Modeling and Multivariate Methods - SAS

Chapter 6 Fitting Generalized Linear Models 183
Examples
The Pearson chi-square statistic is defined as
X 2 w i
( y i
– μ i
) 2
= ----------------------------
V( μ i
)
i
where y i is the i th response, μ i is the corresponding predicted mean, V(μ i ) is the variance function, and w i is
a known weight for the i th observation. If no weight is known, w i = 1 for all observations.
One strategy for variable selection is to fit a sequence of models, beginning with a simple model with only
an intercept term, and then include one additional explanatory variable in each successive model. You can
measure the importance of the additional explanatory variable by the difference in deviances or fitted log
likelihoods between successive models. Asymptotic tests computed by JMP enable you to assess the
statistical significance of the additional term.
Examples
The following examples illustrate how to use JMP’s generalized linear models platform.
Poisson Regression
This example uses data from a study of nesting horseshoe crabs. Each female crab had a male crab resident
in her nest. This study investigated whether there were other males, called satellites, residing nearby. The
data set CrabSatellites.jmp contains a response variable listing the number of satellites, as well as variables
describing the female crab’s color, spine condition, weight, and carapace width. The data are shown in
Figure 6.2.
Figure 6.2 Crab Satellite Data