Predict with the soybean model on test data

In this exercise you will apply the soybean models from the previous exercise (model.lin and model.gam, already in your workspace) to new data: soybean_test.

The data frame soybean_test and the models model.lin and model.gam are in the workspace.

Create a column soybean_test$pred.lin with predictions from the linear model model.lin.
Create a column soybean_test$pred.gam with predictions from the gam model model.gam.
- For GAM models, the predict() method returns a matrix, so use as.numeric() to convert the matrix to a vector.
Fill in the blanks to gather() the prediction columns into a single value column pred with key column modeltype. Call the long dataframe soybean_long.
Calculate and compare the RMSE of both models.
- Which model does better?
Run the code to compare the predictions of each model against the actual average leaf weights.
- A scatter plot of weight as a function of Time.
- Point-and-line plots of the predictions (pred) as a function of Time.
- Notice that the linear model sometimes predicts negative weights! Does the gam model?

Exercise

Predict with the soybean model on test data

Instructions