# Chapter 2 Multiple Linear Regression

In this chapter we will review multiple linear regression and in particular the Ordinary Least Squares (OLS) estimator. We will further investigate the challenges which appear in the high-dimensional setting where the number of covariates is large compared to the number of observations, i.e. $$p>>n$$.

## 2.1 Notation

We will typically denote the covariates by the symbol $$X$$. If $$X$$ is a vector, its components can be accessed by subscripts $$X_j$$. The response variable will be denoted by $$Y$$. We use uppercase letters such as $$X$$, $$Y$$ when referring to the generic aspects of a variable. Observed values are written in lowercase; hence the $$i$$th observed value of $$X$$ is written as $$x_i$$ (where $$x_i$$ is again a scalar or vector). Matrices are represented by bold uppercase letters; for example, a set of $$n$$ input p-vectors $$x_i$$, $$i=1,\ldots,n$$ would be represented by the $$n\times p$$ matrix $$\bf{X}$$. All vectors are assumed to be column vectors, the $$i$$th row of $$\bf{X}$$ is $$x_i^T$$.

## 2.2 Ordinary Least Squares

Given a vector of inputs $$X=(X_1,X_2,\ldots,X_p)$$, in multiple regression we predict the output $$Y$$ via the linear model:

$\hat{Y}=\hat\beta_0+\sum_{j=1}^{p}X_j\hat\beta_j.$

The term $$\beta_0$$ is the intercept. If we include the constant variable 1 in $$X$$, include $$\hat\beta_0$$ in the vector of coefficients $$\hat\beta$$, then we can write

$\hat{Y}=X^T\hat\beta.$

How do we fit the linear model to a set of training data (i.e. how do we obtain the estimator $$\hat \beta$$)? We typically use ordinary least squares (OLS) where we pick the coefficient $$\beta$$ to minimize the residual sum of squares

$\begin{eqnarray*} \textrm{RSS}(\beta)&=&\sum_{i=1}^{n}(y_i-x_i^T\beta)^2\\ &=&(\textbf{y} - \textbf{X} \beta)^T (\textbf{y} - \textbf{X} \beta)\\ &=&\|\textbf{y} - \textbf{X} \beta\|^2_2. \end{eqnarray*}$

If the matrix $$\bf X^T \bf X$$ is nonsingular, then the solution is given by

$\hat\beta=(\bf X^T \bf X)^{-1}\bf X^T \bf y.$

Thus, the prediction at the new input point $$X_{\rm new}$$ is

\begin{align*} \hat{Y}&=\hat{f}(X_{\rm new})\\ &=X_{\rm new}^T\hat\beta\\ &=X_{\rm new}^T(\textbf{X}^T \textbf{X})^{-1}\textbf{X}^T \textbf{y}. \end{align*}

Figures 2.1 and 2.2 show two geometric representations of the OLS estimator. In Figure 2.1 the $$n$$ data points $$(y_i,x_{i1},\ldots,x_{ip})$$ randomly spread around a $$p$$-dimensional hyperplane in a $$p+1$$-dimensional space; the random spread only occurs parallel to the y-axis and the hyperplane is defined via $$\hat \beta$$. Figure 2.2 shows a different representation where the vector $$\bf y$$ is a single point in the $$n$$-dimensional space $${\bf R}^n$$; the fitted $$\hat {\bf y}$$ is the orthogonal projection onto the $$p$$-dimensional subspace of $${\bf R}^n$$ spanned by the vectors $${\bf x}_1,\ldots,{\bf x}_p$$. Figure 2.1: Data points spreading around the p-dimensional OLS hyperplane. Figure 2.2: OLS fit $$\hat{\textbf{y}}$$ as the orthogonal projection of $$\textbf{y}$$ onto subspace spanned by covariates.

## 2.3 Overfitting

Overfitting refers to the phenomenon of modelling the noise rather than the signal. In case the true model is parsimonious (few covariates driving the response $$Y$$) and data on many covariates are available, it is likely that a linear combination of all covariates yields a higher likelihood than a combination of the few that are actually related to the response. As only the few covariates related to the response contain the signal, the model involving all covariates then cannot but explain more than the signal alone: it also models the error. Hence, it overfits the data.

