| ecespa {ecespa} | R Documentation |
Some wrappers, functions and data sets for spatial point pattern analysis, with an ecological bias.
| Package: | ecespa |
| Type: | Package |
| Version: | 1.1-1 |
| Date: | 2008-11-16 |
| License: | GPL (>=2) |
Marcelino de la Cruz Rot, with contributions of Philip M. Dixon and Jose M. Blanco-Moreno and heavily borrowing Baddeley's & Turner's spatstat code.
Mantainer: Marcelino de la Cruz Rot marcelino.delacruz@upm.es
De la Cruz, M. 2008. 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. and Escudero, A. 2008. Null models and tools for multivariate heterogeneous point patterns. Submitted.
De la Cruz, M., Romao, R.L., Escudero, A. and Maestre, F.T. In press. Where do seedlings go? A spatio-temporal analysis of early mortality in a semiarid specialist. Ecography.
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.
Olano, J.M., Laskurain, N.A., Escudero, A. and De la Cruz, M. 2008. Why and where adult trees die in a secondary temperate forest? The role of neighbourhood. Submitted.
Penttinen, A. 2006. Statistics for Marked Point Patterns. In The Yearbook of the Finnish Statistical Society, pp. 70-91.
Rey-Benayas, J.M., de la Montaña, E., Pérez-Camacho, L., de la Cruz, M., Moreno, D., Parejo, J.L. and Suárez-Seoane, S. 2008. Inter-annual dynamics and spatial congruence of a nocturnal bird assemblage inhabiting a Mediterranean agricultural mosaic. Submitted.
Syrjala, S. E. 1996. A statistical test for a difference between the spatial distributions of two populations. Ecology 77: 75-80.
## Not run:
#############################################
### Transfom easily data from a data.frame into the ppp format
### of spatstat:
data(fig1)
plot(fig1) #typical xyplot
fig1.ppp <- haz.ppp (fig1)
fig1.ppp
plot(fig1.ppp) # point pattern plot of spatstat
#############################################
### 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:
## Figure 3.10 of De la Cruz (2008):
data(seedlings1)
data(seedlings2)
s1km <- Kmm(seedlings1, r=1:100)
s2km <- Kmm(seedlings2, r=1:100)
plot(s1km, ylime=c(0.6,1.2), lwd=2, maine="", xlabe="r(cm)")
plot(s2km, lwd=2, lty=2, add=T )
abline(h=1, lwd=2, lty=3)
legend(x=60, y=1.2, legend=c("Hs_C1", "Hs_C2", "H0"),
lty=c(1, 2, 3), lwd=c(3, 2, 2), bty="n")
## A pointwise test of normalized Kmm == 1 for seedlings1:
s1km.test <- Kmm(seedlings1, r=1:100, nsim=99)
plot(s1km.test, xlabe="r(cm)")
#############################################
### 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=25)
plot(seed.m, what="marksum", sigma = 5) # raw mark-sum measure; sigma is bandwith for smoothing
plot(seed.m, what="pointsum", sigma = 5) # point sum measure
plot(seed.m, what="normalized", dimyx=200, contour=TRUE, sigma = 5) # normalized mark-sum measure
# the same with added grid
plot(seed.m, what="normalized", dimyx=200, contour=TRUE, sigma = 5, grid=TRUE)
#############################################
### Test against the null model of "independent labelling",
### i.e. 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. in press).
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. in press).
data(Helianthemum)
cosa12 <- K1K2(Helianthemum, j="deadpl", i="survpl", r=seq(0,200,le=201),
nsim=999, 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$k2k12, lty=c(2, 1, 2), col=c(2, 1, 2), xlim=c(0, 200),
main= "segregation of dying seedlings")
