Diagnostics – again

Different types of residuals

Influence

Outlier detection

Residual plots:

partial regression (added variable) plot,

partial residual (residual plus component) plot.

The dataset we will use is based on record times on Scottish hill races.

In [2]:

```
make_table(Description)
apply_theme('basic')
```

Out[2]:

In [3]:

```
%%R
url = 'http://www.statsci.org/data/general/hills.txt'
races.table = read.table(url, header=TRUE, sep='\t')
head(races.table)
```

As we'd expect, the time increases both with `Distance`

and `Climb`

.

In [4]:

```
%%R -w 800 -h 800
plot(races.table[,2:4], pch=23, bg='orange', cex=2)
```

Let's look at our multiple regression model.

In [5]:

```
%%R
races.lm = lm(Time ~ Distance + Climb, data=races.table)
summary(races.lm)
```

But is this a good model?

What can go wrong?

Regression function can be wrong: maybe regression function should have some other form (see diagnostics for simple linear regression).

Model for the errors may be incorrect:

may not be normally distributed.

may not be independent.

may not have the same variance.

Detecting problems is more

*art*then*science*, i.e. we cannot*test*for all possible problems in a regression model.Basic idea of diagnostic measures: if model is correct then residuals $e_i = Y_i -\widehat{Y}_i, 1 \leq i \leq n$ should look like a sample of (not quite independent) $N(0, \sigma^2)$ random variables.

`R`

produces a set of standard plots for `lm`

that help us assess whether our assumptions are reasonable or not. We will go through each in some, but not too much, detail.

As we see below, there are some quantities which we need to define in order to read these plots. We will define these first.

In [6]:

```
%%R -h 800 -w 800
par(mfrow=c(2,2))
plot(races.lm, pch=23 ,bg='orange',cex=2)
```

Possible problems & diagnostic checks

Errors may not be normally distributed or may not have the same variance – qqnorm can help with this. This may not be too important in large samples.

Variance may not be constant. Can also be addressed in a plot of $\pmb{X}$ vs. $\pmb{e}$ :

*fan shape*or other trend indicate non-constant variance.Influential observations. Which points “affect” the regression line the most?

Outliers: points where the model really does not fit! Possibly mistakes in data transcription, lab errors, who knows? Should be recognized and (hopefully) explained.

Errors may not be normally distributed or may not have the same variance – qqnorm can help with this. This may not be too important in large samples.

Variance may not be constant. Can also be addressed in a plot of ${X}$ vs. ${e}$:

*fan shape*or other trend indicate non-constant variance. One of`R`

's plots is $\hat{Y}$ vs $e=Y-\hat{Y}$.Influential observations. Which points “affect” the regression line the most?

Outliers: points where the model really does not fit! Possibly mistakes in data transcription, lab errors, who knows? Should be recognized and (hopefully) explained.

Ordinary residuals: $e_i = Y_i - \widehat{Y}_i$. These measure the deviation of predicted value from observed value, but their distribution depends on unknown scale, $\sigma$.

Internally studentized residuals (

`rstandard`

in R): $$r_i = e_i / s(e_i) = e_i / \widehat{\sigma} \sqrt{1 - H_{ii}}$$,Above, $H$ is the “hat” matrix. These are almost $t$-distributed, except $\widehat{\sigma}$ depends on $e_i$.

Externally studentized residuals (

`rstudent`

in R): $$t_i = e_i / \widehat{\sigma_{(i)}} \sqrt{1 - H_{ii}} \sim t_{n-p-2}.$$ These are exactly $t$ distributed so we know their distribution and can use them for tests, if desired.Numerically, these residuals are highly correlated, as we would expect.

In [7]:

```
%%R -h 800 -w 800
plot(resid(races.lm), rstudent(races.lm), pch=23, bg='blue', cex=3)
```

In [8]:

```
%%R -h 800 -w 800
plot(rstandard(races.lm), rstudent(races.lm), pch=23, bg='blue', cex=3)
```

The first plot is the quantile plot for the residuals, that compares their distribution to that of a sample of independent normals.

In [9]:

```
%%R -h 800 -w 800
qqnorm(rstandard(races.lm), pch=23, bg='red', cex=2)
```

In [10]:

```
%%R -h 800 -w 800
plot(fitted(races.lm), sqrt(abs(rstandard(races.lm))), pch=23, bg='red', cex=2)
```

In [11]:

```
%%R -h 800 -w 800
plot(fitted(races.lm), resid(races.lm), pch=23, bg='red', cex=2)
```

Other plots provide an assessment of the `influence`

of each observation.
Usually, this is done by dropping an entire case $(y_i, x_i)$ from the dataset and
refitting the model.

In this setting, a $\cdot_{(i)}$ indicates $i$-th observation was not used in fitting the model.

