# # Gradient Descent for Linear Regression In this problem we will...

## Question

# Gradient Descent for Linear Regression

In this problem we will use gradient descent to help us estimate the parameters for a Linear Regression model.

I begin with some artificial data. The variable $t$ depends on two variables: $x_1$ and $x_2$. The true relationship between t and $x_1$ and $x_2$ is $t = 2x_1 + x_2$ but we add some random noise.

{r}
set.seed(6)
x1 <- 5 * runif(10)
x2 <- 4 * runif(10)
t <- 2 * x1 + x2 + rnorm(10, sd = 2)
X <- cbind(1, x1, x2)


I can use lm() to find the OLS coefficient estimates.

{r}
lm(t ~ x1 + x2)$coefficients  We will now try to arrive at the same estimates by applying gradient descent to the Ordinary Least Squares Loss Function. The Loss function for Ordinary Least Squares Regression is: $$\mathcal{L} = \frac{1}{N}(\mathbf{t}^T\mathbf{t} - 2\mathbf{w}^T\mathbf{X}^T\mathbf{t} + \mathbf{w}^T\mathbf{X}^T\mathbf{X}\mathbf{w})$$ The gradient of$\mathcal{L}$with respect to$\mathbf{w}$is $$\nabla \mathcal{L} = \frac{-2}{N}\mathbf{X}^T\mathbf{t} + \frac{2}{N}\mathbf{X}^T\mathbf{X}\mathbf{w}$$ ### Task 1: Refering to the gradient in matrix notation, write a function to calculate the value of the gradient at a particular value of$\mathbf{w}$. The function will accept a vector w = c(intercept, w1, w2). You can use values of X and t or any other variables that are available in the global environment. {r} loss_grad <- function(w){ # write your function here }  {r} # do not modify. This chunk is used to check your results for grading. loss_grad(c(0,0,0)) loss_grad(lm(t ~ x1 + x2)$coefficients) # should be a vector of ~0 values.


One way for us to check if our gradient function is coded correctly is to perform numeric gradient checking.

Numeric gradient checking is the idea of adding a small perturbation to each element in the vector or matrix $\mathbf{w}$ one element at a time, and to measure the effect it has on the loss.

Complete the code in the following loop to find the numeric gradient estimate for w at point c(0,0,0).

{r}
w <- c(0, 0, 0)
epsilon <- 1e-4
numeric_gradient <- matrix(0, nrow = 3)
for(i in 1:length(w)){
# find the difference in the loss function between using w = c(0,0,0)
# and the value of the loss function when using w = c(0 + epsilon, 0, 0)
# do this for each element in w
}


Write a loop to perform gradient descent. Start at the point w = c(0,0,0) and iterate to estimate the coefficients to fit the linear model.

Using trial and error, play around with your choice of gamma (hint: start small) and how many iterations to run (probably between 10 and 100 thousand).

Print out your choice of gamma (no right answer) and the resulting coefficient estimates at the end of the gradient descent algorithm (should be within 0.001 of the estimates made by lm).

{r}

# The Effects of Regularization on the variation of parameter estimates

In this section, we will look at the effects of regularization on parameter estimates, especially in relation to the Bias-Variance Tradeoff.

From Wikipedia:

The Gauss-Markov theorem states that in a linear regression model in which the errors have expectation zero, are uncorrelated and have equal variances, the best linear unbiased estimator (BLUE) of the coefficients is given by the ordinary least squares (OLS) estimator. Here "best" means giving the lowest variance of the estimate, as compared to other unbiased, linear estimators.

https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem

In that regard, OLS provides estimates of coefficients with nice properties. These coefficient estimates, however, often have relatively high variance if there is high correlation between the predictors.

To illustrate this, I will generate some random data and use OLS to fit models to them.

For the generation of data, I will use library mvtnorm which is used to generate values from a multivariate-normal distribution.

{r}
library(mvtnorm)


Study and understand the code below.

The matrix sigma is a variance-covariance matrix. I use sigma as an argument in the function rmvnorm() when generating random multivariate normal data.

