In our example, we will use the “Auto” dataset from the “ISLR” package and use the variables “mpg”,“displacement”,“horsepower”,and “weight” to predict “acceleration”. We will also use the “mgcv” package. Below is some initial code to begin the analysis

`library(mgcv)`

`library(ISLR) data(Auto)`

We will now make the model we want to understand the response of “accleration” to the explanatory variables of “mpg”,“displacement”,“horsepower”,and “weight”. After setting the model we will examine the summary. Below is the code

```
model1<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight),data=Auto)
summary(model1)
```

```
##
## Family: gaussian
## Link function: identity
##
## Formula:
## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.54133 0.07205 215.7 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(mpg) 6.382 7.515 3.479 0.00101 **
## s(displacement) 1.000 1.000 36.055 4.35e-09 ***
## s(horsepower) 4.883 6.006 70.187 < 2e-16 ***
## s(weight) 3.785 4.800 41.135 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.733 Deviance explained = 74.4%
## GCV = 2.1276 Scale est. = 2.0351 n = 392
```

All of the explanatory variables are significant and the adjust r-squared is .73 which is excellent. edf stands for “effective degrees of freedom”. This modified version of the degree of freedoms is due to the smoothing process in the model. GCV stands for generalized cross validation and this number is useful when comparing models. The model with the lowest number is the better model.

We can also examine the model visually by using the “plot” function. This will allow us to examine if the curvature fitted by the smoothing process was useful or not for each variable. Below is the code.

`plot(model1)`

We can also look at a 3d graph that includes the linear predictor as well as the two strongest predictors. This is done with the “vis.gam” function. Below is the code

`vis.gam(model1)`

If multiple models are developed. You can compare the GCV values to determine which model is the best. In addition, another way to compare models is with the “AIC” function. In the code below, we will create an additional model that includes “year” compare the GCV scores and calculate the AIC. Below is the code.

```
model2<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight)+s(year),data=Auto)
summary(model2)
```

```
##
## Family: gaussian
## Link function: identity
##
## Formula:
## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight) +
## s(year)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.54133 0.07203 215.8 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(mpg) 5.578 6.726 2.749 0.0106 *
## s(displacement) 2.251 2.870 13.757 3.5e-08 ***
## s(horsepower) 4.936 6.054 66.476 < 2e-16 ***
## s(weight) 3.444 4.397 34.441 < 2e-16 ***
## s(year) 1.682 2.096 0.543 0.6064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.733 Deviance explained = 74.5%
## GCV = 2.1368 Scale est. = 2.0338 n = 392
```

```
#model1 GCV
model1$gcv.ubre
```

```
## GCV.Cp
## 2.127589
```

```
#model2 GCV
model2$gcv.ubre
```

```
## GCV.Cp
## 2.136797
```

As you can see, the second model has a higher GCV score when compared to the first model. This indicates that the first model is a better choice. This makes sense because in the second model the variable “year” is not significant. To confirm this we will calculate the AIC scores using the AIC function.

`AIC(model1,model2)`

```
## df AIC
## model1 18.04952 1409.640
## model2 19.89068 1411.156
```

Again, you can see that model1 s better due to its fewer degrees of freedom and slightly lower AIC score.

**Conclusion**

Using GAMs is most common for exploring potential relationships in your data. This is stated because they are difficult to interpret and to try and summarize. Therefore, it is normally better to develop a generalized linear model over a GAM due to the difficulty in understanding what the data is trying to tell you when using GAMs.