# Interpreting Linear Prediction Models

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Although linear models are one of the simplest machine learning techniques, they are still a powerful tool for predictions. This is particularly due to the fact that linear models are especially easy to interpret. Here, I discuss the most important aspects when interpreting linear models by example of ordinary least-squares regression using the airquality data set.

## The airquality data set

The airquality data set contains 154 measurements of the following four air quality metrics as obtained in New York:

- Ozone: Mean ozone level in parts per billion
- Solar.R: Solar radiation in Langleys
- Wind: Average wind speed in miles per hour
- Temp: Maximum daily temperature in degrees Fahrenheit

We’ll clean up the data set by removing all `NA`

entries and excluding the `Month`

and `Day`

columns, which should not play a role as predictors.

```
data(airquality)
ozone <- subset(na.omit(airquality),
select = c("Ozone", "Solar.R", "Wind", "Temp"))
```

### Data exploration and preparation

The prediction task is the following: Can we predict the ozone level given solar radiation, wind speed, and temperature? To see whether the assumptions of the linear model are appropriate for the data at hand, we will compute the correlation between the variables:

```
# scatterplot matrix
plot(ozone)
```

```
# pairwise variable correlations
cors <- cor(ozone)
print(cors)
```

```
## Ozone Solar.R Wind Temp
## Ozone 1.0000000 0.3483417 -0.6124966 0.6985414
## Solar.R 0.3483417 1.0000000 -0.1271835 0.2940876
## Wind -0.6124966 -0.1271835 1.0000000 -0.4971897
## Temp 0.6985414 0.2940876 -0.4971897 1.0000000
```

```
# which variables are highly correlated, exclude self-correlation
cor.idx <- which(abs(cors) >= 0.5 & cors != 1, arr.ind = TRUE)
cor.names <- paste0(colnames(ozone)[cor.idx[,1]], "+",
colnames(ozone)[cor.idx[,2]], ": ", round(cors[cor.idx], 2))
print(cor.names)
```

```
## [1] "Wind+Ozone: -0.61" "Temp+Ozone: 0.7" "Ozone+Wind: -0.61"
## [4] "Ozone+Temp: 0.7"
```

Since ozone is involved in two linear interactions, namely:

- Ozone has a positive correlation with temperature
- Ozone has a negative correlation with wind

This suggests that it should be possible to form a linear model predicting the ozone level using the remaining features.

### Splitting into training and test sets

We will take 70% of samples for training and 30% for testing:

```
set.seed(123)
N.train <- ceiling(0.7 * nrow(ozone))
N.test <- nrow(ozone) - N.train
trainset <- sample(seq_len(nrow(ozone)), N.train)
testset <- setdiff(seq_len(nrow(ozone)), trainset)
```

## Investigating the linear model

To illustrate the most important aspects of interpreting linear models, we will train an ordinary least-squares model for the training data in the following way:

`model <- lm(Ozone ~ Solar.R + Temp + Wind, ozone, trainset)`

To interpret the model, we use the `summary`

function:

```
model.summary <- summary(model)
print(model.summary)
```

```
##
## Call:
## lm(formula = Ozone ~ Solar.R + Temp + Wind, data = ozone, subset = trainset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.135 -12.670 -2.221 9.420 65.914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -65.76604 22.52381 -2.920 0.004638 **
## Solar.R 0.05309 0.02305 2.303 0.024099 *
## Temp 1.56320 0.25530 6.123 4.03e-08 ***
## Wind -2.61904 0.68921 -3.800 0.000295 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.17 on 74 degrees of freedom
## Multiple R-squared: 0.5924, Adjusted R-squared: 0.5759
## F-statistic: 35.85 on 3 and 74 DF, p-value: 2.039e-14
```

### The residuals

The first piece of information we obtain is on the residuals. The term *residual* comes from the residual sum of squares (RSS), which is defined as

where the residual \(r_i\) is defined as the difference between observed and predicted values, \(f(x_i)\), from the observed value, \(y_i\).