I generate matrices x1x2 (representing variables x1 and x2) and x3x4 (variables x3 and x4) and use cbind to create a matrix X.

{r x_creation}
set.seed(4)
sigma <- matrix(c(1,.9,.9,1), nrow = 2)
x1x2 <- rmvnorm(30, sigma = sigma)
x3x4 <- rmvnorm(30, sigma = 2 * sigma) # x3 and x4 has larger variance than x1 and x2
X <- cbind(x1x2,x3x4) # creation of matrix X
round(cor(X), 3) # rounded for easier reading


When we look at the correlation matrix of X, we see that variables x1 and x2 have high correlation (around 0.9) with each other, and low correlation with variables x3 and x4. Meanwhile x3 and x4 have high correlation with each other and low correlation with variables x1 and x2. This makes sense as we generated matrix x1x2 independently from matrix x3x4.

{r}
error = rnorm(30, sd = 1)
true_y <- 1.2 * X[,1] - 1.5 * X[,3] # creation of true_y
y <- true_y + error


I now generate values of y. We can see that the true values of y true_y depend only on variables x1 and x3 but we have also added some random noise.

{r}
model <- lm(y ~ X)
coefs <- model$coefficients coefs  Based on the 'true_y' we know that the true coefficients should be: - intercept: 0 - x1: 1.2 - x2: 0 - x3: -1.5 - x4: 0 However because of the random noise added to our data, we get something in the ballpark but different. I've written some code to explore how the estimates of the coefficients vary with randomness. I run a couple thousand iterations of a loop. In each iteration, I generate new random noise to add to the "true values" to produce new values of y. We fit a linear model between our new noisy y values and the X matrix. I store the model coefficients into matrix coef to keep track of these results. {r} reps <- 2000 coefs <- matrix(NA, nrow = reps, ncol = 5) # we will store the model coefficients here for(i in 1:reps){ set.seed(i) error = rnorm(30, sd = 1) y <- true_y + error model <- lm(y ~ X) coefs[i,] <- model$coefficients
}


Now that the loop has finished running, we can see the mean and variation of our coefficient estimates.

{r}
means <- round(colMeans(coefs), 4)
medians <- round(apply(coefs, 2, FUN = median), 4)
variances <- round(apply(coefs, 2, FUN = var), 4)
rbind(means, medians, variances)


We can see that the parameter estimates indeed are unbiased. The mean values of the parameter estimates indeed align closely with what we know to be the true parameter values (intercept: 0, x1: 1.2, x2: 0, x3: -1.5, x4: 0).

We also see the medians and the variances of these coefficient estimates.

{r}
par(mfrow = c(2,2))
hist(coefs[,2], breaks = 25, main = "Coefficient estimates for variable x1")
hist(coefs[,3], breaks = 25, main = "Coefficient estimates for variable x2")
hist(coefs[,4], breaks = 25, main = "Coefficient estimates for variable x3")
hist(coefs[,5], breaks = 25, main = "Coefficient estimates for variable x4")


The above plots confirm the same: the coefficients are unbiased in that their distributions are centered around the true coefficient values. We also get a sense of the variation that exists.

## Getting Parameter estimates for Lasso and Ridge regression models

We can do something similar for Lasso and Ridge regression models. Instead of using lm() to fit an OLS linear regression model, we use glmnet().

A Lasso fit is run by default, and ridge regression is used when we set the paramter alpha = 0. (Elasticnet is used for values of alpha between 0 and 1.)

The coefficients of glmnet can be extracted with the function coef(), along with a specified s. The argument s corresponds to the $\lambda$ that was used in the loss function. Lower values of $\lambda$ correspond to loss function that have low complexity penalties, while larger values of $\lambda$ correspond to loss functions with larger complexity penalties.

When run on a glmnet fit, coef() by default returns a sparse column matrix. So I call t() to transpose it, and as.matrix() to fill in the blanks with zeros.

