Dear all,

 

Let P1, P2 and P3 to be 3 realizations of a Log Gaussian Cox Process, say:

 

#Setting parameter values

sigma2 <- 1

kappa <- 1

beta0 <-  3

beta1 <-  4.5

beta2 <- -1.5

beta3 <- 2.5

 

## spatstat_1.33-0

##R  3.0.1

library(spatstat)

 

m <- as.im(function(x, y){beta0 + beta1 * x + beta2 * (x^2) + beta3 * y},
W=owin())

 

#Generating replicated point patterns

set.seed(1)

P1 <- rLGCP("matern", m, c(0, variance=sigma2, nugget=0, scale=1/kappa,
nu=1))

set.seed(2)

P2 <- rLGCP("matern", m, c(0, variance=sigma2, nugget=0, scale=1/kappa,
nu=1))

set.seed(3)

P3 <- rLGCP("matern", m, c(0, variance=sigma2, nugget=0, scale=1/kappa,
nu=1))

 

 

Considering either replicate P1, P2 or P3, estimates of the parameters
beta0, beta1, beta2, eta3, sigma2 and kappa can be obtained using the kppm
function:

 

Q1 <- quadscheme(P1)

Est1 <- kppm(Q1, trend=~polynom(x, 2) + y, cluster= "LGCP", method="clik",
covmodel=list(model="matern", nu=1))

 

Is is possible (and if yes how) to use the kppm function, or any other R
function,  to estimate the parameters beta0, beta1, beta2, beta3, sigma2 and
kappa based on the whole dataset (i.e., using the three replicates P1, P2
and P3) ?

 

Thanks in advance,

 

Hugues.

 

 

 


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