BTRY/STSCI 4520 Homework 4
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############# BTRY/STSCI 4520 ########################
Copyright By PowCoder代写 加微信 powcoder
############# Homework 4 ########################
############# Due: April 13, 2018 ########################
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# Instructions: save this file in the format
# Complete each question using code below the question number.
# You need only upload this file to CMS.
# Note, we assume your working directory contains any files
# that accompany this one.
# Further note: 10% will be deducted if your file produces
# an error when run. If your code produces an error and you
# cannot find it, comment out that portion of the code and we
# will give partial credit for it.
# Do not use either the function set.seed() or rm(list=ls())
# in your code.
# The file 1DOptimizers.r contains the functions
# GoldenSection and NewtonRaphson from Lecture 11.
# If you wish to use them, you can load these up with
source(‘1DOptimizers.R’)
# if you wish to use them. DO NOT modify these — when
# we look at your code, we will have this file in our
# working directory as it currently is. We will not
# replace it with a modified version.
# A note on these, in many cases you will want to optimize
# functions that have more than one argument, but neither
# NewtonRaphson nor GoldenSection have the option to add
# more inputs.
# You can either choose to re-write these functions, or
# an alternative is to define a new function that inherits
# the other inputs as constants.
# Eg in 1a below, within Mixed.GS, I can define a function
# newfn = function(x){ fn(x,mean1,mean2,sd1,sd2) }
# to use within GoldenSection. Here newfn will not see
# mean1, mean2, sd1, sd2, but will look in the environment
# in Mixed.GS to find these quantities, rather than in the
# global environment.
# These sort of issues come up all the time when using
# code that others have written. Not allowing additional inputs
# is poor form (sorry!), but there are many other times
# when you have to find ways around in-built (often unintentional)
# restrictions.
######################################
# Question 1: Domains of Convergence #
######################################
# In class we saw that even finding the mode (the
# most probable value) of a mixture distribution can
# be difficult. In particular, we looked at
# f(x) = 0.5*dnorm(x, mean = 0, sd = 1) + 0.5*dnorm(x,mean=2,sd=2)
# Here we will observe that transforming f can have
# an important effect.
# In particular, we will examine what happens when we
# try to optimize f(x), versus log( f(x) ) when we
# examine different starting points
# a) Write a function to find the maximum of f(x)
# using Golden Section search starting from an interval
# of [-m,m]. Your function should have inputs that are
# – mean1, mean2: the means of the two components
# – sd1, sd2: the standard deviations of the two components
# – m – to define the search interval [-m, m]
# – tol – the tolerance criteria
# – maxit — maximum number of iterations
# It should return the optimizing value of x in argmax,
# the value of f in value, and the number of iterations.
Mixed.GS = function(mean1,mean2,sd1,sd2,m,tol=1e-6,maxit=1000)
return(list( argmax = , value = , niter = )
# Using the values
# along with settings
# record the argmax values found and the number of iterations taken
opt.x.gs =
niter.gs =
# b) We will compare this to using Newton-Raphson. Write a function
# to obtain the NewtonRaphson estimate starting from m with
# the same inputs and outputs as part a:
Mixed.NR = function(mean1,mean2,sd1,sd2,m,tol=1e-6,maxit=1000)
return(list( argmax = , value = , niter = )
# Again, store the values that you found but this time for
m2 = -10:10
# along with the means and variances above.
opt.x.nr =
niter.nr =
# c) Repeat a and b using log(f) instead of f
Mixed.GS.log = function(mean1,mean2,sd1,sd2,m,tol=1e-6,maxit=1000)
return(list( argmax = , value = , niter = )
Mixed.NR.log = function(mean1,mean2,sd1,sd2,m,tol=1e-6,maxit=1000)
return(list( argmax = , value = , niter = )
# How do the convergence properties change? Why?
opt.x.gs.log =
niter.gs.log =
opt.x.nr.log =
niter.nr.log =
# d) Repeat the experiments above but with
# params = (mean1,mean2,sd1,sd2) given by
params2 = c(-2,2,2,1.5)
opt.x.gs2 =
niter.gs2 =
opt.x.nr2 =
niter.nr2 =
opt.x.gs.log2 =
niter.gs.log2 =
opt.x.nr.log2 =
niter.nr.log2 =
params3 = c(-2,2,1,0.8)