{r}
library(glmnet)
set.seed(1)
error = rnorm(30, sd = 1)
y <- true_y + error
lassofit = glmnet(X, y)
# we can see lasso will push a paramter value down to 0
as.matrix(t(coef(lassofit, s = 0.5)))

ridgefit = glmnet(X, y, alpha = 0)
as.matrix(t(coef(ridgefit, s = 0.5)))


## Task 4: Simulate random data, fit Lasso and Ridge, track the coefficients

Use package glmnet to fit lasso and ridge regression models between y and X.

Follow my code example where I fit 2000 linear models to the data where different random noise was applied to the "true"" values of y.

For each iteration, fit Lasso and Ridge regression. Extract the coefficient estimates for $\lambda = 0.1$, $\lambda = 0.3$, and $\lambda = 0.5$ for both Lasso and Ridge regression models. Keep track of all the coefficient estimates in matrices (there are 6 matrices to make).

{r}
reps <- 2000

coefsLasso1 <- matrix(NA, nrow = reps, ncol = 5)
coefsLasso3 <- matrix(NA, nrow = reps, ncol = 5)
coefsLasso5 <- matrix(NA, nrow = reps, ncol = 5)
coefsRidge1 <- matrix(NA, nrow = reps, ncol = 5)
coefsRidge3 <- matrix(NA, nrow = reps, ncol = 5)
coefsRidge5 <- matrix(NA, nrow = reps, ncol = 5)

for(i in 1:reps){
set.seed(i)

# ... write your code here

}



Once the loop finishes running, create summary tables showing the mean, median, and variance of the coefficient estimates.

{r}
# Summary tables of the Lasso coefficients
# lambda = 0.1
# ...

# lambda = 0.3
# ...

# lambda = 0.5


**Comment on the effect of lambda on the distributions of the coefficient estimates of Lasso.**
{r}
# Summary tables for Ridge regression coefficient estimates
# lambda = 0.1
# ...

# lambda = 0.3
# ...

# lambda = 0.5

**Comment on the effect of lambda on the distributions of the coefficient estimates of Ridge Regression.**

## Task 5: Comment on a bunch of plots

If you succesfully ran your loop and stored the values, then the following code should work and produce plots. You might need to make some minor changes to get it to work.

I've written the code for you. You need to explain what it means.

{r, error = TRUE}
par(mfrow = c(2,2))
xlimits = c(min(coefs[,2]), max(coefs[,2]))
hist(coefs[,2], breaks = 25, main = "Coef of x1 OLS", xlim = xlimits)
hist(coefsLasso1[,2], breaks = 25, main = "Coef of x1 Lasso Lambda = 0.1", xlim = xlimits)
hist(coefsLasso3[,2], breaks = 25, main = "Coef of x1 Lasso Lambda = 0.3", xlim = xlimits)
hist(coefsLasso5[,2], breaks = 25, main = "Coef of x1 Lasso Lambda = 0.5", xlim = xlimits)
hist(coefs[,2], breaks = 25, main = "Coef of x1 OLS", xlim = xlimits)
hist(coefsRidge1[,2], breaks = 25, main = "Coef of x1 Ridge Lambda = 0.1", xlim = xlimits)
hist(coefsRidge3[,2], breaks = 25, main = "Coef of x1 Ridge Lambda = 0.3", xlim = xlimits)
hist(coefsRidge5[,2], breaks = 25, main = "Coef of x1 Ridge Lambda = 0.5", xlim = xlimits)


Comment on the plots, talk about the effect of lambda. How does lasso and ridge regression affect the parameter estimates you get? Keep in mind what the true value of the coefficient should be. What kind of bias do you see? What can you say about the variance of estimates?

