Summary statistics in linear regression

Jose M Sallan 2021-06-04 7 min read

In this post, we will examine the results obtained with the function glance of the broom package for linear regression estimators obtained with the lm function. Let’s remember first that linear regression estimates the values of dependent variable y from a set of independent variables \(x_j\):

\[ y_i = \beta_0 + \beta_1x_{i1} + \dots + \beta_px_{ip} + \varepsilon_i \]

When we estimate the regression coeficients from a dataset we obtain:

\[ y_i = b_0 + b_1x_{i1} + \dots + b_px_{ip} + e_i = \hat{y}_i + e_i \]

The lm function obtains ordinary least squares estimators (OLS) for the linear regression model. Under conditions defined in this post, OLS estimates are the ones of maximum likelihood.

Some models

Let’s fit four regression models using the mtcars dataset:

  • mod0: the null model, with no predictors for fuel consumption mpg.
  • mod1: mpg as a function of weight wt.
  • mod2: mpg as a function of wt and the square of wt. I have used the poly function to obtain linearly independent polynomial predictors.
  • mod3: mpg as a function of wt and type of transmission am, considering the interaction between the two variables.
  • mod4: mpg as a function of the rest of the variables of the dataset.
mod0 <- lm(mpg ~ 1, mtcars)
mod1 <- lm(mpg ~ wt, mtcars)
mod2 <- lm(mpg ~ poly(wt, 2), mtcars)
mod3 <- lm(mpg ~ wt*am, mtcars)
mod4 <- lm(mpg ~ ., mtcars)

Collecting summary statistics

Let’s examine the summary statistics of model fit obtained with the glance function of the broom package applied to a linear regression obtained with lm.

fit_models <- bind_rows(lapply(list(mod0, mod1, mod2, mod3, mod4), function(x) glance(x))) %>%
  mutate(mod =c("mod0", "mod1", "mod2", "mod3", "mod4")) %>%
  relocate(mod, r.squared)

fit_models %>%
  kbl(digits = 3) %>%
    kable_paper("hover", full_width = F)
mod r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
mod0 0.000 0.000 6.027 NA NA NA -102.378 208.756 211.687 1126.047 31 32
mod1 0.753 0.745 3.046 91.375 0 1 -80.015 166.029 170.427 278.322 30 32
mod2 0.819 0.807 2.651 65.638 0 2 -75.024 158.048 163.911 203.745 29 32
mod3 0.833 0.815 2.591 46.567 0 3 -73.738 157.476 164.805 188.008 28 32
mod4 0.869 0.807 2.650 13.932 0 10 -69.855 163.710 181.299 147.494 21 32

We can group those statistics into three categories:

  • statistic, p.value, df and df.residual provide information about the test of overall significance of the model.
  • r.squared and adj.r.squared are measures of the coefficient of determination.
  • logLik, AIC, BIC and deviance are fit measures based on maximum likelihood estimation.

sigma and nobs retrieve the an estimation of variance of the residuals and the number of observations, respectively.

Test of overall significance

This test consists in evaluating the null hypothesis that the regression model does not explain the variability of the dependent variable better than its mean:

\[H_0 : \beta_1 = \beta_2 = \dots = \beta_p = 0\]

To test that hypotesis we compute:

  • The sum of squares of errors or residuals \(SSE\):

\[SSE = \sum_{i=1}^n \left( y_i - \hat{y}_i \right)^2\]

  • The sum of squares of the model, or the differences between mean and fitted values \(SSM\):

\[SSM = \sum_{i=1}^n \left( \hat{y}_i - \bar{y_i} \right)^2 \]

  • The total sum of squares \(SST\) or differences respect to the mean:

\[SST = \sum_{i=1}^n \left( y_i - \bar{y}_i \right)^2\]

These three magnitudes are related by the equality:

\[ SST = SSE + SSM \]

We can calculate the mean sum of squares dividing each by its degrees of freedom:

\[\begin{align} MSE &= \frac{SSE}{n-p-1} & MSM &= \frac{SSM}{p} & MST &= \frac{SSE}{n-1} \end{align}\]

If the null hypothesis is true, the quotient between \(MSM\) and \(MSE\) will follow a law:

\[ \frac{MSM}{MSE} \sim F_{p,n-p-1} \]

If the quotient is large enough, it can be interpreted as an abnormal observation of the underlying distribution (so the null hypothesis will be true). The probability of this is the p.value listed on the table. If this p-value is smaller than 0.05, we usually prefer the alternative explanation that the null hypothesis is false, and therefore the model is significant.

