GLM in R: Generalized Linear Model
Data scientists often use models to predict future situations. GLMs are one such tool and, when used for these situations, they are sometimes called supervised learning.
For this exercise, you will predict the expected number of daily civilian fire injury victims for the North American summer months of June, July, and August using the Poisson regression you previously fit and the
Recall that the Poisson slope and intercept estimates are on the natural log scale and can be exponentiated to be more easily understood. You can do this by specifying
type = "response" with the predict function.
TRY IT YOURSELF: Access the exercise in our Generalized Linear Models in R course here.
newDatto see your new prediction situation.
- Use the fit Poisson regression,
poissonOutas the object and
newDatas the new data in
predict(). Be sure to exponentiate your output by setting
type = "response". Save the results as
# print the new input months print(___) # use the model to predict with new data ___ <- predict(object = ___, newdata = ___, type = "response") # print the predictions print(___)
To learn more about generalized linear models in R, please see this video from our course Generalized Linear Models in R.