Fitting an Inhomogeneous Poisson model

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Fitting an Inhomogeneous Poisson model

R-sig-geo mailing list
Dear R-Sig-Geo members,

I've like to fitting an Inhomogeneous Poisson model and I have one
question about better approaches and/or steps order for this.

### Simulating Inhomogeneous Poisson process
library(spatstat)
cI <- rpoispp(function(x, y) {200 * x^2}, lmax = 1000)
cII <- rpoispp(function(x, y) {50 * y^2}, lmax = 1000)
cIII <- rpoispp(function(x,y) {100 * exp(-3*x)}, lmax = 1000)
ant<-superimpose(cI=cI,cII=cII,cIII=cIII)

#Fitting a Poisson model with an intensity that is log-linear in the
cartesian coordinates with marks
m1<-ppm(ant, ~x + y + marks) # Allows different (constant) intensity for
each category

#Now I fitting a Inhomogeneous Poisson model with an intensity that is
log-quadratic in the cartesian coordinates
#with marks

m2<-ppm(ant, ~polynom(x,y,2) + marks)

#Comparing the models
anova(m1,m2,test="Chi")
#Pr(>Chi) = 0.8497 No difference and I choose the more parsimonious
model m1.

#But if I make the models coefficients inspection:
m1#First model
#������������� Estimate����� S.E.����� CI95.lo��� CI95.hi Ztest����� Zval
#(Intercept)� 3.0370358 0.3075106� 2.434326133� 3.6397455�� *** 9.876199
#x����������� 1.3466820 0.3453355� 0.669836907� 2.0235272�� *** 3.899634
#y����������� 0.6497591 0.3338675 -0.004609147� 1.3041274 1.946159
#markscII��� -1.4190842 0.2877424 -1.983048959 -0.8551194�� *** -4.931787
#markscIII�� -0.6306268 0.2154810 -1.052961828 -0.2082918��� ** -2.926601
m2 #Second model
#�������������� Estimate����� S.E.�� CI95.lo��� CI95.hi Ztest������� Zval
#(Intercept)� 3.31439533 0.6095378� 2.119723� 4.5090674�� *** 5.43755529
#x����������� 0.05883185 1.5736189 -3.025405� 3.1430683 0.03738634
#y����������� 0.53037435 1.5536416 -2.514707� 3.5754559 0.34137497
#I(x^2)������ 1.16253158 1.2939978 -1.373658� 3.6987207 0.89840303
#I(x * y)���� 0.04678026 1.1589111 -2.224644� 2.3182043 0.04036570
#I(y^2)������ 0.08747001 1.2903205 -2.441512� 2.6164518 0.06778937
#markscII��� -1.41908418 0.2877424 -1.983049 -0.8551194�� *** -4.93178655
#markscIII�� -0.63062682 0.2154810 -1.052962 -0.2082918��� ** -2.92660056

I was so astonished with the no significance of I(x^2) by Ztest and my
question is
whats is the better workflow for improve models of this nature:

1) Try the new models with several cartesian coordinates combinations
and degrees (first),
2) Try the new models with pair-wise comparison between levels (marks)
(second),
3) Comparing with null model.

or another approach and/or step order?

--
Alexandre dos Santos
Geotechnologies and Spatial Statistics applied to Forest Entomology
Instituto Federal de Mato Grosso (IFMT) - Campus Caceres
Caixa Postal 244 (PO Box)
Avenida dos Ramires, s/n - Vila Real
Caceres - MT - CEP 78201-380 (ZIP code)
Phone: (+55) 65 99686-6970 / (+55) 65 3221-2674
Lattes CV: http://lattes.cnpq.br/1360403201088680
OrcID: orcid.org/0000-0001-8232-6722
ResearchGate: www.researchgate.net/profile/Alexandre_Santos10
Publons: https://publons.com/researcher/3085587/alexandre-dos-santos/
--


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