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Spatial Point Patterns
book

Spatial Point Patterns

by Adrian Baddeley, Ege Rubak, Rolf Turner
November 2015
Intermediate to advanced content levelIntermediate to advanced
828 pages
33h 11m
English
Chapman and Hall/CRC
Content preview from Spatial Point Patterns
Poisson Models 359
status indica tors, I
i
= 1 if y
i
is a case a nd I
i
= 0 if it is a control. Then, by the principle explained in
Section 9.10.2, the disease status indicators satisfy a binary regression
log
P {I
i
= 1}
P {I
i
= 0}
= log
p
i
1 p
i
= logr(y
i
,θ).
If the risk function r(u , θ) is loglinear in θ, the relationship is a logistic regression. This is a
well-known principle in epidemiology. Digg le and Rowlingson [236] argue the advantages of this
approa c h in a spatial context, which include not having to estimate th e population density.
For the Chorley-Ribble dataset we can carry out such an analysis as fo llows:
> X <- split(chorley)$larynx
> D <- split ...
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Publisher Resources

ISBN: 9781482210217