{r, error = TRUE}
par(mfrow = c(2,2))
xlimits = c(min(coefs[,3]), max(coefs[,3]))
hist(coefs[,3], breaks = 25, main = "Coef of x2 OLS", xlim = xlimits)
hist(coefsLasso1[,3], breaks = 25, main = "Coef of x2 Lasso Lambda = 0.1", xlim = xlimits)
hist(coefsLasso3[,3], breaks = 25, main = "Coef of x2 Lasso Lambda = 0.3", xlim = xlimits)
hist(coefsLasso5[,3], breaks = 25, main = "Coef of x2 Lasso Lambda = 0.5", xlim = xlimits)
hist(coefs[,3], breaks = 25, main = "Coef of x2 OLS", xlim = xlimits)
hist(coefsRidge1[,3], breaks = 25, main = "Coef of x2 Ridge Lambda = 0.1", xlim = xlimits)
hist(coefsRidge3[,3], breaks = 25, main = "Coef of x2 Ridge Lambda = 0.3", xlim = xlimits)
hist(coefsRidge5[,3], breaks = 25, main = "Coef of x2 Ridge Lambda = 0.5", xlim = xlimits)


Comment on the plots, talk about the effect of lambda. How does lasso and ridge regression affect the parameter estimates you get? Keep in mind what the true value of the coefficient should be. What kind of bias do you see? What can you say about the variance of estimates?

{r, error = TRUE}
par(mfrow = c(2,2))
xlimits = c(min(coefs[,4]), max(coefs[,4]))
hist(coefs[,4], breaks = 25, main = "Coef of x3 OLS", xlim = xlimits)
hist(coefsLasso1[,4], breaks = 25, main = "Coef of x3 Lasso Lambda = 0.1", xlim = xlimits)
hist(coefsLasso3[,4], breaks = 25, main = "Coef of x3 Lasso Lambda = 0.3", xlim = xlimits)
hist(coefsLasso5[,4], breaks = 25, main = "Coef of x3 Lasso Lambda = 0.5", xlim = xlimits)
hist(coefs[,4], breaks = 25, main = "Coef of x3 OLS", xlim = xlimits)
hist(coefsRidge1[,4], breaks = 25, main = "Coef of x3 Ridge Lambda = 0.1", xlim = xlimits)
hist(coefsRidge3[,4], breaks = 25, main = "Coef of x3 Ridge Lambda = 0.3", xlim = xlimits)
hist(coefsRidge5[,4], breaks = 25, main = "Coef of x3 Ridge Lambda = 0.5", xlim = xlimits)


Comment on the plots, talk about the effect of lambda. How does lasso and ridge regression affect the parameter estimates you get? Keep in mind what the true value of the coefficient should be. What kind of bias do you see? What can you say about the variance of estimates?

{r, error = TRUE}
par(mfrow = c(2,2))
xlimits = c(min(coefs[,5]), max(coefs[,5]))
hist(coefs[,5], breaks = 25, main = "Coef of x4 OLS", xlim = xlimits)
hist(coefsLasso1[,5], breaks = 25, main = "Coef of x4 Lasso Lambda = 0.1", xlim = xlimits)
hist(coefsLasso3[,5], breaks = 25, main = "Coef of x4 Lasso Lambda = 0.3", xlim = xlimits)
hist(coefsLasso5[,5], breaks = 25, main = "Coef of x4 Lasso Lambda = 0.5", xlim = xlimits)
hist(coefs[,5], breaks = 25, main = "Coef of x4 OLS", xlim = xlimits)
hist(coefsRidge1[,5], breaks = 25, main = "Coef of x4 Ridge Lambda = 0.1", xlim = xlimits)
hist(coefsRidge3[,5], breaks = 25, main = "Coef of x4 Ridge Lambda = 0.3", xlim = xlimits)
hist(coefsRidge5[,5], breaks = 25, main = "Coef of x4 Ridge Lambda = 0.5", xlim = xlimits)


Comment on the plots, talk about the effect of lambda. How does lasso and ridge regression affect the parameter estimates you get? Keep in mind what the true value of the coefficient should be. What kind of bias do you see? What can you say about the variance of estimates?

Let's revisit the exact same artificial data we created at the beginning of the homework.

The variable $t$ depends on two variables: $x_1$ and $x_2$. The true relationship between t and $x_1$ and $x_2$ is $t = 2x_1 + x_2$ but we add some random noise.

