STAT 420代做、R程序语言调试、R代写代写、Likelihood Ratio代做

日期：2019-04-25 10:54

STAT 420: Homework 12

Spring 2019, R. Zhu

Due: Tuesday, April 23 by 11:30 PM CT

Contents

Directions 1

Assignment 2

Exercise 1 (Simulating Wald and Likelihood Ratio Tests) . . . . . . . . . . . . . . . . . . . . . . . 2

Exercise 2 (Surviving the Titanic) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Exercise 3 (Breast Cancer Detection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Directions

Students are encouraged to work together on homework. However, sharing, copying or providing any part of

a homework solution or code is an infraction of the University’s rules on Academic Integrity. Any violation

will be punished as severely as possible.

Final submissions must be uploaded to our Compass 2g site on the Homework page. No email, hardcopy, or

late submissions will be accepted.

Your assignment must be submitted through the submission link on Compass 2g. You have two

options to submit your file:

– [PDF format] Attach two files:

Your RMarkdown file, saved as hw11_yourNetID.Rmd. For example hw11_rqzhu.Rmd.

The result of knitting your RMarkdown file as hw11_yourNetID.pdf. For example

hw10_rqzhu.pdf.

– [HTML format] Attach one .zip file, named hw11_yourNetID.zip, which contains:

Your RMarkdown file which should be saved as hw11_yourNetID.Rmd.

The result of knitting your RMarkdown file as hw11_yourNetID.html.

Your resulting .html/.pdf file will be considered a “report” which is the material that will determine

the majority of your grade. Be sure to visibly include all R code and output that is relevant to answering

the exercises. (You do not need to include irrelevant code you tried that resulted in error or did not

answer the question correctly.)

You are granted an unlimited number of submissions, but only the last submission before the deadline

will be viewed and graded.

If you use this .Rmd file as a template, be sure to remove the directions section. Consider removing

eval = FALSE from any code chunks provided in the template, if you would like to run that code as

part of your assignment.

Your .Rmd file should be written such that, if it is placed in a folder with any data your are asked to

import, it will knit properly without modification.

Unless otherwise stated, you may use R for each of the exercises.

Be sure to read each exercise carefully!

Include your Name and NetID in the final document, not only in your filenames.

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Assignment

Exercise 1 (Simulating Wald and Likelihood Ratio Tests)

In this exercise we will investigate the distributions of hypothesis tests for logistic regression. For this exercise,

we will use the following predictors.

sample_size = 150

set.seed(114)

x1 = rnorm(n = sample_size)

x2 = rnorm(n = sample_size)

x3 = rnorm(n = sample_size)

Recall that

p(x) = P[Y = 1 | X = x]

Consider the true model

(a) To investigate the distributions, simulate from this model 2500 times. To do so, calculate

P[Y = 1 | X = x]

for an observation, and then make a random draw from a Bernoulli distribution with that success probability.

(Note that a Bernoulli distribution is a Binomial distribution with parameter n = 1. There is no direction

function in R for a Bernoulli distribution.)

Each time, fit the model:

Store the test statistics for two tests:

The Wald test for H0 : β2 = 0, which we say follows a standard normal distribution for “large” samples

The likelihood ratio test for H0 : β2 = β3 = 0, which we say follows a χ

2 distribution (with some degrees

of freedom) for “large” samples

(b) Plot a histogram of the empirical values for the Wald test statistic. Overlay the density of the true

distribution assuming a large sample.

(c) Use the empirical results for the Wald test statistic to estimate the probability of observing a test statistic

larger than 1. Also report this probability using the true distribution of the test statistic assuming a large

sample.

(d) Plot a histogram of the empirical values for the likelihood ratio test statistic. Overlay the density of the

true distribution assuming a large sample.

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(e) Use the empirical results for the likelihood ratio test statistic to estimate the probability of observing

a test statistic larger than 5. Also report this probability using the true distribution of the test statistic

assuming a large sample.

