[R-sig-Geo] Simple Ripley's CRS test for market point patters

[Alexandre Santos]

Dear R-Sig-Geo Members,     I"ve like to find any simple way for apply CRS test for market point patters, for this I try to create a script below:
#Packages require(spatstat)require(sp)

# Create some points that represents ant nests in UTMxp<-c(371278.588,371250.722,371272.618,371328.421,371349.974,371311.95,371296.265,371406.46,371411.551,371329.041,371338.081,371334.182,371333.756,371299.818,371254.374,371193.673,371172.836,371173.803,371153.73,371165.051,371140.417,371168.279,371166.367,371180.575,371132.664,371129.791,371232.919,371208.502,371289.462,371207.595,371219.008,371139.921,371133.215,371061.467,371053.69,371099.897,371108.782,371112.52,371114.241,371176.236,371159.185,371159.291,371158.552,370978.252,371120.03,371116.993)
yp<-c(8246507.94,8246493.176,8246465.974,8246464.413,8246403.465,8246386.098,8246432.144,8246394.827,8246366.201,8246337.626,8246311.125,8246300.039,8246299.594,8246298.072,8246379.351,8246431.998,8246423.913,8246423.476,8246431.658,8246418.226,8246400.161,8246396.891,8246394.225,8246400.391,8246370.244,8246367.019,8246311.075,8246255.174,8246255.085,8246226.514,8246215.847,8246337.316,8246330.197,8246311.197,8246304.183,8246239.282,8246239.887,8246241.678,8246240.361,8246167.364,8246171.581,8246171.803,8246169.807,8246293.57,8246183.194,8246189.926)
# Then I create the size of each nest - my covariate used as marked processarea<-c(117,30,4,341,15,160,35,280,108,168,63,143,2,48,182,42,88,56,27,156,288,45,49,234,72,270,91,40,304,56,35,4,56.7,9,4.6,105,133,135,23.92,190,12.9,15.2,192.78,104,255,24)

# Make a countour - only as exerciseW <- convexhull.xy(xp,yp)
#Create a ppp objectp_xy<-cbind(xp,yp)syn.ppp<-ppp(x=coordinates(p_xy)[,1],y=coordinates(p_xy)[,2],window=W, marks=area)syn.ppp <- as.ppp(syn.ppp)plot(syn.ppp, main=" ")
First, I've like to study CSR of market point process (my hypothesis is that different size have a same spatial distribution) when area >= 0, area < 25 and area >=25, area < 55, for this I make:
# Area 0-25env.formi1<-envelope(syn.ppp,nsim=99,fun=Kest, area >= 0, area < 25)plot(env.formi1,lwd=list(3,1,1,1), main="") 
# Area 25-55env.formi2<-envelope(syn.ppp,nsim=99,fun=Kest, area >=25, area < 55)plot(env.formi2,lwd=list(3,1,1,1), main="") 
My first problem is that I have the same plot in both conditions and I don't know why.
Second, if I try to estimate the market intensity observed pattern
est.int <- density(syn.ppp)est_xy <-  rmpoispp(est.int)plot(est_xy, main=" ")
My output is only my points position without marked area in my ppp object created.
My question is what is the problem with this Ripley's reduced second moment function approach? There are any way for study my point process when the area is a covariate of my point process?
Thanks in advanced
Alexandre


-- ======================================================================Alexandre dos SantosProteção Florestal IFMT - Instituto Federal de Educação, Ciência e Tecnologia de Mato GrossoCampus CáceresCaixa Postal 244Avenida dos Ramires, s/nBairro: Distrito Industrial Cáceres - MT                      CEP: 78.200-000Fone: (+55) 65 99686-6970 (VIVO) (+55) 65 3221-2674 (FIXO)e-mails:alexandresantosbr using yahoo.com.br         alexandre.santos using cas.ifmt.edu.br Lattes: http://lattes.cnpq.br/1360403201088680 OrcID: orcid.org/0000-0001-8232-6722   -   ResearcherID: A-5790-2016Researchgate: www.researchgate.net/profile/Alexandre_Santos10                       LinkedIn: br.linkedin.com/in/alexandre-dos-santos-87961635Mendeley:www.mendeley.com/profiles/alexandre-dos-santos6/
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