{r}
set.seed(6)
x1 <- 5 * runif(10)
x2 <- 4 * runif(10)
t <- 2 * x1 + x2 + rnorm(10, sd = 2)
X <- cbind(1, x1, x2)


The Loss function for Ordinary Least Squares Regression is:

$$\mathcal{L} = \frac{1}{N}(\mathbf{t} - \mathbf{Xw})^T(\mathbf{t} - \mathbf{Xw}) = \frac{1}{N}(\mathbf{t}^T\mathbf{t} - 2\mathbf{w}^T\mathbf{X}^T\mathbf{t} + \mathbf{w}^T\mathbf{X}^T\mathbf{X}\mathbf{w})$$

The Loss function for Ridge Regression is:

$$\mathcal{L'} = \mathcal{L} + \lambda \mathbf{w}^T\mathbf{w}$$

Write a function to calculate the ridge regression loss.

The function will accept a vector w = c(intercept, w1, w2) and a value of lambda.

{r}
ridge_loss = function(w, lambda){

}


{r, error = TRUE}
# for grading. I got 12.11428 and 27.11428
ridge_loss(c(1,1,1), lambda = 0)
ridge_loss(c(1,1,1), lambda = 5)


Use optim() to estimate the best parameter estimates.

{r, error = TRUE}
# done for you
optim(par = c(0,0,0), ridge_loss, lambda = 0 )$par # should return the same value as OLS optim(par = c(0,0,0), ridge_loss, lambda = 1 )$par


Find the gradient of Ridge Regression Loss function.

$$\frac{\partial \mathcal{L'} }{\partial \mathbf{w}} =$$

Set the gradient equal to 0, and solve for the best estimate of $\mathbf{w}$ that achieves this.

$$\mathbf{\hat w}_{ridge} =$$

Using matrix operation, verify that your closed form solution of $$\mathbf{\hat w}_{ridge}$$ (closely) matches the best parameter estimates found by using optim() for lambda = 1

{r}


Write a function to calculate the value of the gradient at a particular value of $\mathbf{w}$.

The function will accept a vector w = c(intercept, w1, w2) and a value of lambda. You can use values of X and t or any other variables that are available in the global environment.

{r}

}


Write a loop to perform gradient descent with lambda = 1. Start at the point w = c(0,0,0) and iterate.

Using trial and error, play around with your choice of gamma (hint: start small) and how many iterations to run (probably between 10 and 100 thousand).

Print out your choice of gamma (no right answer) and the resulting coefficient estimates at the end of the gradient descent algorithm (should be sorta close to the estimates produced by running optim() on the ridge regression loss function).

{r}


## Solution Preview

These solutions may offer step-by-step problem-solving explanations or good writing examples that include modern styles of formatting and construction of bibliographies out of text citations and references. Students may use these solutions for personal skill-building and practice. Unethical use is strictly forbidden.

---
title:
author:
date: ''
output:
html_document: default
pdf_document: default
---
{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)  # Gradient Descent for Linear Regression In this problem we will use gradient descent to help us estimate the parameters for a Linear Regression model. ## Start with Some artificial Data I begin with some artificial data. The variable$t$depends on two variables:$x_1$and$x_2$. The true relationship between t and$x_1$and$x_2$is$t = 2x_1 + x_2$but we add some random noise. {r} set.seed(6) x1 <- 5 * runif(10) x2 <- 4 * runif(10) t <- 2 * x1 + x2 + rnorm(10, sd = 2) X <- cbind(1, x1, x2)  I can use lm() to find the OLS coefficient estimates. {r} lm(t ~ x1 + x2)$coefficients


We will now try to arrive at the same estimates by applying gradient descent to the Ordinary Least Squares Loss Function.