We illustrate overfitting by generating artificial data. We simulate $$n=10$$ training data points, take $$p=15$$ and $$X_{i1},\ldots,X_{ip}$$ i.i.d. $$N(0,1)$$. We assume that the response depends only on the first covariate, i.e. $$Y_i=\beta_1 X_{i1}+\epsilon_i$$, where $$\beta_1=2$$ and $$\epsilon_i$$ i.i.d. $$N(0,0.5^2)$$.

set.seed(1)
n <- 10
p <- 15
beta <- c(2,rep(0,p-1))

# simulate covariates
xtrain <- matrix(rnorm(n*p),n,p)
ytrain <- xtrain%*%beta+rnorm(n,sd=0.5)
dtrain <- data.frame(xtrain)
dtrain$y <- ytrain We fit a univariate linear regression model with X1 as covariate and print the summary. fit1 <- lm(y~X1,data=dtrain) summary(fit1) ## ## Call: ## lm(formula = y ~ X1, data = dtrain) ## ## Residuals: ## Min 1Q Median 3Q Max ## -0.59574 -0.41567 -0.06222 0.18490 0.97592 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -0.1002 0.1785 -0.561 0.59 ## X1 1.8070 0.2373 7.614 6.22e-05 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.5558 on 8 degrees of freedom ## Multiple R-squared: 0.8787, Adjusted R-squared: 0.8636 ## F-statistic: 57.97 on 1 and 8 DF, p-value: 6.223e-05 The coefficient for X1 is close to the true value. The R squared value $$R^2=$$ 0.88 indicates that the model fits the data well. In order to explore what happens if we add noise covariates, we re-fit the model with an increasing number of covariates, i.e. $$p=4$$, $$8$$ and $$15$$. fit4 <- lm(y~X1+X2+X3+X4,data=dtrain) fit8 <- lm(y~X1+X2+X3+X4+X5+X6+X7+X8,data=dtrain) fit15 <- lm(y~.,data=dtrain) #all 15 covariates The next plot shows the data points (black circles) together with the fitted values (red crosses). Figure 2.3: Observed and fitted values for models with increasing $$p$$. With increasing $$p$$ the fitted values start to deviate from the true model (blue line) and they move closer towards the observed data points. Finally, with $$p=15$$, the fitted values match perfectly the data, i.e. the model captures the noise and overfits the data. In line with these plots we note that the R squared values increase with $$p$$. Table 2.1: R2 for models with increasing p. model R2 p=1 0.88 p=4 0.90 p=8 0.98 p=15 1.00 The following figure shows the regression coefficients for the different models. The larger the $$p$$, the bigger the discrepancy to the true coefficients. Figure 2.4: Regression coefficients for models with increasing $$p$$. This becomes even more evident when calculating the mean squared error between the estimated and true coefficients. Table 2.2: MSE between estimated and true coefficients. mse p=1 0.02 p=4 0.04 p=8 0.84 p=15 1.63 The summary of the full model with $$p=15$$ indicates that something went wrong. summary(fit15) ## ## Call: ## lm(formula = y ~ ., data = dtrain) ## ## Residuals: ## ALL 10 residuals are 0: no residual degrees of freedom! ## ## Coefficients: (6 not defined because of singularities) ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -0.01592 NaN NaN NaN ## X1 -0.50138 NaN NaN NaN ## X2 0.81492 NaN NaN NaN ## X3 -0.56052 NaN NaN NaN ## X4 0.72667 NaN NaN NaN ## X5 1.84831 NaN NaN NaN ## X6 0.05759 NaN NaN NaN ## X7 -1.21460 NaN NaN NaN ## X8 -1.30908 NaN NaN NaN ## X9 -1.39005 NaN NaN NaN ## X10 NA NA NA NA ## X11 NA NA NA NA ## X12 NA NA NA NA ## X13 NA NA NA NA ## X14 NA NA NA NA ## X15 NA NA NA NA ## ## Residual standard error: NaN on 0 degrees of freedom ## Multiple R-squared: 1, Adjusted R-squared: NaN ## F-statistic: NaN on 9 and 0 DF, p-value: NA There is a note saying “no residual degrees of freedom”. Furthermore, many entries in the table of coefficients are not available and a note says that coefficients cannot be calculated because of singularities. What has happened? In fact, the OLS estimator as introduced above is not well defined. The design matrix $$\bf X$$ is rank deficient ($${\rm rank}({\bf X})=n< p$$) and therefore the matrix $${\bf X}^T {\bf X}$$ is singular (not invertible). We can check this by calculating the determinant. x <- model.matrix(fit15) det(t(x)%*%x) ##  -2.8449e-81 In this simulation exercise we illustrated the problem of overfitting. We have seen that the models with large $$p$$ fit the data very well, but the estimated coefficients are far off from the truth. In practice we do not know the truth. How do we know when a model is overfitting and how do we decide what a “good” model is? Shortly we will introduce the Generalization Error which will shed light on this question. We end this section with the helpful 10:1 rule: In order to avoid overfitting the number of predictors (or covariates) p should be less than n/10. This rule can be extended to binary and time-to-event endpoints. For binary endpoints we replace $$n$$ with $$\min\{n_0,n_1\}$$ and for time-to-event with $$n_{\rm events}$$. ## 2.4 Generalization Error The ultimative goal of a good model is to make good predictions for the future. That is we need to assess how the fitted model generalizes beyond the “observed” data. Conceptually, given new input data $$x_{\rm new}$$, the model provides a prediction $$\hat{Y}=\hat{f}(x_{\rm new})$$. The Generalization Error is the expected discrepancy between the prediction $$\hat{Y}=\hat{f}(x_{\rm new})$$ and the actual outcome $$Y_{\rm new}$$ ${\rm Err}(x_{\rm new})=E[(Y_{\rm new}-\hat{f}(x_{\rm new}))^2].$ One can show that this error can be decomposed into three terms $\begin{eqnarray} {\rm Err}(x_{\rm new})&=&\sigma_{\epsilon}^2+{\rm Bias}^2(\hat{f}(x_{\rm new})) + {\rm Var}(\hat{f}(x_{\rm new})), \end{eqnarray}$ where the first term is the irreducible error (or “noise”), the second term describes the systematic bias from the truth and the third term is the variance of the predictive model. For linear regression the expected variance can be approximated by $$\sigma^2_{\epsilon} \frac{p}{N}$$. Complex models (with large number of covariates) have typically a small bias but a large variance. Therefore the equation above is referred to as the bias-variance dilemma as it describes the conflict in trying simultaneously minimize both sources of error, bias and variance. How do we calculate the Generalization Error in practice? The most simple approach is to separate the data into a training and testing set (see Figure 2.5). The model is fitted (or “trained”) on the training data and the Generalization Error is calculated on the test data and quantified using the root-mean-square error (RMSE) ${\rm RMSE}= \sqrt{\frac{\sum_{i=1}^{n_{\rm test}}(y_{\rm test,i}-\hat y_{i})^2}{n_{\rm test}}}.$ Figure 2.5: Splitting the data into training set and test sets. We illustrate this based on the dummy data. First we simulate test data. # simulate test data xtest <- matrix(rnorm(n*p),n,p) ytest <- xtest%*%beta+rnorm(n,sd=0.5) dtest <- data.frame(xtest) dtest$y <- ytest

Next, we take the fitted models and make predictions on the test data

# prediction
pred1 <- predict(fit1,newdata = dtest)
pred4 <- predict(fit4,newdata = dtest)
pred8 <- predict(fit8,newdata = dtest)
pred15 <- predict(fit15,newdata = dtest)

and we calculate the RMSE.

# rmse
rmse <- data.frame(
RMSE(pred1,ytest),RMSE(pred4,ytest),
RMSE(pred8,ytest),RMSE(pred15,ytest)
)
colnames(rmse) <- paste0("p=",c(1,4,8,15))
rownames(rmse) <- "RMSE"
kable(rmse,digits=2,booktabs=TRUE,
caption="RMSE for models with increasing p.")
Table 2.3: RMSE for models with increasing p.
p=1 p=4 p=8 p=15
RMSE 0.54 0.63 3.28 3.7

The models with $$p=1$$ and $$4$$ achieve a good error close to the “irreducible” $$\sigma_{\epsilon}$$. On the other hand the predictions obtained with $$p=8$$ and $$15$$ are very poor (RMSEs are 6 to 8-fold larger).