimated Parameters of the Gamma GLM”)
Table 1: Estimated Parameters of the Gamma GLM
Estimate Std. Error t value Pr(|t|)
(Intercept) 63.1200 0.0377 109.9177 0.0000
throaxC 14.7000 0.2277 11.8044 0.0000
activityone 1.0600 0.0534 1.0357 0.3024
activitylow 0.8900 0.0533 -2.1844 0.0309
activitymany 1.0900 0.0541 1.5240 0.1302
activityhigh 0.6600 0.0539 -7.6874 0.0000
shape 28.1455 NA NA NA
shape = 1/summary(mod1)$dispersion
scale = exp(mod1$coef[“(Intercept)”])/shape
xSeq = seq(0,100,len=100)
hist(fruitfly$longevity, prob = TRUE, breaks = 20, xlab = “longevity”,ylim = c(0,0.040),
main=”Empirical Distribution of the Data and the Model Fit”)
lines(xSeq, dgamma(xSeq, shape = shape,scale = scale), col = “blue”)
1
Empirical Distribution of the Data and the Model Fit
longevity
Density
20 40 60 80 100
0.00 0.01 0.02 0.03 0.04
Question 2
smokeUrl = http://pbrown.ca/teaching/appliedstats/data/smoke.RData
(smokeFile = tempfile(fileext=.RData))
## [1] “/var/folders/d6/69h871p92l59n9sx36t_bz000000gn/T//RtmpDV0x4b/file33cb37bbaf98.RData”
download.file(smokeUrl, smokeFile, mode=wb)
(load(smokeFile))
## [1] “smoke” “smokeFormats”
smokeFormats[smokeFormats$colName == Tried_cigarette_smkg_even, ]
## ID label
## 23 qn7 Tried cigarette smkg, even 1 or 2 puffs
## shortLabel colName
## 23 Tried cigarette smkg even 1 or 2 puffs Tried_cigarette_smkg_even
smoke$everSmoke = factor(smoke$Tried_cigarette_smkg_eve, levels=1:2, labels=c(yes,no))
smokeSub = smoke[smoke$Age != 9 & !is.na(smoke$Race)
& !is.na(smoke$ever_tobacco_hookah_or_wa)
& !is.na(smoke$chewing_tobacco_snuff_or), ]
smokeAgg = reshape2::dcast(smokeSub,
Age + Sex + Race + RuralUrban ~ chewing_tobacco_snuff_or,
length)
## Using everSmoke as value column: use value.var to override.
2
smokeAgg = na.omit(smokeAgg)
smokeAgg = smokeAgg[-7]
dim(smokeAgg)
## [1] 207 6
# smokeModel
smokeAgg$y = cbind(smokeAgg$TRUE, smokeAgg$FALSE)
smokeFit = glm(y ~ Age + Sex + Race + RuralUrban,
family=binomial(link=logit), data=smokeAgg)
# We want to scale the variable Age,
# since the center age of intercept is 15, we substract 15 from values of variable
smokeAgg$ageC = smokeAgg$Age – 15
smokeFit = glm(y ~ ageC + Sex + Race + RuralUrban,
family=binomial(link=logit), data=smokeAgg)
smokeTable = as.data.frame(summary(smokeFit)$coef)
# LRT
smokeFitReduced = glm(y ~ ageC + Sex + RuralUrban,
family=binomial(link=logit), data=smokeAgg)
knitr::kable(anova(smokeFit,smokeFitReduced,test = “Chisq”),
digits = 2,caption = “Likelihood Ratio Test of
Regular Use of Chewing Tobacco, Snuff or Dip”)
Table 2: Likelihood Ratio Test of Regular Use of Chewing Tobacco,
Snu or Dip
Resid. Df Resid. Dev Df Deviance Pr(Chi)
198 265.33 NA NA NA
203 427.36 -5 -162.04 0
