An **influential observation** dramatically changes the
fitted model based on whether it is included or excluded from the
analysis.

`library(api2lm)`

Influential observations are often extreme with respect to their response values or their associated predictors.

- An
**outlier**is an observation with a large residual.- The response is relatively far from its fitted value.

- A
**leverage point**is an observation whose predictor values are extreme relative to most other observations.

`home_sales`

dataTo facilitate our discussion we will use the `home_sales`

data set in the **api2lm** package, which contains
information about homes sold in King County, WA between May 2014 and May
2015. The data set is a data frame with 216 rows and 8 columns:

`price`

: sale price (log10 units).`bedrooms`

: number of bedrooms.`bathrooms`

: number of bathrooms.`sqft_living`

: size of living space.`sqft_lot`

: size of property.`floors`

: number of floors.`waterfront`

: a factor variable with levels`"yes"`

and`"no"`

that indicate whether a home as a waterfront view.`condition`

: a factor variable indicating the condition of a house with levels ranging from`"poor"`

to`"very good"`

.

We load the data below.

`data(home_sales, package = "api2lm")`

The validity of the assumptions we want to check depend on the model fit to the data.

We will regress the `price`

variable on all of the
remaining variables in `home_sales`

.

```
<- lm(price ~ ., data = home_sales)
lmod coef(lmod)
```

```
## (Intercept) bedrooms bathrooms sqft_living
## 5.377543e+00 2.604201e-03 3.149792e-02 9.987617e-05
## sqft_lot floors waterfrontyes conditionfair
## -8.773790e-07 8.630242e-02 3.152404e-01 -3.128967e-01
## conditionaverage conditiongood conditionvery_good
## -1.278938e-01 -9.122793e-02 -2.185523e-02
```

We describe two common approaches for identifying outliers.

The simplest approach for identifying outliers is to use an index plot of the studentized residuals to determine the observations with extreme residuals.

- An index plot is a plot of a set of statistics versus their observation number.
- We will create a scatter plot of the pairs \((i, t_i)\) for \(i=1,2,\ldots,n\).

The other approach is to compare the set of studentized residuals to the appropriate quantile of a \(t_{n-p-1}\) distribution.

To evaluate whether a *single* observation is an outlier, we
compare its studentized residual to \(t^{\alpha/2}_{n-p-1}\), i.e., the \(1-\alpha/2\) quantile of a \(t\) distribution with \(n-p-1\) degrees of freedom.

- If \(|t_i| > t^{\alpha/2}_{n-p-1}\), then observation \(i\) is an outlier.
- We won’t do this because we have more than one observation to evaluate.

To identify the outliers from the \(n\) observations of our fitted model, we should adjust the previous idea using the Bonferroni correction to address the multiple comparisons problem.

- We conclude observation \(i\) is an outlier if \(|t_i| > t^{\alpha/2n}_{n-p-1}\).

**Outlier example**

The `outlier_plot`

function is a convenient way to create
an index plot of the studentized residuals from a fitted model.

`outlier_plot`

has four main arguments that we need to
know about:

`model`

: the fitted model.`id_n`

: the number of points to identify with labels.`add_reference`

: a logical value indicating whether the Bonferroni-corrected \(t\) quantiles (\(t^{1-\alpha/2n}_{n-p-1}\) and \(t^{\alpha/2n}_{n-p-1}\)) are added as reference lines to the plot. The default is`TRUE`

.`alpha`

: the \(\alpha\) value used for the \(t\) quantiles. The default is`0.05`

.

We use the `outlier_plot`

function to identify the 2 most
extreme outliers for our fitted model. Observations 214 and 33 have the
most extreme studentized residuals and observation 214 is more extreme
than the Bonferroni-adjusted \(t\)
quantile.

`outlier_plot(lmod, id_n = 2)`

The `outlier_test`

function can be used to identify the
observations with studentized residuals more extreme than the
appropriate Bonferroni-corrected \(t\)
quantiles. The main arguments to the `outlier_test`

function
are:

`model`

: the fitted model.`n`

: the number of outliers to return (by default, all of them).`alpha`

: the desired familywise error rate for the family of \(n\) tests. The default is`alpha = 0.05`

.

Running `outlier_test`

on a fitted model will return the
identified outliers and their Bonferroni-adjusted p-values
(`adj_pvalue`

). If the adjusted p-value is less than
`alpha`

, then the observation is declared an outlier.

We use `outlier_test`

to see that observation 214 is an
outlier according to this test.

`outlier_test(lmod)`

```
## stat pvalue adj_pvalue
## 214 -3.948387 5.414913e-05 0.01169621
```

The **leverage values** are the diagonal elements of the
hat matrix \(\mathbf{H}=\mathbf{X}(\mathbf{X}^T
\mathbf{X})^{-1} \mathbf{X}^T\). The \(i\)th leverage value is given by \(h_i=\mathbf{H}_{i,i}\), the \(i\)th diagonal position of the hat
matrix.

