Modeling grain yields

Introduction to Writing Functions in R

Richie Cotton

Data Evangelist at DataCamp

It's the faceted time series of corn yields that you drew in the previous exercise. There are nine panels, one for each census region. In each panel, yields increase slowly until about 1950, then increase rapidly after that.

Linear models vs. generalized additive models

A linear model


lm(
  response_var ~ explanatory_var1 + explanatory_var2, 
  data = dataset
)

A generalized additive model

library(mgcv)
gam(
  response_var ~ s(explanatory_var1) + explanatory_var2, 
  data = dataset
)

Predicting GAMs

predict_this <- data.frame(
  explanatory_var1 = c("some", "values"),
  explanatory_var2 = c("more", "values")
)

predicted_responses <- predict(model, predict_this, type = "response")

predict_this %>%
  mutate(predicted_responses = predicted_responses)

Let's practice!

Introduction to Writing Functions in R