ecespa {ecespa} | R Documentation |
Some wrappers, functions and data sets for spatial point pattern analysis, with an ecological bias.
Package: | ecespa |
Type: | Package |
Version: | 1.0-3 |
Date: | 2007-11-11 |
License: | GPL (version 2 or later) |
Marcelino de la Cruz Rot, with contributions of Philip M. Dixon and heavily borrowing Baddeley's & Turner's spatstat code.
Mantainer: Marcelino de la Cruz Rot marcelino.delacruz@upm.es
De la Cruz, M. 2007. Métodos para analizar datos puntuales. En: Introducción al Análisis Espacial de Datos en Ecología y Ciencias Ambientales: Métodos y Aplicaciones (eds. Maestre, F. T., Escudero, A. y Bonet, A.), pp 000-000. Asociación Española de Ecología Terrestre, Universidad Rey Juan Carlos y Caja de Ahorros del Mediterráneo, Madrid.
De la Cruz, M., Romao, R.L. & Escudero, A. 2007. Where do seedlings go? A spatio-temporal analysis of early mortality in a semiarid specialist. Submitted.
Diggle, P. J. 2003. Statistical analysis of spatial point patterns. Arnold, London.
Dixon, P.M. 2002. Nearest-neighbor contingency table analysis of spatial segregation for several species. Ecoscience, 9 (2): 142-151.
Penttinen, A. 2006. Statistics for Marked Point Patterns. In The Yearbook of the Finnish Statistical Society, pp. 70-91.
## Not run: ### Summarize the joint pattern of points and marks at different scales ### with the normalized mark-weighted K-function (Penttinen, 2006). ### Compare this function in two consecutive cohorts of Helianthemum ### squamatum seedlings: data(seedlings1) data(seedlings2) s1km <- Kmm(seedlings1, r=1:100) s2km <- Kmm(seedlings2, r=1:100) plot(s1km$r, s1km$Kmm.n, type="l", lty=1, lwd=3, ylim=c(0.6, 1.2), xlab="r (cm)", ylab= expression (K[mm](r)), main="Mark-weighted K-function of Hs seedling cohorts") lines(s2km$r, s2km$Kmm.n, lty=2,lwd=3) abline(h=1, lwd=2, lty=3) legend(x=60, y=1.2, legend=c("HsC1", "HsC2", "Ho:"), lty=c(1, 2, 3), lwd=c(3, 2, 2), bty="n") ### Explore the local relationships between marks and locations (e.g. size ### of one cohort of H. squamatum seedlings). Map the marked point pattern ### to a random field for visual inspection, with the normalized mark-sum ### measure (Penttinen, 2006). data(seedlings1) seed.m <- marksum(seedlings1, R=20) marksum.plot(seed.m, what="marksum") # raw mark-sum measure marksum.plot(seed.m, what="pointsum") # point sum measure marksum.plot(seed.m, what="normalized") # normalized mark-sum measure ### Test asociation/repulsion between a "fixed" pattern (e.g. adult ### H. squamatum plants) and a "variable" pattern (e.g. of surviving and ### dead seedlings), with 2.5% and 97.5% envelopes of 999 random ### labellings (De la Cruz & al. 2007). data(Helianthemum) cosa <- K012(Helianthemum, fijo="adultHS", i="deadpl", j="survpl", r=seq(0,200,le=201), nsim=999, nrank=25, correction="isotropic") plot(cosa$k01, sqrt(./pi)-r~r, col=c(3, 1, 3), lty=c(3, 1, 3), las=1, ylab=expression(L[12]), xlim=c(0, 200), main="adult HS vs. dead seedlings") plot(cosa$k02, sqrt(./pi)-r~r, col=c(3, 1, 3), lty=c(3, 1, 3), las=1, ylab=expression(L[12]), xlim=c(0, 200), main="adult HS vs. surviving seedlings") ### Test differences of agregation and segregation between two patterns, ### e.g. surviving and dying H. squamatum seedlings (De la Cruz & al. 2007). data(Helianthemum) cosa12 <- K1K2(Helianthemum, j="deadpl", i="survpl", r=seq(0,200,le=201), nsim=99, nrank=1, correction="isotropic") plot(cosa12$k1k2, lty=c(2, 1, 2), col=c(2, 1, 2), xlim=c(0, 200), main= "survival- death") plot(cosa12$k1k12, lty=c(2, 1, 2), col=c(2, 1, 2), xlim=c(0, 200), main="segregation of surviving seedlings") plot(cosa12$k2k12, lty=c(2, 1, 2), col=c(2, 1, 2), xlim=c(0, 200), main= "segregation of dying seedlings") ### Test segregation based on the counts in the contingency table ### of nearest neighbors in a multitype point pattern (Dixon, 2002) data(swamp) dixon2002(swamp,nsim=99) ### Fit the Poisson cluster point process to a point pattern with the method of ### minimum contrast (Diggle 2003). data(gypsophylous) # Estimate K function ("Kobs"). gyps.env <- envelope(gypsophylous, Kest, correction="iso") plot(gyps.env, sqrt(./pi)-r~r) # Fit Poisson Cluster Process. The limits of integration # rmin and rmax are setup to 0 and 60, respectively. cosa.pc <- pc.estK(Kobs = gyps.env$obs[gyps.env$r<=60], r = gyps.env$r[gyps.env$r<=60]) # Add fitted Kclust function to the plot. lines(gyps.env$r,sqrt(Kclust(gyps.env$r, cosa.pc$sigma2,cosa.pc$rho)/pi)-gyps.env$r, lty=2, lwd=3, col="purple") # A kind of pointwise test of the gypsophylous pattern been a realisation # of the fitted model, simulating with sim.poissonc and using function J (Jest). gyps.env.sim <- envelope(gypsophylous, Jest, simulate=expression(sim.poissonc(gypsophylous, sigma=sqrt(cosa.pc$sigma2), rho=cosa.pc$rho))) plot(gyps.env.sim, main="") ## End(Not run)