Exercise 1 Solution

Includes:

  1. Lion noses linear regression
  2. Data generation consistent with model
  3. Linear regression of this first dataset
  4. In-class Sampling Distribution Simulation Assignment

Document Preamble

Load libraries

library(knitr)
library(abd)

Settings for Knitr (optional)

opts_chunk$set(fig.width = 8, fig.height = 6)

1. Lion noses linear regression:

Data entry

data(LionNoses)
head(LionNoses)
  age proportion.black
1 1.1             0.21
2 1.5             0.14
3 1.9             0.11
4 2.2             0.13
5 2.6             0.12
6 3.2             0.13

Fit linear model

lm.nose<-lm(age~proportion.black, data=LionNoses)

Parameters:

Coefficients and residual variation are stored in lmfit:

coef(lm.nose)
     (Intercept) proportion.black 
       0.8790062       10.6471194 
summary(lm.nose)$sigma # residual variation
[1] 1.668764

What else is stored in lmfit? (residuals, variance covariance matrix, etc)

names(lm.nose)
 [1] "coefficients"  "residuals"     "effects"       "rank"         
 [5] "fitted.values" "assign"        "qr"            "df.residual"  
 [9] "xlevels"       "call"          "terms"         "model"        
names(summary(lm.nose))
 [1] "call"          "terms"         "residuals"     "coefficients" 
 [5] "aliased"       "sigma"         "df"            "r.squared"    
 [9] "adj.r.squared" "fstatistic"    "cov.unscaled"

2. Data generation consistent with fitted model

## Use the same sampmle size Sample size - use length so it matches sample size of original data
n <- length(LionNoses$age)

## Predictor - copy of original proporation black data, now in vector
p.black <- LionNoses$proportion.black

## Parameters
sigma <- summary(lm.nose)$sigma # residual variation
betas <- coef(lm.nose)# regression coefficients

## Errors and response
# Residual errors are modeled as ~ N(0, sigma)
epsilon <- rnorm(n, 0, sigma)

# Response is modeled as linear function plus residual errors
y <- betas[1] + betas[2]*p.black + epsilon

3. Linear regression of this generated dataset

# Fit of model to simulated data:  
lmfit.generated <- lm(y ~ p.black)
summary(lmfit.generated)

Call:
lm(formula = y ~ p.black)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8741 -0.7826 -0.3292  0.7713  3.2279 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   0.8138     0.5025   1.620    0.116    
p.black      10.8301     1.3335   8.121 4.58e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.474 on 30 degrees of freedom
Multiple R-squared:  0.6874,    Adjusted R-squared:  0.6769 
F-statistic: 65.96 on 1 and 30 DF,  p-value: 4.579e-09

In-Class Sampling Distribution Simulation Assignment

Exercise 1:

  1. Generate 5000 datasets using the same code
  2. Fit a linear regression model to each dataset “lm.temp”
  3. Store the estimates of \(\beta_1\)

Hint: if you get stuck, try starting with a small number of simulations (less than 5000) until you get the code right.

#   set up a matrix of size 5000 by 1 to store our estimates of beta_1
nsims <- 5000 # number of simulations
beta.hat<- matrix(NA,   nrow    =   nsims,  ncol    =   1)

# Simulation
for(i in 1:nsims){
  epsilon <- rnorm(n, 0, sigma) # random errors
  y <- betas[1] + betas[2]*p.black + epsilon # response
  lm.temp <- lm(y ~ p.black)
  ## extract beta-hat  
  beta.hat[i] <- coef(lm.temp)[2] 
}

Plot results

hist(beta.hat, col="gray",xlab="", main=expression(paste("Sampling Distribution of ", hat(beta)[1])))
abline(v=betas[2]) # add population parameter

Histogram showing the sampling distribution