We can extract the leverage values of a fitted model using the
`hatvalues`

function.

An observation is declared to be a leverage point if its leverage value is unusually large.

Kutner et al. (2005) suggest two different thresholds for identifying a leverage point.

- Observation \(i\) is declared to be
an outlier if \(h_i > 2p/n\).
- This approach can be overly sensitive.

- Observation \(i\) is declared to be an outlier if \(h_i > 0.5\).

**Leverage example**

We can create an index plot of the leverage values of a fitted model
using the `leverage_plot`

function.

The `leverage_plot`

function takes a few main
arguments:

`model`

: the fitted model.`id_n`

: the number of points to identify with labels.`ttype`

: the threshold type. The default is`"half"`

, which uses a reference threshold of`0.5`

. Alternatively, we can choose`"2mean"`

, which uses a reference threshold of \(2p/n\).

We use the `leverage_plot`

function to identify leverage
points for the model fit to the `home_sales`

data.

`leverage_plot(lmod)`

Observations 33 and 179 are leverage points with leverage values greater than 0.5. Observation 214 has a leverage value nearly at 0.5, so we should consider it a leverage point.

An **influential observation** dramatically changes the
fitted model based on whether it is included or excluded from the
analysis.

We will now discuss several other measures of influence.

The **DFBETA** measure of influence is an \(n\times p\) matrix of statistics that
measures the change in the estimated regression coefficients when we fit
the model using all \(n\) observations
versus when we fit the model leaving one observation out at a time.

The \(i\)th row of DFBETA is

\[ \text{DFBETA}_i = \hat{\boldsymbol{\beta}} - \hat{\boldsymbol{\beta}}_{(i)}. \]

- \(\hat{\boldsymbol{\beta}} = [\hat{\beta}_0, \hat{\beta}_1, \ldots, \hat{\beta}_{p-1}]\) is the vector of estimated regression coefficients when using all \(n\) observations.
- \(\hat{\boldsymbol{\beta}}_{(i)} = [\hat{\beta}_{0(i)}, \hat{\beta}_{1(i)}, \ldots, \hat{\beta}_{p-1(i)}]\) is the vector of estimated regression coefficients obtained from the model fit using all \(n\) observations except the \(i\)th observation.
- \(\text{DFBETA}_{ij} = \hat{\beta}_j - \hat{\beta}_{j(i)}\).

Identifying influential observations using the DFBETA matrix can be difficult because the sampling variance associated with each coefficient can be very different.

The **DFBETAS** matrix is a transformation of the DFBETA
matrix that has been scaled so that the individual statistics have
similar sampling variability.

DFBETAS is also an \(n\times p\) matrix of statistics with the \(j\)th element of the \(i\)th row being defined as

\[\text{DFBETAS}_{i,j} = \frac{\hat{\beta}_{j-1} - \hat{\beta}_{j-1(i)}}{\hat{\mathrm{se}}(\hat{\beta}_{j-1})},\quad i=1,2,\ldots,n, \quad j=0,1,\ldots,p-1.\]

A DFBETAS statistic less than -1 or greater than 1 is often used as an indicator that the associated observation is influential in determining the fitted model.

One way of identifying influential observations is to create index plots the DFBETA or DFBETAs values for each regression coefficient and determine the estimates that substantially change when observation \(i\) is excluded from analysis.

- We recommend using index plots of the DFFBETAS values instead of the DFBETA values because the plots will have more similar scales and are easier to work with.

The DFBETA and DFBETAS matrices can be obtained by using the
`dfbeta`

and `dfbetas`

functions,
respectively.

**DFBETAS example**

We can use the `dfbetas_plot`

function to get index plots
of the DFBETAS statistics for each regression coefficient.

Some of the main arguments to the `dfbetas_plot`

function
are:

`model`

: the fitted model.`id_n`

: the number of observations for which to print an associated label.`regressors`

: a formula indicating the regressors for which we want to plot the DFBETAS statistics. By default, index plots are created for all regressors.

Reference lines are automatically added at \(\pm 1\) to indicate observations whose DFBETAS values are particularly extreme.

We plot the `bedrooms`

, `bathrooms`

,
`sqft_living`

, and `sqft_lot`

variables for the
fitted model of our `home_sales`

data and label the two most
extreme observations.

```
dfbetas_plot(lmod, id_n = 2,
regressors = ~ bedrooms + bathrooms + sqft_living + sqft_lot)
```

We can see from these plots that:

- The estimated coefficient for
`bedrooms`

changes by more than 3 standard errors based on whether observation 179 is used in fitting the model. - The estimated coefficients for
`sqft_living`

and`sqft_lot`

change by more than 1 standard error based on whether observations 33 or 214 are used in fitting the model.