The Loss function for Ordinary Least Squares Regression is:

$$\mathcal{L} = \frac{1}{N}(\mathbf{t}^T\mathbf{t} - 2\mathbf{w}^T\mathbf{X}^T\mathbf{t} + \mathbf{w}^T\mathbf{X}^T\mathbf{X}\mathbf{w})$$

The gradient of $\mathcal{L}$ with respect to $\mathbf{w}$ is

$$\nabla \mathcal{L} = \frac{-2}{N}\mathbf{X}^T\mathbf{t} + \frac{2}{N}\mathbf{X}^T\mathbf{X}\mathbf{w}$$

Refering to the gradient in matrix notation, write a function to calculate the value of the gradient at a particular value of $\mathbf{w}$.

The function will accept a vector w = c(intercept, w1, w2). You can use values of X and t or any other variables that are available in the global environment.

{r}
-2/nrow(X)*t(X)%*%t + 2/nrow(X)*t(X)%*%X%*%w
}


{r}
# do not modify. This chunk is used to check your results for grading.

loss_grad(lm(t ~ x1 + x2)$coefficients) # should be a vector of ~0 values.  ## Numeric Gradient Checking One way for us to check if our gradient function is coded correctly is to perform numeric gradient checking. Numeric gradient checking is the idea of adding a small perturbation to each element in the vector or matrix$\mathbf{w}$one element at a time, and to measure the effect it has on the loss. ### Task 2: Complete the code in the following loop to find the numeric gradient estimate for w at point c(0,0,0). {r} w <- c(0, 0, 0) epsilon <- 1e-4 numeric_gradient <- matrix(0, nrow = 3) for(i in 1:length(w)){ # write your code # find the difference in the loss function between using w = c(0,0,0) # and the value of the loss function when using w = c(0 + epsilon, 0, 0) # do this for each element in w wh <- w wh[i] <- wh[i] + epsilon N <- nrow(X) numeric_gradient[i,1] <- t(t-X%*%wh)%*%(t-X%*%wh) - t(t-X%*%w)%*%(t-X%*%w) numeric_gradient[i,1] <- numeric_gradient[i,1]/N } numeric_gradient  Compare the results of your numeric gradient estimate with the values you calculated with your gradient function. The gradient of loss function and its numerical approximation are nearly identical after diviided by the epsilon for the numerical estimate. ## Gradient Descent ### Task 3: Write a loop to perform gradient descent. Start at the point w = c(0,0,0) and iterate to estimate the coefficients to fit the linear model. Using trial and error, play around with your choice of gamma (hint: start small) and how many iterations to run (probably between 10 and 100 thousand). Print out your choice of gamma (no right answer) and the resulting coefficient estimates at the end of the gradient descent algorithm (should be within 0.001 of the estimates made by lm). {r} # your code here N <- 10000 gamma <- 0.045 w = c(0,0,0) for (i in 1:N) { w <- w - gamma*loss_grad(w) } rownames(w) <- c("intercept", "x1", "x2") paste("gamma = ", gamma) t(w)  # The Effects of Regularization on the variation of parameter estimates In this section, we will look at the effects of regularization on parameter estimates, especially in relation to the Bias-Variance Tradeoff. From Wikipedia: The Gauss-Markov theorem states that in a linear regression model in which the errors have expectation zero, are uncorrelated and have equal variances, the best linear unbiased estimator (BLUE) of the coefficients is given by the ordinary least squares (OLS) estimator. Here "best" means giving the lowest variance of the estimate, as compared to other unbiased, linear estimators. https://en.wikipedia.org/wiki/Gauss%E2%80%93Markov_theorem In that regard, OLS provides estimates of coefficients with nice properties. These coefficient estimates, however, often have relatively high variance if there is high correlation between the predictors. To illustrate this, I will generate some random data and use OLS to fit models to them. For the generation of data, I will use library mvtnorm which is used to generate values from a multivariate-normal distribution.... By purchasing this solution you'll be able to access the following files: Solution.Rmd. # 50% discount Hours Minutes Seconds$40.00 \$20.00
for this solution

PayPal, G Pay, ApplePay, Amazon Pay, and all major credit cards accepted.

### Find A Tutor

View available Statistics-R Programming Tutors

Get College Homework Help.

Are you sure you don't want to upload any files?

Fast tutor response requires as much info as possible.