This analysis is presented at the bottom of the summary of the lm function:

## Call:
## lm(formula = mpg ~ wt, data = mtcars)
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5432 -2.3647 -0.1252  1.4096  6.8727 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  37.2851     1.8776  19.858  < 2e-16 ***
## wt           -5.3445     0.5591  -9.559 1.29e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 3.046 on 30 degrees of freedom
## Multiple R-squared:  0.7528, Adjusted R-squared:  0.7446 
## F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10

We can use a similar test to compare pairs of nested models, where the model with more variables contains all the variables of the other model. This is the case of mod1 and mod2:

anova(mod1, mod2)
## Analysis of Variance Table
## Model 1: mpg ~ wt
## Model 2: mpg ~ poly(wt, 2)
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)   
## 1     30 278.32                               
## 2     29 203.75  1    74.576 10.615 0.00286 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As the p-value of the test is smaller than 0.05, we conclude that mod2 adds explanatory power to mod1.

Coefficients of determination

Another parameter to evaluate the fit of a linear regression is the coefficient of determination. It is calculated as:

\[R^2 = 1- \frac{\sum \left( y_i - \hat{y}_i \right)^2 }{\sum \left( y_i - \bar{y}_i \right)^2} = 1- \frac{SEE}{SST}\]

If the model fits well to the data, the sum of squared residuals will be small and \(R^2\) will be close to one.

A problem of \(R^2\) is that it does not decrease when we add variables to the model, because we always can set all regression coefficients of the new variables equal to zero and obtain the same sum of squared residuals of the former model. To account for this, we define the adjusted coefficient of determination as:

\[ R^2_{adj.} = 1- \frac{\sum \left( y_i - \hat{y}_i \right)^2 / \left( n-p-1 \right) }{\sum \left( y_i - \bar{y}_i \right)^2 / \left( n-1 \right)} = 1- \frac{MSE}{MST} \]

\(R^2_{adj.}\) and \(R^2\) are related by the expression:

\[ R^2_{adj.} = 1 - \left( 1- R^2 \right) \frac{n-1}{n-p-1} \]

Looking at the table, we observe that r.squared increases with number of variables, but not adj.r.squared. The best value of adjusted coefficient of determination goes to mod3.

Log-likelihood estimation metrics

In this post I discussed the meaning of the likelihood function, and how can we obtain estimates of a model maximizing the log likelihood function. This function for linear regression is equal to:

\[ \mathcal{l} \left[ \left( \sigma, \mu \right), \mathbf{e} \right] = - \frac{n}{2}ln\left( 2\pi \right) - \frac{n}{2}ln \left( \sigma^2 \right) - \frac{1}{2\sigma^2} \sum_{i=1}^i e_i^2 \]

This value is (approximately) returned in the logLik column of the table. As we maximize likelihood the larger (less negative) its value the better the fit. Similarly to \(R^2\), ´logLik` tends to increase as we add variables.

We can obtain a more parsimonious indicator of fit based on likelihood with the Akaike information criterion (AIC). This parameter is equal to:

\[AIC = 2k - 2\mathcal{l}\]

where \(k\) is the number of parameters of the model. In this case \(k=p+2\) as we are estimating the \(p+1\) regression coefficients plus residual variance \(\sigma^2\). We prefer models with smaller values of AIC.

Another similar metric is the Bayesian information criterion (BIC):

\[BIC = k ln\left(n\right) - \mathcal{l}\]

Like AIC, we will prefer models with lower values of BIC.

AIC and BIC are suited to compare models. Both favour parsimonious models, that is, models with high explanatory power and few variables. mod2 and mod3 are the best models according to these criteria.

The deviance has little sense in linear models estimated through OLS. The value presented by glance is the sum of squared residuals \(SSE\).

Which is the best model?

The summary statistics presented by the glance function for models estimated with lm measure goodness of fit and parsimony.

  • A model with goodness of fit has a high power to explain the variablity and to predict the dependent variable. All the summary statistics presented here measure goodness of fit.
  • Parsimony combines simplicity and goodness of fit. Parsimonious models are simple models with great explanatory or predictive power. The adjusted coefficient of deteremination \(R^2_{adj.}\) and metrics AIC and BIC allow to detect parsimonious models.

For the models presented here, we can say that mod0 and mod1 show low goodness of fit, while the other three models have better values of r.squared, adj.r.squared and logLik. AIC and BIC show that mod2 and mod3 have the best balance between simplicity and goodness of fit, so we can consider them as the best models. mod3 has the best values of \(R^2_{adj.}\) and AIC, and mod2 the best value of BIC.