# After renaming the variables and using knitr
rownames(smokeTable) = gsub(“Race|RuralUrban|C$”, “”,
rownames(smokeTable) )
rownames(smokeTable) = gsub(“SexF”,”Female”,
rownames(smokeTable))
smokeTable[,1] = exp(smokeTable[,1])
knitr::kable(smokeTable, digits=4,
caption = “Odds of Regular Use of Chewing Tobacco, Snuff or Dip”)
Table 3: Odds of Regular Use of Chewing Tobacco, Snu or Dip
Estimate Std. Error z value Pr(|z|)
(Intercept) 0.0473 0.0849 -35.9626 0.0000
age 1.4106 0.0214 16.1053 0.0000
Female 0.1680 0.1107 -16.1118 0.0000
black 0.2244 0.1720 -8.6872 0.0000
hispanic 0.4791 0.1073 -6.8565 0.0000
asian 0.2190 0.3424 -4.4357 0.0000
native 1.1070 0.2866 0.3549 0.7227
pacific 2.4259 0.3973 2.2303 0.0257
3
Estimate Std. Error z value Pr(|z|)
Rural 2.5635 0.0894 10.5322 0.0000
smokeAgg1 = reshape2::dcast(smokeSub,
Age + Sex + Race + RuralUrban ~ ever_tobacco_hookah_or_wa,
length)
## Using everSmoke as value column: use value.var to override.
smokeAgg1 = na.omit(smokeAgg1)
smokeAgg1 = smokeAgg1[-7]
dim(smokeAgg1)
## [1] 207 6
# smokeModel
smokeAgg1$y = cbind(smokeAgg1$TRUE, smokeAgg1$FALSE)
smokeFit1 = glm(y ~ Age + Sex + Race + RuralUrban,
family=binomial(link=logit), data=smokeAgg1)
# We want to scale the variable Age,
# since the center age of intercept is 15, we substract 15 from values of variable
smokeAgg1$ageC = smokeAgg1$Age – 15
smokeFit1 = glm(y ~ ageC + Sex + Race + RuralUrban,
family=binomial(link=logit), data=smokeAgg1)
smokeTable1 = as.data.frame(summary(smokeFit1)$coef)
smokeFitReduced1 = glm(y ~ ageC + Sex + RuralUrban,
family=binomial(link=logit), data=smokeAgg1)
knitr::kable(anova(smokeFit1,smokeFitReduced1,test = “Chisq”),
digits = 2,caption = “Likelihood Ratio Test for Having Used Hookah or Waterpipe”)
Table 4: Likelihood Ratio Test for Having Used Hookah or Waterpipe
Resid. Df Resid. Dev Df Deviance Pr(Chi)
198 371.79 NA NA NA
203 625.40 -5 -253.61 0
# After renaming the variables and using knitr
rownames(smokeTable1) = gsub(“Race|RuralUrban|C$”, “”,
rownames(smokeTable1) )
rownames(smokeTable1) = gsub(“SexF”,”Female”,
rownames(smokeTable1))
smokeTable1[,1] = exp(smokeTable1[,1])
knitr::kable(smokeTable1, digits=4,
caption = “Odds of Having Used Hookah or Waterpipe”)
Table 5: Odds of Having Used Hookah or Waterpipe
Estimate Std. Error z value Pr(|z|)
(Intercept) 0.1780 0.0441 -39.1338 0.0000
4
Estimate Std. Error z value Pr(|z|)
age 1.5221 0.0116 36.2011 0.0000
Female 1.0414 0.0431 0.9424 0.3460
black 0.5249 0.0711 -9.0705 0.0000
hispanic 1.4155 0.0486 7.1475 0.0000
asian 0.5234 0.1188 -5.4507 0.0000
native 1.1773 0.1905 0.8569 0.3915
pacific 2.7478 0.2705 3.7366 0.0002
Rural 0.6794 0.0445 -8.6916 0.0000
5

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