The residual median value suggest that the model generally predicts slightly higher values of ozone than observed. The large maximum value, however, indicates that some outlier predictions are also much too low. Looking at a numbers can be a bit abstract, so let us plot the predictions of the model against the observed values:

```
res <- residuals(model)
# ensure that x- and y-axis have the same range
pred <- model$fitted.values
obs <- ozone[trainset, "Ozone"]
# determine maximal range
val.range <- range(pred, obs)
plot(obs, pred,
xlim = val.range, ylim = val.range,
xlab = "Observed ozone concentration",
ylab = "Predicted ozone concentration",
main = "Residuals of the linear model for the training data")
# show ideal prediction as a diagonal
abline(0,1, col = "red")
# add residuals to the plot
segments(obs, pred, obs, pred + res)
```

### The coefficients

Now that we understand the residuals, let’s take a look at the coefficients. We can use the `coefficients`

function to obtain the fitted coefficients of the model:

`coefficients(model)`

```
## (Intercept) Solar.R Temp Wind
## -65.76603538 0.05308965 1.56320267 -2.61904128
```

Note that the intercept of the model has quite a low value. This is the value that the model will predict in the case that all independent values are zero. The low coefficient for `Solar.R`

indicates that solar radiation does not play an important role for predicting ozone levels, which does not come as a surprise as it did not show a large correlation with the ozone level in our exploratory analysis. The coefficient for `Temp`

indicates that ozone levels are high if the temperature is high (since ozone will form faster). The coefficient for `Wind`

tells us that ozone levels will be low for fast winds (since ozone will be blown away).

The other values associated with the coefficients give information about the statistical certainty of the estimates.

`model.summary$coefficients`

```
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -65.76603538 22.52380940 -2.919845 4.638426e-03
## Solar.R 0.05308965 0.02305379 2.302860 2.409936e-02
## Temp 1.56320267 0.25530453 6.122894 4.034064e-08
## Wind -2.61904128 0.68920661 -3.800081 2.946349e-04
```

`Std. Error`

is the standard error of the coefficient estimate`t value`

indicates the value of the coefficient in terms of the standard error`Pr(>|t|)`

is the p-value for the t-test, which indicates the significance of the test statistic

### The standard error

The standard error of the coefficients is defined as the standard deviation of the feature variance:

\[\hat{\sigma} = \sqrt{\text{diag}\left(\sigma_{\text{err}}^2 (X^T X)^{-1}\right)}\]

where \({X^T X}^{-1}\) is the inverse of the design matrix \(X\), which includes the intercept, and where \(\sigma_{\text{err}}^2\) is the variance of the error.

In R, the standard errors of the model estimates can be computed via:

```
# compute variance-covariance matrix of model estimates
var.matrix <- vcov(model)
print(var.matrix)
```

```
## (Intercept) Solar.R Temp Wind
## (Intercept) 507.32198977 2.893612e-02 -5.345957524 -9.940961e+00
## Solar.R 0.02893612 5.314773e-04 -0.001667748 7.495211e-05
## Temp -5.34595752 -1.667748e-03 0.065180401 6.715467e-02
## Wind -9.94096142 7.495211e-05 0.067154670 4.750058e-01
```

```
# use variances from variance-covariance matrix
std.err <- sqrt(diag(vcov(model)))
print(std.err)
```

```
## (Intercept) Solar.R Temp Wind
## 22.52380940 0.02305379 0.25530453 0.68920661
```

Now, you are probably wondering where the values from `vcov`

are coming from. It is defined as the variance-covariance matrix of the design matrix scaled by the variance of the error:

```
scaled.var.matrix <- model.summary$cov.unscaled * model.summary$sigma^2
print(scaled.var.matrix)
```

```
## (Intercept) Solar.R Temp Wind
## (Intercept) 507.32198977 2.893612e-02 -5.345957524 -9.940961e+00
## Solar.R 0.02893612 5.314773e-04 -0.001667748 7.495211e-05
## Temp -5.34595752 -1.667748e-03 0.065180401 6.715467e-02
## Wind -9.94096142 7.495211e-05 0.067154670 4.750058e-01
```

The variance that is used to scale to unscaled variance-covariance matrix is the estimated variance of the error, which is defined by

\[ \sigma_{\text{err}}^2 = \frac{1}{n-p} \text{RSS}\]

