3 Day 3 (June 7)

3.1 Announcements

  • Meet-and-greet tomorrow

  • A comment about email help!

  • If office hours times don’t work for you let me know

  • Recommended reading

    • Chapters 1 and 2 (pgs 1 - 28) in Linear Models with R
    • Chapter 2 in Applied Regression and ANOVA Using SAS

3.2 Intro to statistical modelling

  • Quick review of major ideas from Day 2 notes

  • Example: disease introduction

3.3 Matrix algebra

  • Column vectors
    • \(\mathbf{y}\equiv(y_{1},y_{2},\ldots,y_{n})^{'}\)
    • \(\mathbf{x}\equiv(x_{1},x_{2},\ldots,x_{n})^{'}\)
    • \(\boldsymbol{\beta}\equiv(\beta_{1},\beta_{2},\ldots,\beta_{p})^{'}\)
    • \(\boldsymbol{1}\equiv(1,1,\ldots,1)^{'}\)
    • In R
    y <- matrix(c(1,2,3),nrow=3,ncol=1)
y
    ##      [,1]
## [1,]    1
## [2,]    2
## [3,]    3
  • Matrices
    • \(\mathbf{X}\equiv(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{p})\)
    • In R
    X <- matrix(c(1,2,3,4,5,6),nrow=3,ncol=2,byrow=FALSE)
X
    ##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6
  • Vector multiplication
    • \(\mathbf{y}^{'}\mathbf{y}\)
    • \(\mathbf{1}^{'}\mathbf{1}\)
    • \(\mathbf{1}\mathbf{1}^{'}\)
    • In R
    t(y)%*%y    
    ##      [,1]
## [1,]   14
  • Matrix by vector multiplication
    • \(\mathbf{X}^{'}\mathbf{y}\)
    • In R
    t(X)%*%y
    ##      [,1]
## [1,]   14
## [2,]   32
  • Matrix by matrix multiplication
    • \(\mathbf{X}^{'}\mathbf{X}\)
    • In R
    t(X)%*%X
    ##      [,1] [,2]
## [1,]   14   32
## [2,]   32   77
  • Matrix inversion
    • \((\mathbf{X}^{'}\mathbf{X})^{-1}\)
    • In R
    solve(t(X)%*%X)
    ##            [,1]       [,2]
## [1,]  1.4259259 -0.5925926
## [2,] -0.5925926  0.2592593
  • Determinant of a matrix
    • \(|\mathbf{I}|\)
    • In R
    I <- diag(1,3)
I
    ##      [,1] [,2] [,3]
## [1,]    1    0    0
## [2,]    0    1    0
## [3,]    0    0    1
    det(I)
    ## [1] 1
  • Quadratic form
    • \(\mathbf{y}^{'}\mathbf{S}\mathbf{y}\)
  • Derivative of a quadratic form (Note \(\mathbf{S}\) is a symmetric matrix; e.g., \(\mathbf{X}^{'}\mathbf{X}\))
    • \(\frac{\partial}{\partial\mathbf{y}}\mathbf{y^{'}\mathbf{S}\mathbf{y}}=2\mathbf{S}\mathbf{y}\)
  • Other useful derivatives
    • \(\frac{\partial}{\partial\mathbf{y}}\mathbf{\mathbf{x^{'}}\mathbf{y}}=\mathbf{x}\)
    • \(\frac{\partial}{\partial\mathbf{y}}\mathbf{\mathbf{X^{'}}\mathbf{y}}=\mathbf{X}\)

3.4 Introduction to linear models

  • What is a model?

  • What is a linear model?

    • Most widely used model in science, engineering, and statistics

    • Vector form: \(\mathbf{y}=\beta_{0}+\beta_{1}\mathbf{x}_{1}+\beta_{2}\mathbf{x}_{2}+\ldots+\beta_{p}\mathbf{x}_{p}+\boldsymbol{\varepsilon}\)

    • Matrix form: \(\mathbf{y}=\mathbf{X}\boldsymbol{\beta}+\boldsymbol{\varepsilon}\)

    • Which part of the model is the mathematical model

    • Which part of the model makes the linear model a “statistical” model

    • Visual

  • Which of the four below are a linear model \[\mathbf{y}=\beta_{0}+\beta_{1}\mathbf{x}_{1}+\beta_{2}\mathbf{x}^{2}_{1}+\boldsymbol{\varepsilon}\] \[\mathbf{y}=\beta_{0}+\beta_{1}\mathbf{x}_{1}+\beta_{2}\text{log(}\mathbf{x}_{1}\text{)}+\boldsymbol{\varepsilon}\] \[\mathbf{y}=\beta_{0}+\beta_{1}e^{\beta_{2}\mathbf{x}_{1}}+\boldsymbol{\varepsilon}\] \[\mathbf{y}=\beta_{0}+\beta_{1}\mathbf{x}_{1}+\text{log(}\beta_{2}\text{)}\mathbf{x}_{1}+\boldsymbol{\varepsilon}\]

  • Why study the linear model?

    • Building block for more complex models (e.g., GLMs, mixed models, machine learning, etc)
    • We know the most about it

3.5 Estimation

  • Three options to estimate \(\boldsymbol{\beta}\)
    • Minimize a loss function
    • Maximize a likelihood function
    • Find the posterior distribution
    • Each option requires different assumptions

3.6 Loss function approach

  • Define a measure of discrepancy between the data and the mathematical model

    • Find the values of \(\boldsymbol{\beta}\) that make \(\mathbf{X}\boldsymbol{\beta}\) “closest” to \(\mathbf{y}\)
    • Visual

  • Classic example \[\underset{\boldsymbol{\beta}}{\operatorname{argmin}}\sum_{i=1}^{n}(y_i-\mathbf{x}_{i}^{\prime}\boldsymbol{\beta})^2\] or in matrix form \[\underset{\boldsymbol{\beta}}{\operatorname{argmin}}(\mathbf{y} - \mathbf{X}\boldsymbol{\beta})^{\prime}(\mathbf{y} - \mathbf{X}\boldsymbol{\beta})\] which results in \[\hat{\boldsymbol{\beta}}=(\mathbf{X}^{\prime}\mathbf{X})^{-1}\mathbf{X}^{\prime}\mathbf{y}\] -In R

    y <- c(0.16,2.82,2.24)
X <- matrix(c(1,1,1,1,2,3),nrow=3,ncol=2,byrow=FALSE)

solve(t(X)%*%X)%*%t(X)%*%y
    ##       [,1]
## [1,] -0.34
## [2,]  1.04
    optim(par=c(0,0),method = c("Nelder-Mead"),fn=function(beta){t(y-X%*%beta)%*%(y-X%*%beta)})
    ## $par
## [1] -0.3399977  1.0399687
## 
## $value
## [1] 1.7496
## 
## $counts
## function gradient 
##       61       NA 
## 
## $convergence
## [1] 0
## 
## $message
## NULL
    lm(y~X-1)
    ## 
## Call:
## lm(formula = y ~ X - 1)
## 
## Coefficients:
##    X1     X2  
## -0.34   1.04