NScluster (version 1.2.0)

EstimateTypeA: Parameter Estimation of the Type A Model

Description

Parameter estimation of the Type A model via numerical calculation of the Ripley's \(K\)-function.

Usage

EstimateTypeA(xy.points, pars, eps = 0.001, uplimit = 0.3, skip = 1,
                process.report =0, plot=TRUE)

Arguments

xy.points

a matrix containing the coordinates (x,y) of points in a unit square: \(W=[0,1]\times[0,1]\).

pars

a named vector of containing the initial guess of the model parameters (mu, nu, a, sigma1, sigma2), where mu is an intensity of parents, nu is an expected number of descendants for each parent, a is a mixture parameter, sigma1 and sigma2 are parameters of the dispersal kernel for each component.

eps

the optimization procedure is iterated at most 1000 times until process2$stderr becomes smaller than eps.

uplimit

upper limit value in place of \(\infty\) in the integral in the distribution function.

skip

the variable for the fast likelihood but rough approximation of the initial estimates. The skip calculate the Palm intensity function in the log-likelihood unction for every skip-th \(r_{ij}\) in the ordered distances of the pairs \(i\) and \(j\).

process.report

the level of reporting the process of minimizing. Allowed values are as follows:

0

no report (default).

1

output the process of minimizing the negative Palm log-likelihood function until the values converge to MPLEs. (process1)

2

output the process of optimizing by the simplex with the normalized parameters. (process2)

3

output both processes.

plot

logical. If TRUE (default), the process of optimizing by the simplex with the normalized parameters is plotted.

Value

mple

MPLE (maximum Palm likelihood estimate).

process1

a list with following components. (Only returned if process.report = 1 or 3.)

cflg

1 (="update") or -1 (="testfn"), where "update" indicates that -log L value has attained the minimum so far, otherwise not.

logl.palm

the minimized -log L in the process of minimizing the negative Palm log-likelihood function.

mples

corresponding MPLEs.

process2

a list with following components. (Only returned if process.report = 2 or 3.)

logl.simplex

the minimized -log L by the simplex method.

stderr

the standard deviations.

pa.normal

the normalized variables corresponding the initial estimates.

References

U. Tanaka, Y. Ogata and K. Katsura, Simulation and estimation of the Neyman-Scott type spatial cluster models, Computer Science Monographs No.34, 2008, 1-44. The Institute of Statistical Mathematics.

Examples

# NOT RUN {
## simulation
pars <- c(mu = 50.0, nu = 30.0, a = 0.3, sigma1 = 0.005, sigma2 = 0.1)
z <- SimulateTypeA(pars, seed = 575)

## estimation
## need very long c.p.u time in the minimization procedure
# }
# NOT RUN {
init.pars <- c(mu = 60.0, nu = 40.0, a = 0.5, sigma1 = 0.01, sigma2 = 0.1)
EstimateTypeA(z$offspring$xy, init.pars, skip = 100)
# }