The `cov.unscaled`

parameter is simply the variance-covariance matrix of all features \(\beta_i\) (including the intercept) of the design matrix:

```
# include intercept as a feature via 'model.matrix'
X <- model.matrix(model) # design matrix
# solve X^T %*% X = I to find the inverse of X^T * X
unscaled.var.matrix <- solve(crossprod(X), diag(4))
print(paste("Is this the same?", isTRUE(all.equal(unscaled.var.matrix, model.summary$cov.unscaled, check.attributes = FALSE))))
```

`## [1] "Is this the same? TRUE"`

#### The t-value

The t-value is defined as

\[t_i = \frac{\hat{\beta}_i}{\hat{\sigma}_i}\]

In R, they are computed via

```
# divide model coefficients by standard errors
t.vals <- coef(model) / std.err
print(t.vals)
```

```
## (Intercept) Solar.R Temp Wind
## -2.919845 2.302860 6.122894 -3.800081
```

#### The p-value

The p-value is computed under the assumption that \(\beta_i = 0\) for all coefficients. The t-values follow a t-distribution with

`model.df <- df.residual(model)`

degrees of freedom. The degrees of freedom of a linear model is defined as \[\text{df} = n – p\] where \(n\) is the number of samples and \(p\) is the number of features (including the inctercept). The p-value indicates the likelihood that the obtained coefficient estimates are different from zero purely by chance. Thus, a low p-value suggests that there is a significant association between a variable and the outcome.

### Further statistics

The following additional statistics are provided by the `summary`

function: the multiple R-squared, the adjusted R-squared, and the F-statistic. A nice summary of these statistics is available at StackExchange.

#### Residual standard error

As the name suggests, the residual standard error is the square root of the mean RSS (MSE) of model:

```
rss <- sum(res^2)
mean.rss <- rss / model.df
res.std.error <- sqrt(mean.rss)
print(res.std.error)
```

`## [1] 18.16979`

The residual standard error simply indicates the average accuracy of the model. In this case, the value is quite low, indicating that the model has a good fit.

#### The multiple R-squared

The multiple R-squared indicates the coefficient of determination. It is defined as the square of the correlation between estimates and observed outcomes:

```
r.squared <- cor(pred, obs)^2
print(r.squared)
```

`## [1] 0.5924073`

In contrast to the correlation, which is in \([-1,1]\), R-squared is in \([0,1]\).

#### The adjusted R-squared

The adjusted R-squared value adjusts the R-squared according to the complexity of the model:

\[R^2_{a} = 1 – (1 – R^2) \frac{n – 1}{n – p – 1}\]

where \(n\) is the number of observations and \(p\) the number of features. Thus, the adjusted R-squared can be computed like this:

```
n <- length(trainset) # number of samples
p <- length(coefficients(model)) - 1 # number of features w/o intercept
r.squared.adj <- 1 - (1 - r.squared) * ((n - 1) / (n - p - 1))
print(r.squared.adj)
```

`## [1] 0.5758832`

If there is a considerable difference between R-squared and the adjusted R-squared, this indicates that one may consider reducing the feature space.

#### The F-statistic

The F-statistic is defined as the ratio of explained vs unexplained variance. For regression, the F-statistic always indicates the difference between two models where model 1 (\(p_1\)) is defined by a subset of features from model 2 (\(p_2\)):

\[F={\frac {\left({\frac {{\text{RSS}}_{1}-{\text{RSS}}_{2}}{p_{2}-p_{1}}}\right)}{\left({\frac {{\text{RSS}}_{2}}{n-p_{2}}}\right)}}\]

The F-statistic describes the extent to which the predictive performance (in terms of the RSS) of model 2 is superior over model 1. The default F-statistic that is reported refers to the difference between the trained model and the intercept-only model:

```
# create intercept-only model
null.model <- lm(formula = Ozone ~ 1, ozone, trainset)
print(null.model)
```

```
##
## Call:
## lm(formula = Ozone ~ 1, data = ozone, subset = trainset)
##
## Coefficients:
## (Intercept)
## 36.76
```

```
# plot the incercept-only prediction function
plot(ozone$Ozone[trainset], null.model$fitted.values)
```

Hence, the null hypothesis of the test is that the fit of the intercept-only model and the specified model is equal. If the null hypothesis can be rejected, this implies that the specified model has a better fit than the null model.