opt.x.gs3 =
niter.gs3 =
opt.x.nr3 =
niter.nr3 =
opt.x.gs.log3 =
niter.gs.log3 =
opt.x.nr.log3 =
niter.nr.log3 =
# Describe how your results change in this case. Why?
############################
# Question 2: Optimization #
############################
# In particle physics, when muons decay, they emit electrons
# in a direction with distribution relative to their orientation.
# This distribution has been calculated to have density
# f(x;alpha) = (1+alpha x)/2
# where x is cos(theta) and theta is the angle of the direction, so
# x lies in [-1, 1]. Here alpha also lies in [-1,1].
# A sample of such x’s is in
muon = read.table(‘muon.txt’)$V1
# A standard way to estimate alpha is to maximize the log
# likelihood. That is, we seek to maximize
# l(alpha) = \sum_i log[ (1 + alpha x_i)/2 ]
# a) Write a function to apply a Golden section search
# (you may copy from notes) to find the maximum likelihood estimate
# for alpha, up to some tolerance.
# It should return
# – opt.alpha: the value that maximizes the log likelihood
# – value: the value of the log likelihood
# – niter: the number of iterations taken
muon.golden.section = function(data, tol=1e-6,maxit=1000)
return( list( opt.alpha = , value = , niter = ) )
# b) Write a function to provide a bias-corrected normal-theory
# bootstrap confidence interval for your estimate. Test this using
# nboot = 200 bootstrap samples. You should return the bias-corrected
# estimate and the confidence interval
boot.muon.ci = function(data,tol=1e-6,nboot=200)
return( list(corrected.estimate = , confint = ) )
# c) If amax is the value of alpha that maximizes l(alpha), an
# alternative confidence interval is to find values alpha such
# l(amax) – l(alpha) = 3.84
# there should be one point smaller than amax and one larger than it.
# To obtain these we will try a secant method. Since amax should
# be at the top of the likelihood function, we should be able to
# head left or right to start heading downhill.
# Your function should implement the secant method (you may code
# this up as a separate function) and then start from initial
# points amax and -1 to head left, and amax and 1
# to head right.
# When you carry out the iteration, you should make sure that
# you never have an estimate outside the interval [-1,1], by
# taking any such value back to the nearest endpoint.
# Return the confidence interval.
likelihood.ratio.ci = function(data,amax,tol=1e-6)
return(confint)
#########################################################
# Question 3: Rejection Sampling for a Truncated Normal #
#########################################################
# In this question we will look at generating data at one
# end of a normal distribution. Specifically, we consider
# trying to simulate X as N(0,1), conditional on X > a
# where a is positive. Formally, we can write the density in
# f(x) = dnorm(x)*(x>a)/(1-pnorm(a))
# This can be quite challenging when a is very large as
# the approximations in pnorm and in qnorm become unstable.
# a) The first option is to simply generate normal data and
# throw away the data for which x < a. Write a function
# to generate n samples this way. Return the samples X and the
# number of random numbers N needed to generate them.
rtruncnorm1 = function(n,a){
return(list( X = , N = ) )
