poilog (version 0.3)

MLE for bivariate Poisson lognormal distribution: Maximum Likelihood Estimation for Bivariate Poisson Lognormal Distribution

Description

bipoilogMLE fits the bivariate Poisson lognormal distribution to data

Usage

bipoilogMLE(n1, n2 = NULL, 
            startVals = c(mu1=1, mu2=1, sig1=2, sig2=2, rho=0.5), 
            nboot = 0, zTrunc = TRUE, file = NULL, 
            method = "BFGS", control = list(maxit=1000))

Arguments

n1

a vector or a matrix with two columns of pairwise counts of observed individuals for each species

n2

if n1 is not given as a matrix, a vector of counts with same ordering of species as in argument n1

startVals

starting values of parameters

nboot

number of parametric bootstraps, defaults to zero

zTrunc

logical; if TRUE (default) the zero-truncated distribution is fitted

file

text file to hold copies of bootstrap estimates

method

method to use during optimization, see details

control

a list of control parameters for the optimization routine, see details

Value

• par
  Maximum likelihood estimates of the parameters
• logLval
  Log likelihood of the data given the estimated parameters
• gof
  Godness of fit measure obtained by checking the rank of logLval against logLval's obtained during the bootstrap procedure, (gof < 0.05) or (gof > 0.95) indicates lack of fit
• boot
  A data frame containing the bootstrap replicates of parameters and logLval

encoding

latin1

Details

The function estimates the parameters mu1, sig1, mu2, sig2 and rho. In cases of incomplete sampling the estimates of mu1 and mu2 will be confounded with the sampling intensities (see rbipoilog). Assuming sampling intensities $\nu_1$ and $\nu_2$, the estimates of the means are $\code{mu1}+\ln \nu_1$ and $\code{mu2}+\ln\nu_2$. Parameters sig1, sig2 and rho can be estimated without any knowledge of sampling intensities. The parameters must be given starting values for the optimization procedure (default starting values are used if starting values are not specified in the function call). A zero-truncated distribution (see dbipoilog) is assumed by default (zTrunc = TRUE). In cases where the number of zeros is known the zTrunc argument should be set to FALSE. The function uses the optimization procedures in optim to obtain the maximum likelihood estimate. The method and control arguments are passed to optim, see the help page for this function for additional methods and control parameters. Parametric bootstrapping could be time consuming for large data sets. If argument file is specified, e.g. file =C:\\myboots.txt, the matrix with bootstrap estimates are copied into a tab-seperated text-file providing extra backup. Bootstrapping is done by simulating new sets of observations conditioned on the observed number of species (see rbipoilog).

References

Engen, S., R. Lande, T. Walla and P. J. DeVries. 2002. Analyzing spatial structure of communities using the two-dimensional Poisson lognormal species abundance model. American Naturalist 160, 60-73.

See Also

optim, dbipoilog, rbipoilog

Examples

set.seed(200)
## simulate observations
xy  <- rbipoilog(S=30,mu1=1,mu2=1,sig1=2,sig2=2,rho=0.5)

## obtain estimates of parameters
est <- bipoilogMLE(xy)

## similar, but now with bootstrapping 
est <- bipoilogMLE(xy,nboot=10)

## change start values and request tracing information 
## from optimization procedure
est <- bipoilogMLE(xy,startVals=c(2,2,4,4,0.3),
       control=list(maxit=1000,trace=1, REPORT=1))

## effect of sampling intensity 
xy  <- rbipoilog(S=100,mu1=1,mu2=1,sig1=2,sig2=2,rho=0.5,nu1=0.5,nu2=0.5)
est <- bipoilogMLE(xy)
## the expected estimates of mu1 and mu2 are now 1-log(0.5) = 0.3 (approximately)