Let’s calculate this by hand to show the idea:

```
rss <- function(model) {
return(sum(model$residuals^2))
}
f.statistic <- function(model1, model2) {
n <- length(model1$fitted.values)
n2 <- length(model2$fitted.values)
if (n != n2) {
stop("Both models should have the same number of training samples!")
}
p1 <- length(coefficients(model1))
p2 <- length(coefficients(model2))
enum <- (rss(model1) - rss(model2)) / (p2 - p1)
denom <- rss(model2) / (n - p2)
out <- enum/denom
return(out)
}
# compare the intercept-only model and the model with 3 features
f.statistic(null.model, model)
```

`## [1] 35.85126`

In this case, the F-statistic has a large value, which indicates that we the trained model is significantly better than an intercept-only model.

## Confidence intervals

Confidence intervals are a useful tool for interpreting linear models. By default, `confint`

computes the 95% confidence interval (\(\pm 1.96 \hat{\sigma}\)):

```
ci <- confint(model)
apply(ci, 1, function(x) paste0("95% CI: [",
round(x[1], 2), ",", round(x[2], 2), "]"))
```

```
## (Intercept) Solar.R
## "95% CI: [-110.65,-20.89]" "95% CI: [0.01,0.1]"
## Temp Wind
## "95% CI: [1.05,2.07]" "95% CI: [-3.99,-1.25]"
```

These values indicate that the model is quite unsure about the estimate of the intercept. This could suggest that more data is necessary to obtain a better fit.

## Retrieving confidence and prediction intervals for estimated values

The predictions from a linear model can be turned into intervals by providing the `interval`

argument. These intervals give a measure of confidence for the predicted values. There are two types of intervals: confidence and prediction intervals. Let’s apply our model on the test set using different arguments for the `interval`

parameter to see the difference between the two types of intervals:

```
# compute confidence intervals (CI) for predictions:
preds.ci <- predict(model, newdata = ozone[testset,], interval = "confidence")
# compute prediction intervals (PI) for predictions:
preds.pred <- predict(model, newdata = ozone[testset,], interval = "prediction")
# note that the values differ:
cbind(rbind(preds.ci[1,], preds.pred[2,]), "Method" = c("CI", "PI"))
```

```
## fit lwr upr Method
## [1,] "-4.42397219873667" "-13.4448767773931" "4.59693237991976" "CI"
## [2,] "-22.0446990408131" "-61.0004555440645" "16.9110574624383" "PI"
```

The confidence intervals are narrow intervals, while the prediction (tolerance) intervals are wide intervals. Their values are based on the provided tolerance/significance level, as specified by the `level`

parameter (default: 0.95).

Their definitions are slightly different. Given a new observation \(x\), CIs and PIs are defined as follows

\[\hat{y}_{\text{CI}} \pm t_{\alpha/2, \text{df}} \sqrt{\text{MSE} \cdot \left(\frac{1}{n} \frac{(x – \overline{x})^2}{\sum_i (x_i – \overline{x})^2}\right)}\] \[\hat{y}_{\text{PI}} \pm t_{\alpha/2, \text{df}} \sqrt{\text{MSE} \cdot \left(1 + \frac{1}{n} \frac{(x – \overline{x})^2}{\sum_i (x_i – \overline{x})^2}\right)}\]

where \(t_{\alpha/2, \text{df}}\) is the t-value with \(\text{df} = 2\) degrees of freedom and a significance level of \(\alpha\), \(\sigma_{\text{err}}\) is the standard error of the residual, \({\sigma}_x^2\) is the variance of the independent features, and \(\overline{x}\) indicates the mean value of the features. More detailed explanations for calculating CIs and PIs are available at RPubs and through this video.

With regard to their interpretation, the difference between the two intervals is further discussed at GraphPad.

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