Download the template R markdown file for this workshop.
In this exercise you will:
RiverProbAll
data.You should only attempt this (second) exercise using this dataset after completing the first exercise.)
data(RiverProbAll, package="ELMER")
str(RiverProbAll)
'data.frame': 91 obs. of 3 variables:
$ Flow : int 24 24 26 26 50 48 72 72 25 23 ...
$ Type : Factor w/ 3 levels "Canadian","Kayak",..: 1 3 2 1 1 1 1 2 1 2 ...
$ Problems: int 1 0 0 0 1 0 1 2 1 3 ...
You’ll also want the subset used previously.
data(RiverProb, package="ELMER")
str(RiverProb)
'data.frame': 21 obs. of 2 variables:
$ Flow : int 24 36 24 25 72 37 46 24 37 46 ...
$ Problems: int 0 0 0 0 0 0 0 0 0 0 ...
The dataset RiverProbAll
gives the number of problems
experienced by travelers on the Whanganui river. This is the full data
set including the craft type as a categorical variable.
In the previous exercise, you fitted a generalised linear model to this data using a Poisson distribution for the number of problems, using a log link function.
The example in Chapter 3 of ELMER used a subset of the Whanganui river data for Other craft. An identity link was used in that analysis instead of a log link.
Calculate the predicted number of problems for a range of river flows using this model, and compare with the predictions for the same flow and Other craft type for the model in the previous exercise .
Compare the models using a graph.
Hint: the values of the final parameters after 8 iterations were 1.22329 and -0.01703 for the weighted model fitted in Chapter 3.
You should compare your work with the solutions for this workshop.