Welsch and Kuh (1977) proposed measuring an observation’s influence through the DFFITS statistic, which is the difference between its fitted value and its LOO predicted response value.

The **DFFITS** statistic for observations \(i\) defined as

\[\text{DFFITS}_i = \hat{Y}_i - \hat{Y}_{i(i)}.\]

- A large difference between a fitted value and its associated LOO predicted value provides evidence an observation is influential.

Belsley et al. (2005) suggest that observation \(i\) is influential if \(|\text{DFFITS}_i|>2\sqrt{p/n}\).

An index plot of the DFFITS statistics for all observations will indicate the observations most influential in changing their associated predicted value.

The DFFITS statistics for each observation can be obtained using the
`dffits`

function.

**DFFITS example**

We can create an index plot of our DFFITS statistics using the
`dffits_plot`

function.

Some of the main arguments to the `dffits_plot`

function
are:

`model`

: the fitted model.`id_n`

: the number of observations for which to print an associated label.

Horizontal reference lines are automatically added at \(\pm 2\sqrt{p/n}\) to identify influential observations.

We create an index plot of the DFFITS statistics below. We identify the observations with the 3 most extreme DFFITS statistics.

`dffits_plot(lmod, id_n = 3)`

We see that observations 33, 179, and 214 all have particularly
extreme DFFITS statistics. Several other observations have statistics
more extreme than \(\pm 1\) (run
`dffits_test(lmod)`

for a complete list).

Cook (1977) proposed the **Cook’s distance** to
summarize the potential influence of an observation with a single
statistic.

The Cook’s distance for the \(i\)th observation is

\[D_i=\frac{\sum_{k=1}^n (Y_k - \hat{Y}_{k(i)})^2}{p \widehat{\sigma}^2} = \frac{1}{p} r_i^2 \frac{h_i}{1-h_i}.\]

- \(\hat{Y}_{k(i)}\) is the predicted response for observation \(k\) when observation \(i\) is not included in the analysis (this is a type of LOO prediction).
- The
**standardized residual**for observation \(i\) is

\[ r_i = \frac{\hat{\epsilon}_i}{\widehat{\sigma}\sqrt{1-h_i}}. \]

An index plot of the Cook’s distances can be used to identify observations with unusually large Cook’s distances.

- Cook’s distance values can be obtained using the
`cooks.distance`

function. - Kutner et al. (2015) suggest declaring an observation to be influential if its Cook’s distance is more than \(F^{0.5}_{p, n-p}\), i.e., the 0.5 quantile of an \(F\) distribution with \(p\) numerator degrees of freedom and \(n-p\) denominator degrees of freedom.

**Cook’s distance example**

An index plot of the Cook’s distances can be created using the
`cooks_plot`

function.

Some of the main arguments to the `cooks_plot`

function
are:

`model`

: the fitted model.`id_n`

: the number of observations for which to print an associated label.

A horizontal reference line is automatically added at \(F^{0.5}_{p,n-p}\) to identify influential observations.

We create an index plot of the Cook’s distances below. We identify the observations with the 3 most extreme Cook’s distances.

`cooks_plot(lmod, id_n = 3)`

We see that observations 33, 179, and 214 all have particularly extreme Cook’s distances, indicating those observations are influential in some sense. This is consistent with our previous results.

An **influence plot** is a scatter plot of the
studentized or standardized residuals versus the leverage values. The
size of each point in the plot is proportional to either its Cook’s
distance or DFFITS statistic.

In general, observations that are extreme with respect to their residuals, leverage values, or Cook’s distance/DFFITS statistics are more likely be be potentially influential.

**Influence plot example**

An influence plot can be created using the
`influence_plot`

function from the **api2lm**
package.

Some of the main arguments to the `influence_plot`

function are:

`model`

: the fitted model.`rtype`

: the type of residual to plot on the y-axis. The default is the studentized residuals.`criterion`

: the criterion that controls the size of the points. The default is the Cook’s distance.`id_n`

: the number of observations for which to print an associated label. The most extreme observations with respect to the criterion are labeled.

We create in influence plot for the model fit to the
`home_sales`

data below.

`influence_plot(lmod)`

Belsley, D. A., Kuh, E., & Welsch, R. E. (2005). Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley & Sons.

Cook, R. D. (1977). Detection of Influential Observation in Linear Regression. Technometrics, 19(1), 15–18. https://doi.org/10.2307/1268249

Kutner, Michael H, Christopher J Nachtsheim, John Neter, and William Li. 2005. Applied Linear Statistical Models, 5th Edition. McGraw-Hill/Irwin, New York.

Welsch, R. E., & Kuh, E. (1977). Linear regression diagnostics (No. w0173). National Bureau of Economic Research.