#############################################
### Test 'univariate' and 'bivariate' point patterns
### against non-Poisson (in-)homogeneous models
### (De la Cruz and Escudero, submited).
data(urkiola)
#####################
## univariate example
# get univariate pp
I.ppp <- split.ppp(urkiola)$birch
# estimate inhomogeneous intensity function
I.lam <- predict (ppm(I.ppp, ~polynom(x,y,2)), type="trend", ngrid=200)
# Compute and plot envelopes to Kinhom, simulating from an Inhomogeneous
# Poisson Process:
I2.env <- envelope( I.ppp,Kinhom, lambda=I.lam, correction="trans",
nsim=99, simulate=expression(rpoispp(I.lam)))
plot(I2.env, sqrt(./pi)-r~r)
# It seems that there is short scale clustering; let's fit an Inhomogeneous
# Poisson Cluster Process:
I.ki <- ipc.estK(mippp=I.ppp, lambda=I.lam, correction="trans")
# Compute and plot envelopes to Kinhom, simulating from the fitted IPCP:
Ipc.env <- Ki(I.ki, correction="trans", nsim=99, ngrid=200)
plot (Ipc.env)
#####################
## bivariate example: test independence between birch and quercus in Urkiola
J.ppp <- split.ppp(urkiola)$oak
# We want to simulate oak from a homogeneous Poisson model:
J.ppm <- ppm(J.ppp, trend=~1, interaction=Poisson() )
IJ.env <- Kci (mod1=I.ki, mod2=J.ppm, nsim=99)
plot(IJ.env, type=12)
plot(IJ.env, type=21)
#############################################
### Simulate envelopes from the fitted values of a logistic model,
### as in Olano et al. (submited)
data(quercusvm)
# read fitted values from logistic model:
probquercus <-c(0.99955463, 0.96563477, 0.97577094, 0.97327199, 0.92437309,
0.84023396, 0.94926682, 0.89687281, 0.99377915, 0.74157478, 0.95491518,
0.72366493, 0.66771787, 0.77330148, 0.67569082, 0.9874892, 0.7918891,
0.73246803, 0.81614635, 0.66446411, 0.80077908, 0.98290508, 0.54641754,
0.53546689, 0.73273626, 0.7347013, 0.65559655, 0.89481468, 0.63946334,
0.62101995, 0.78996371, 0.93179582, 0.80160346, 0.82204428, 0.90050059,
0.83810669, 0.92153079, 0.47872421, 0.24697004, 0.50680935, 0.6297911,
0.46374812, 0.65672284, 0.87951682, 0.35818237, 0.50932432, 0.92293014,
0.48580241, 0.49692053, 0.52290553, 0.7317549, 0.32445982, 0.30300865,
0.73599359, 0.6206056, 0.85777043, 0.65758613, 0.50100406, 0.31340849,
0.22289286, 0.40002879, 0.29567678, 0.56917817, 0.56866864, 0.27718552,
0.4910667, 0.47394411, 0.40543788, 0.29571349, 0.30436276, 0.47859015,
0.31754526, 0.42131675, 0.37468782, 0.73271225, 0.26786274, 0.59506388,
0.54801851, 0.38983575, 0.64896835, 0.37282031, 0.67624306, 0.29429766,
0.29197755, 0.2247629, 0.40697843, 0.17022391, 0.26528042, 0.24373722,
0.26936163, 0.13052254, 0.19958585, 0.18659692, 0.36686678, 0.47263005,
0.39557661, 0.68048997, 0.74878567, 0.88352322, 0.93851375)
################################
## Envelopes for an homogeneous point pattern:
cosap <- Kinhom.log(A=quercusvm, lifemark="0", nsim=99, prob=probquercus)
plot(cosap)
################################
## Envelopes for an inhomogeneous point pattern:
## First, fit an inhomogeneous Poisson model to alive trees :
quercusalive <- unmark(quercusvm[quercusvm$marks == 0])
mod2 <- ppm(quercusalive, ~polynom(x,y,2))
## Now use mod2 to estimate lambda for K.inhom:
cosapm <- Kinhom.log(A=quercusvm, lifemark="0", prob=probquercus,
nsim=99, mod=mod2)
plot(cosapm)
#############################################
### 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", nsim=99)
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$r,
lty=2, lwd=3, col="purple")
# A kind of pointwise test of the gypsophylous pattern been a realisation
# of the fitted model, simulating with sim.poissonc and using function J (Jest).
gyps.env.sim <- envelope(gypsophylous, Jest, nsim=99,
simulate=expression(sim.poissonc(gypsophylous,
sigma=sqrt(cosa.pc$sigma2), rho=cosa.pc$rho)))
plot(gyps.env.sim, main="")
#############################################
### Fit the inhomogeneous Poisson cluster point process to a point pattern
### with the method of minimum contrast (Diggle 2003) as explained
### by Waagepetersen (2007).
data(urkiola)
# get univariate pp
I.ppp <- split.ppp(urkiola)$birch
#estimate inhomogeneous intensity function
I.lam <- predict (ppm(I.ppp, ~polynom(x,y,2)), type="trend", ngrid=200)
# Compute and plot envelopes to Kinhom, simulating from an Inhomogeneous Poisson Process:
I2.env <- envelope( I.ppp,Kinhom, lambda=I.lam, correction="trans",
nsim=99, simulate=expression(rpoispp(I.lam)))
plot(I2.env, sqrt(./pi)-r~r)
# It seems that there is short scale clustering; lets fit an IPCP:
I.ki <- ipc.estK(mippp=I.ppp, lambda=I.lam, correction="trans")
# Compute and plot envelopes to Kinhom, simulating from the fitted IPCP:
Ipc.env= Ki(I.ki, correction="trans", nsim=99, ngrid=200)
plot (Ipc.env)
#############################################
### Compute Syrjala's test for the difference between the spatial
### distributions of two populations, as in Rey-Benayas et al.
### (submited)
data(syr1); data(syr2); data(syr3)
plot(syrjala.test(syr1, syr2, nsim=999))
plot(syrjala.test(syr1, syr3, nsim=999))
## End(Not run)