# What percentage of samples do you keep? (Bonus 1 point
# for being as efficient as possible.
# b) An alternative is to perform rejection sampling. To
# do this, we will use a shifted exponential distribution.
# That is, we will look at Z=Y+a where Y ~ Exp(r). Since
# the Y's are all positive, Z is necessarily greater
# The density of Z can be written down as
# g(z) = r*exp(-r*(z-a))
# and we can obtain a Z from a uniform U by
# Z = a - log(1-U)/r
# An important note about rejection sampling; we only need
# to know the form of a density, not the constants. That is
# if we can generate
# X ~ g(x)
# and want Y ~ C*f(x) where C is a number we can't calculate
# easily, we can still perform rejection sampling to generate
# y by finding k such that k*g(x) > f(x) for all x. Then
# the recipe is the same:
# 1. Simulate X ~ g(x)
# 2. Simulate U ~ [0, k*g(x)]
# 3. Keep X if U < f(x)
# i) For a fixed a and r, find the smallest value of k such that
# k * dexp(x-a,r) > dnorm(x)
# for all x > a (the x>a restriction may be important). You may
# submit scanned hand-calculations if you wish, or give the math
# in comments below.
# ii) For a fixed value of a, what is the optimal exponential rate
# r to choose? What value of k does this yield?
# c) Write a function to derive truncated normal random
# variables using this rejection sampler. As in part a, return
# n samples as well as a count of the number of random numbers
rtruncnorm2 = function(n,a){
return(list( X = , N = ) )
# d) Plot the effort to generate 1000 samples for a = 1,2,3,4
# for both methods above along with the expected effort for
# rtruncnorm1.
# Store the effort in a 4-by-3 table
# BONUS: Find a way to generate truncated normals with no
# waste, assuming you can use qnorm and pnorm. How does this
# perform at a = 2? Compare all three methods at a=10.
##################################
# Question 4: Poisson Regression #
##################################
# The Poisson distribution is often used for modeling count
# data (eg, the number of trucks going past Giles’ house between
# 5 and 6 in the morning). It has density parameterized by the
# rate r with formula:
# P(Y = k; r) = r^k * exp(-r)/factorial(k)
# Here we will examine relating a count to a covariate (day of
# the week in the case of Giles’ ability to sleep in, but see
# examples below).
# The idea is to model the Poisson rate as changing with X and
# in particular
# log(r) = b0 + b1 X
# So that each Yi has its own rate Ri that is determined
# by Xi. This means that the probability that we see
# the data Yi at covariate Xi is
# P(Y = Yi| Xi) = exp(Yi*(b0+b1*Xi)) * exp(-exp(b0+b1*Xi))/factorial(Yi)
# so that the likelihood is the product of these over i.
# Here, we will maximize the log likelihood (ie, the sum of the
# log probabilities)
# l(b0,b1|Y,X) = sum log( P(Yi | Xi; b0,b1) )
# = sum Y_i*(b0 + b1*Xi) – exp(b0 + b1*Xi) – log factorial(Yi)
# Since the last term does not change with b0 and b1, we usually drop it
# and just try to maximize
# l(b0,b1|Y,X) = sum Y_i*(b0 + b1*Xi) – exp(b0 + b1*Xi)
# As an example, the data in
Flu = read.csv(‘Influenza.txt’)
# gives the number of Influenza cases reported for high school
# students along with the number of days since the outbreak.
# We will model Students as Y and Days as X above.
# You may want to write functions evaluating the first derivative
# vector and matrix of second derviatives to use in parts
# a and b below.
# a) Write a co-ordinate ascent algorithm to maximize l(b0,b1|Y,X)
# using a one-dimensional Newton-Raphson method by first maximizing
# for b0, then b1, then b0 and so on until convergence. You may copy
# the Newton-Raphson algorithm from notes if you wish.
# Your function should return the optimum values of b0 and b1
# along with the value of the likelihood at the optimum and the
# number of iterations taken to achieve it. How many iterations
# did you need to fit the Influenza data?
# Start from b0=0, b1=0
PoissonReg1 = function(b0,b1,X,Y,tol=1e-6,maxit=1000)
return( list( b0 = , b1 = , likelihood = , num.iter = ) )
# b) Compare this to applying a two-dimensional Newton-Raphson
# method to update b0 and b1 simultaneously. Write a function to
# perform this method and report the same quantities along with the
# iteration history.
PoissonReg2 = function(beta,X,Y,tol=1e-6,maxit=1000)
return( list( beta = , likelihood = , num.iter = , iterhist = ) )
# Produce a contour plot of the likelihood over the range of the iteration
# history and add the path taken by the Newton steps. To view this
# reasonably, you should set any likelihood value less than -800 to be 800.
# C) Carry out a parametric bootstrap based on your fitted parameters
# (you can generate Poisson random variables with the rpois() function).
# Provide confidence intervals for b0, b1 and b0+b1*5 — the last of these
# is the log of the expected count at 5 days. Examine the bootstrap
# distribution for exp(b0+b1*5) — would a symmetric confidence interval
# be appropriate here?
Flu.confint.b0 =
Flu.confint.b1 =
Flu.confint.5 =
# BONUS: In the linear regression function lm, you can
# specify a vector of weights as an input. If you do,
# the coefficients are calculated to minimize
# sum Wi (Yi – b0 – b1 Xi)^2
# this minimum can be calculated in matrix terms to
# satisfy (in R code):
# b = solve(t(X) %*% diag(W) %*% X, t(X) %*% diag(W) %*% Y)
# Re-write your function in part b to repeatedly use the
# lm function and weights (which change each iteration).
# This is one example of a general procedure called
# iteratively reweighted least squares (IRWLS) that is used
# throughout Generalized Linear Models.
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