For example: $\widehat{Y}_{j(i)}$ is the regression function evaluated at the $j$-th observations predictors BUT the coefficients $(\widehat{\beta}_{0(i)}, \dots, \widehat{\beta}_{p(i)})$ were fit after deleting $i$-th case from the data.

Idea: if $\widehat{Y}_{j(i)}$ is very different than $\widehat{Y}_j$ (using all the data) then $i$ is an influential point, at least for estimating the regression function at $(X_{1,j}, \dots, X_{p,j})$.

There are various standard measures of influence.

$$DFFITS_i = \frac{\widehat{Y}_i - \widehat{Y}_{i(i)}}{\widehat{\sigma}_{(i)} \sqrt{H_{ii}}}$$

This quantity measures how much the regression function changes at the $i$-th case / observation when the $i$-th case / observation is deleted.

For small/medium datasets: value of 1 or greater is “suspicious”. For large dataset: value of $2 \sqrt{(p+1)/n}$.

`R`

has its own standard rules similar to the above for marking an observation as influential.

In [12]:

```
%%R -h 800 -w 800
plot(dffits(races.lm), pch=23, bg='orange', cex=2, ylab="DFFITS")
```

It seems that some observations had a high influence measured by $DFFITS$:

In [13]:

```
%%R
races.table[which(dffits(races.lm) > 0.5),]
```

`Knock Hill`

? We'll come back to this later.

Cook’s distance measures how much the entire regression function changes when the $i$-th case is deleted.

$$D_i = \frac{\sum_{j=1}^n(\widehat{Y}_j - \widehat{Y}_{j(i)})^2}{(p+1) \, \widehat{\sigma}^2}$$

Should be comparable to $F_{p+1,n-p-1}$: if the “$p$-value” of $D_i$ is 50 percent or more, then the $i$-th case is likely influential: investigate further.

Again,

`R`

has its own rules similar to the above for marking an observation as influential.What to do after investigation? No easy answer.

In [14]:

```
%%R -h 800 -w 800
plot(cooks.distance(races.lm), pch=23, bg='orange', cex=2, ylab="Cook's distance")
```

In [15]:

```
%%R
races.table[which(cooks.distance(races.lm) > 0.1),]
```

This quantity measures how much the coefficients change when the $i$-th case is deleted.

- $$DFBETAS_{j(i)} = \frac{\widehat{\beta}_j - \widehat{\beta}_{j(i)}}{\sqrt{\widehat{\sigma}^2_{(i)} (X^TX)^{-1}_{jj}}}.$$

- For small/medium datasets: absolute value of 1 or greater is “suspicious”. For large dataset: absolute value of $2 / \sqrt{n}$.

In [16]:

```
%%R -h 800 -w 800
plot(dfbetas(races.lm)[,'Climb'], pch=23, bg='orange', cex=2, ylab="DFBETA (Climb)")
races.table[which(abs(dfbetas(races.lm)[,'Climb']) > 1),]
```

In [17]:

```
%%R -h 800 -w 800
plot(dfbetas(races.lm)[,'Distance'], pch=23, bg='orange', cex=2, ylab="DFBETA (Climb)")
races.table[which(abs(dfbetas(races.lm)[,'Distance']) > 0.5),]
```

The essential definition of an *outlier* is an observation pair $(Y, X_1, \dots, X_p)$ that does not follow the model, while most other observations seem to follow the model.

Outlier in

*predictors*: the $X$ values of the observation may lie outside the “cloud” of other $X$ values. This means you may be extrapolating your model inappropriately. The values $H_{ii}$ can be used to measure how “outlying” the $X$ values are.Outlier in

*response*: the $Y$ value of the observation may lie very far from the fitted model. If the studentized residuals are large: observation may be an outlier.The races at

`Bens of Jura`

and`Lairig Ghru`

seem to be outliers in*predictors*as they were the highest and longest races, respectively.How can we tell if the

`Knock Hill`

result is an outlier? It seems to have taken much longer than it should have so maybe it is an outlier in the*response*.

One way to detect outliers in the *predictors*, besides just looking at the actual values themselves, is through their leverage values, defined by
$$
\text{leverage}_i = H_{ii} = (X(X^TX)^{-1}X^T)_ii.
$$

Not surprisingly, our longest and highest courses show up again. This at least reassures us that the leverage is capturing some of this "outlying in $X$ space".

In [18]:

```
%%R
plot(hatvalues(races.lm), pch=23, bg='orange', cex=2, ylab='Hat values')
races.table[which(hatvalues(races.lm) > 0.3),]
```

We will consider a crude outlier test that tries to find residuals that are "larger" than they should be.

Since

`rstudent`

are $t$ distributed, we could just compare them to the $T$ distribution and reject if their absolute value is too large.Doing this for every observation results in $n$ different hypothesis tests.

This causes a problem: if $n$ is large, if we “threshold” at $t_{1-\alpha/2, n-p-2}$ we will get many outliers by chance even if model is correct.