(f) Repeat (a)-(e) but with simulation using a smaller sample size of 10. Based on these results, is this

sample size large enough to use the standard normal and χ

2 distributions in this situation? Explain.

sample_size = 10

set.seed(114)

x1 = rnorm(n = sample_size)

x2 = rnorm(n = sample_size)

x3 = rnorm(n = sample_size)

Exercise 2 (Surviving the Titanic)

For this exercise use the ptitanic data from the rpart.plot package. (The rpart.plot package depends on

the rpart package.) Use ?rpart.plot::ptitanic to learn about this dataset. We will use logistic regression

to help predict which passengers aboard the Titanic will survive based on various attributes.

# install.packages("rpart")

# install.packages("rpart.plot")

library(rpart)

library(rpart.plot)

data("ptitanic")

For simplicity, we will remove any observations with missing data. Additionally, we will create a test and

train dataset.

ptitanic = na.omit(ptitanic)

set.seed(114)

trn_idx = sample(nrow(ptitanic), 300)

ptitanic_trn = ptitanic[trn_idx, ]

ptitanic_tst = ptitanic[-trn_idx, ]

(a) Consider the model β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x3x4

where

p(x) = P[Y = 1 | X = x]

is the probability that a certain passenger survives given their attributes and

x1 is a dummy variable that takes the value 1 if a passenger was 2nd class.

x2 is a dummy variable that takes the value 1 if a passenger was 3rd class.

x3 is a dummy variable that takes the value 1 if a passenger was male.

x4 is the age in years of a passenger.

Fit this model to the training data and report its deviance.

(b) Use the model fit in (a) and an appropriate statistical test to determine if class played a significant role

in surviving on the Titanic. Use α = 0.01. Report:

The null hypothesis of the test

The test statistic of the test

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The p-value of the test

A statistical decision

A practical conclusion

(c) Use the model fit in (a) and an appropriate statistical test to determine if an interaction between age

and sex played a significant role in surviving on the Titanic. Use α = 0.01. Report:

The null hypothesis of the test

The test statistic of the test

The p-value of the test

A statistical decision

A practical conclusion

(d) Use the model fit in (a) as a classifier that seeks to minimize the misclassification rate. Classify each of

the passengers in the test dataset. Report the misclassification rate, the sensitivity, and the specificity of this

classifier. (Use survived as the positive class.)

Exercise 3 (Breast Cancer Detection)

For this exercise we will use data found in wisc-train.csv and wisc-test.csv, which contain train and test

data, respectively. wisc.csv is provided but not used. This is a modification of the Breast Cancer Wisconsin

(Diagnostic) dataset from the UCI Machine Learning Repository. Only the first 10 feature variables have

been provided. (And these are all you should use.)

UCI Page

Data Detail

You should consider coercing the response to be a factor variable if it is not stored as one after importing the

data.

(a) The response variable class has two levels: M if a tumor is malignant, and B if a tumor is benign. Fit

three models to the training data.

An additive model that uses radius, smoothness, and texture as predictors

An additive model that uses all available predictors

A model chosen via backwards selection using AIC. Use a model that considers all available predictors

as well as their two-way interactions for the start of the search.

For each, obtain a 5-fold cross-validated misclassification rate using the model as a classifier that seeks to

minimize the misclassification rate. Based on this, which model is best? Relative to the best, are the other

two underfitting or over fitting? Report the test misclassification rate for the model you picked as the best.

(b) In this situation, simply minimizing misclassifications might be a bad goal since false positives and false

negatives carry very different consequences. Consider the M class as the “positive” label. Consider each of the

probabilities stored in cutoffs in the creation of a classifier using the additive model fit in (a).

cutoffs = seq(0.01, 0.99, by = 0.01)

That is, consider each of the values stored in cutoffs as c. Obtain the sensitivity and specificity in the test

set for each of these classifiers. Using a single graphic, plot both sensitivity and specificity as a function of

the cutoff used to create the classifier. Based on this plot, which cutoff would you use? (0 and 1 have not

been considered for coding simplicity. If you like, you can instead consider these two values.)