Exercise

# Model soybean growth with GAM

In this exercise you will model the average leaf weight on a soybean plant as a function of time (after planting). As you will see, the soybean plant doesn't grow at a steady rate, but rather has a "growth spurt" that eventually tapers off. Hence, leaf weight is not well described by a linear model.

Recall that you can designate which variable you want to model non-linearly in a formula with the `s()`

function:

```
y ~ s(x)
```

Also remember that `gam()`

from the package `mgcv`

has the calling interface

```
gam(formula, family, data)
```

For standard regression, use `family = gaussian`

(the default).

The soybean training data, `soybean_train`

is loaded into your workspace. It has two columns: the outcome `weight`

and the variable `Time`

. For comparison, the linear model `model.lin`

, which was fit using the formula `weight ~ Time`

has already been loaded into the workspace as well.

Instructions 1/3

**undefined XP**

Fill in the blanks to plot `weight`

versus `Time`

(`Time`

on x-axis). *Does the relationship look linear?*