12 Day 12 (June 18)

12.1 Announcements

  • Questions about assignment 3
  • Assignment 3 is graded
  • Read Ch. 2 in linear models with R (estimation)
  • Read Ch. 3 in linear models with R (inference)

12.2 Maximum Likelihood Estimation

12.3 Confidence intervals for paramters

  • Example

    y <- c(63, 68, 61, 44, 103, 90, 107, 105, 76, 46, 60, 66, 58, 39, 64, 29, 37,
27, 38, 14, 38, 52, 84, 112, 112, 97, 131, 168, 70, 91, 52, 33, 33, 27,
18, 14, 5, 22, 31, 23, 14, 18, 23, 27, 44, 18, 19)
year <- 1965:2011
df <- data.frame(y = y, year = year)
plot(x = df$year, y = df$y, xlab = "Year", ylab = "Annual count", main = "",
col = "brown", pch = 20, xlim = c(1965, 2040))

    • Is the population size really decreasing?
    • Write out the model we should use answer this question
    • How can we assess the uncertainty in our estimate of \(\beta_1\)?
    • Confidence intervals in R
    m1 <- lm(y~year,data=df)
coef(m1)
    ## (Intercept)        year 
##  2356.48797    -1.15784
    confint(m1,level=0.95)    
    ##                 2.5 %       97.5 %
## (Intercept) 929.80699 3783.1689540
## year         -1.87547   -0.4402103
    • Note \[\hat{\boldsymbol{\beta}}\sim\text{N}(\boldsymbol{\beta},\sigma^2(\mathbf{X}^{\prime}\mathbf{X})^{-1})\] and let \[\hat{\beta_1}\sim\text{N}(\beta_1,\sigma^{2}_{\beta_1})\] where \(\sigma^{2}_{\beta_1}= \sigma^2\mathbf{c}^{\prime}(\mathbf{X}^{\prime}\mathbf{X})^{-1}\mathbf{c}\) and \(\mathbf{c}\equiv(0,1,0,0,\ldots,0)^{\prime}\). In R we can extract \(\sigma^2(\mathbf{X}^{\prime}\mathbf{X})^{-1}\) using vcov().
    vcov(m1)  
    ##             (Intercept)         year
## (Intercept) 501753.2825 -252.3792372
## year          -252.3792    0.1269513

    Note that

    diag(vcov(m1))^0.5
    ## (Intercept)        year 
## 708.3454542   0.3563023

    corresponds to the column Std. Error from summary()

    summary(m1)
    ## 
## Call:
## lm(formula = y ~ year, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -45.333 -20.597  -9.754  14.035 117.929 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 2356.4880   708.3455   3.327  0.00176 **
## year          -1.1578     0.3563  -3.250  0.00219 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.13 on 45 degrees of freedom
## Multiple R-squared:  0.1901, Adjusted R-squared:  0.1721 
## F-statistic: 10.56 on 1 and 45 DF,  p-value: 0.00219
    • When \(\sigma_{\beta_1}\) is known \[\hat{\beta_1}\sim\text{N}(\beta_1,\sigma^{2}_{\beta_1})\] \[\hat{\beta_1} - \beta_1 \sim\text{N}(0,\sigma^{2}_{\beta_1})\] \[\frac{\hat{\beta_1} - \beta_1}{\sigma_{\beta_1}} \sim\text{N}(0,1)\]
    • When \(\sigma_{\beta_1}\) is estimated \[\frac{\hat{\beta_1} - \beta_1}{\hat{\sigma}_{\beta_1}} \sim\text{t}(\nu)\] where \(\nu = n - p\)
    • Deriving the confidence interval for \(\hat{\beta_1}\) \[\text{P}(a < \frac{\hat{\beta_1} - \beta_1}{\hat{\sigma}_{\beta_1}} < b) = 1-\alpha\] \[\text{P}(a\hat{\sigma}_{\beta_1} < \hat{\beta_1} - \beta_1 < b\hat{\sigma}_{\beta_1}) = 1-\alpha\] \[\text{P}(-\hat{\beta_1} + a\hat{\sigma}_{\beta_1} < - \beta_1 < -\hat{\beta_1} + b\hat{\sigma}_{\beta_1}) = 1-\alpha\] \[\text{P}(\hat{\beta_1} - b\hat{\sigma}_{\beta_1} < \beta_1 < \hat{\beta_1} - a\hat{\sigma}_{\beta_1}) = 1-\alpha\]
    • What is \(\text{P}(a < \frac{\hat{\beta_1} - \beta_1}{\hat{\sigma}_{\beta_1}} < b) = 1-\alpha\)? \[\text{P}(a < z < b) = \int_{a}^{b}[z|\nu]dz\]
    • “By hand” in R
    nu <- length(df$y) - 2
a <- qt(p = 0.025,df = nu)
a
    ## [1] -2.014103
    b <- qt(p = 0.975,df = nu)
b
    ## [1] 2.014103
    beta1.hat <- coef(m1)[2]
sigma.beta1.hat <- (diag(vcov(m1))^0.5)[2]

beta1.hat - b*sigma.beta1.hat
    ##     year 
## -1.87547
    beta1.hat - a*sigma.beta1.hat
    ##       year 
## -0.4402103
    confint(m1)[2,]
    ##      2.5 %     97.5 % 
## -1.8754696 -0.4402103