In fact, we expect to see $n \cdot \alpha$ “outliers” by this test. Every large data set would have outliers in it, even if model was entirely correct!

Let's sample some data from our model to convince ourselves that this is a real problem.

In [19]:

```
%%R
X = rnorm(100)
Y = 2 * X + 0.5 + rnorm(100)
cutoff = qt(0.95, 97)
sum(abs(rstudent(lm(Y~X))) > cutoff)
```

In [20]:

```
%%R
X = rnorm(100)
Y = 2 * X + 0.5 + rnorm(100)
cutoff = qt(0.95, 97)
sum(abs(rstudent(lm(Y~X))) > cutoff)
```

This problem we identified is known as

*multiple comparisons*or*simultaneous inference.*We are performing $n$ hypothesis tests, but would still like to control the probability of making*any*false positive errors.One solution: Bonferroni correction, threshold at $t_{1 - \alpha/(2*n), n-p-2}$.

Dividing $\alpha$ by $n$, the number of tests, is known as a

*Bonferroni*correction.If we are doing many $t$ (or other) tests, say $m \gg 1$ we can control overall false positive rate at $\alpha$ by testing each one at level $\alpha/m$.

**Proof:**when the model is correct, with studentized residuals $T_i$:$$\begin{aligned} P\left( \text{at least one false positive} \right) & = P \left(\cup_{i=1}^m |T_i| \geq t_{1 - \alpha/(2*m), n-p-2} \right) \\ & \leq \sum_{i=1}^m P \left( |T_i| \geq t_{1 - \alpha/(2*m), n-p-2} \right) \\ & = \sum_{i=1}^m \frac{\alpha}{m} = \alpha. \\ \end{aligned}$$

Let's apply this to our data. It turns out that `KnockHill`

is a known error.

In [21]:

```
%%R
n = nrow(races.table)
cutoff = qt(1 - 0.05 / (2*n), (n -4))
races.table[which(abs(rstudent(races.lm)) > cutoff),]
```

The package `car`

has a built in function to do this test.

In [22]:

```
%%R
library(car)
outlierTest(races.lm)
```

The last plot that `R`

produces is a plot of residuals against leverage. Points that have
high leverage and large residuals are particularly influential.

In [23]:

```
%%R -h 800 -w 800
plot(hatvalues(races.lm), rstandard(races.lm), pch=23, bg='red', cex=2)
```

`R`

will put the IDs of cases that seem to be influential in these (and other plots). Not surprisingly, we see our usual three suspects.

In [24]:

```
%%R -h 800 -w 800
plot(races.lm, which=5)
```

As mentioned above, `R`

has its own rules for flagging points as being influential. To
see a summary of these, one can use the `influence.measures`

function.

In [25]:

```
%%R
influence.measures(races.lm)
```

While not specified in the documentation, the meaning of the asterisks can be found
by reading the code. The function `is.influential`

makes the decisions
to flag cases as influential or not.

We see that the

`DFBETAS`

are thresholded at 1.We see that

`DFFITS`

is thresholded at`3 * sqrt((p+1)/(n-p-1))`

.Etc.

In [26]:

```
%%R
influence.measures
```

True regression function may have higher-order non-linear terms, polynomial or otherwise.

We may be missing terms involving more than one $\pmb{X}_{(\cdot)}$, i.e. $\pmb{X}_i \cdot \pmb{X}_j$ (called an

*interaction*).Some simple plots:

*added-variable*and*component plus residual*plots can help to find nonlinear functions of*one variable*.I find these plots of somewhat limited use in practice, but we will go over them as possibly useful diagnostic tools.

The plots can be helpful for finding influential points, outliers. The functions can be found in the

`car`

package.Procedure:

Let $\tilde{e}_{X_j,i}, 1\leq i \leq n$ be the residuals after regressing $X_j$ onto all columns of $X$ except $X_j$;

Let $e_{X_j,i}$ be the residuals after regressing ${Y}$ onto all columns of ${X}$ except ${X}_j$;

Plot $\tilde{e}_{X_j}$ against $e_{X_j}$.

If the (partial regression) relationship is linear this plot should look linear.

In [27]:

```
%%R -h 800 -w 800
av.plots(races.lm, 'Distance')
```

In [28]:

```
%%R -h 800 -w 800
av.plots(races.lm, 'Climb')
```

Similar to added variable, but may be more helpful in identifying nonlinear relationships.

Procedure: plot $X_{ij}, 1 \leq i \leq n$ vs. $e_i + \widehat{\beta}_j \cdot X_{ij} , 1 \leq i \leq n$.

The green line is a non-parametric smooth of the scatter plot that may suggest relationships other than linear.

In [29]:

```
%%R -h 800 -w 800
cr.plots(races.lm, 'Distance')
```

In [30]:

```
%%R -h 800 -w 800
cr.plots(races.lm, 'Climb')
```