# Veterinary Epidemiologic Research: Linear Regression Part 3 – Box-Cox and Matrix Representation

In the previous post, I forgot to show an example of Box-Cox transformation when there’s a lack of normality. The Box-Cox procedure computes values of $\lambda$ which best “normalises” the errors.

$\lambda$ value Transformed value of Y
2 $Y^2$
1 $Y$
0.5 $\sqrt{Y}$
0 $\ln{Y}$
-0.5 $\frac{1}{\sqrt{Y}}$
-1 $\frac{1}{Y}$
-2 $\frac{1}{Y^2}$

For example:

lm.wpc2 <- lm(wpc ~ vag_disch + milk120 + milk120.sq, data = daisy3)
library(MASS)
boxcox(lm.wpc2, plotit = TRUE)


The plot indicates a log transformation.

Matrix Representation
We can use a matrix representation of the regression equation. The regression equation $y_i = \beta_0 + \beta_{1}x_{1i} + \beta_{2}x_{2i} + \epsilon_i, i = 1, \dots, n$ can be written as $y = X\beta + \epsilon$ where $y = (y_1 \dots y_n)^T$, $\epsilon = (\epsilon_1 \dots \epsilon_n)^T$, $\beta = (\beta_0, \dots, \beta_2)^T$ and
$X = \begin{pmatrix} 1 & x_{11} & x_{12} \\ 1 & x_{21} & x_{22} \\ \vdots & \vdots & \vdots \\ 1 & x_{n1} & x_{n2} \end{pmatrix}$.

With the following regression:

lm.wpc3 <- lm(wpc ~ milk120 + hs100_ct, data = daisy3)
(lm.wpc3.sum <- summary(lm.wpc3))

Call:
lm(formula = wpc ~ milk120 + hs100_ct, data = daisy3)

Residuals:
Interval from wait period to conception
Min     1Q Median     3Q    Max
-71.83 -36.57 -15.90  23.86 211.18

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.884063   6.115894  12.408   <2e-16 ***
milk120     -0.001948   0.001858  -1.049    0.294
hs100_ct    18.530340   2.106920   8.795   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 50.53 on 1522 degrees of freedom
Multiple R-squared: 0.0495,	Adjusted R-squared: 0.04825
F-statistic: 39.63 on 2 and 1522 DF,  p-value: < 2.2e-16

anova(lm.wpc3)
Analysis of Variance Table

Response: wpc
Df  Sum Sq Mean Sq F value Pr(>F)
milk120      1    4865    4865  1.9053 0.1677
hs100_ct     1  197512  197512 77.3518 <2e-16 ***
Residuals 1522 3886309    2553
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


We start by making the X matrix and response $y$:

X <- cbind(1 , daisy3[, c(8, 24)])
# or alternatively: model.matrix(lm.wpc3)
y <- daisy3$wpc  Then we get the $X^{T}X$: XX <- t(X) %*% X # or alternatively: crossprod(X, X) XX 1 milk120 hs100_ct 1 1525.00000 4907834.4 -29.87473 milk120 4907834.39954 16535517546.0 -120696.45468 hs100_ct -29.87473 -120696.5 576.60900  We calculate the inverse of $X^{T}X$: XX.inv <- solve(t(X) %*% X) # or alternatively: qr.solve(XX) # or also: lm.wpc3.sum$cov.unscaled
XX.inv
1       milk120      hs100_ct
1         1.464864e-02 -4.348902e-06 -1.513557e-04
milk120  -4.348902e-06  1.351675e-09  5.761289e-08
hs100_ct -1.513557e-04  5.761289e-08  1.738495e-03


Now to get the coefficients estimates:

XX.inv %*% t(X) %*% y
# or alternatively: solve(t(X) %*% X, t(X) %*% y)
# or also: XX.inv %*% crossprod(X, y)
# or: qr.solve(X, y)
[,1]
1        75.884062540
milk120  -0.001948437
hs100_ct 18.530340385


The fitted values:

crossprod(t(X), qr.solve(X, y)


SSE and MSE;

SSE <- crossprod(y, y) - crossprod(y, yhat)
SSE
[,1]
[1,] 3886309

MSE <- SSE / (length(y) - 3)
MSE
[,1]
[1,] 2553.423


The residual standard error:

sqrt(sum(lm.wpc3$res^2) / (1525 - 3))  The standard errors of the coefficients and the $R^2$: sqrt(diag(XX.inv)) * 50.5314 1 milk120 hs100_ct 6.115893490 0.001857794 2.106920139 1 - sum(lm.wpc3$res^2) / sum((y - mean(y))^2)
[1] 0.04949679


## 2 thoughts on “Veterinary Epidemiologic Research: Linear Regression Part 3 – Box-Cox and Matrix Representation”

1. Dave X says:

Thanks for the post. I’d forgotten about box-cox.

In the y^\lambda table, the \lambda==-1 should be shown as Y^-1 rather than -1/y