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  3. Supervised Learning in R: Regression

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 has been pre-loaded. 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 as well.

Instructions 1/3

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Fill in the blanks to plot weight versus Time (Time on x-axis). Does the relationship look linear?