hSDM (version 1.4.4)

hSDM.poisson: Poisson log regression model

Description

The hSDM.poisson function performs a Poisson log regression in a Bayesian framework. The function calls a Gibbs sampler written in C code which uses an adaptive Metropolis algorithm to estimate the conditional posterior distribution of model's parameters.

Usage

hSDM.poisson(counts, suitability, data, suitability.pred = NULL,
burnin = 5000, mcmc = 10000, thin = 10, beta.start, mubeta = 0, Vbeta =
1e+06, seed = 1234, verbose = 1, save.p = 0)

Value

mcmc

An mcmc object that contains the posterior sample. This object can be summarized by functions provided by the coda package. The posterior sample of the deviance \(D\), with \(D=-2\log(\prod_i P(y_i,n_i|\beta))\), is also provided.

lambda.pred

If save.p is set to 0 (default), lambda.pred is the predictive posterior mean of the abundance associated to the suitability process for each prediction. If save.p is set to 1, lambda.pred is an mcmc object with sampled values of the abundance associated to the suitability process for each prediction.

lambda.latent

Predictive posterior mean of the abundance associated to the suitability process for each observation.

Arguments

counts

A vector indicating the count (or abundance) for each observation.

suitability

A one-sided formula of the form '~x1+...+xp' with p terms specifying the explicative covariates for the suitability process of the model.

data

A data frame containing the model's explicative variables.

suitability.pred

An optional data frame in which to look for variables with which to predict. If NULL, the observations are used.

burnin

The number of burnin iterations for the sampler.

mcmc

The number of Gibbs iterations for the sampler. Total number of Gibbs iterations is equal to burnin+mcmc. burnin+mcmc must be divisible by 10 and superior or equal to 100 so that the progress bar can be displayed.

thin

The thinning interval used in the simulation. The number of mcmc iterations must be divisible by this value.

beta.start

Starting values for beta parameters of the suitability process. If beta.start takes a scalar value, then that value will serve for all of the betas.

mubeta

Means of the priors for the \(\beta\) parameters of the suitability process. mubeta must be either a scalar or a p-length vector. If mubeta takes a scalar value, then that value will serve as the prior mean for all of the betas. The default value is set to 0 for an uninformative prior.

Vbeta

Variances of the Normal priors for the \(\beta\) parameters of the suitability process. Vbeta must be either a scalar or a p-length vector. If Vbeta takes a scalar value, then that value will serve as the prior variance for all of the betas. The default variance is large and set to 1.0E6 for an uninformative flat prior.

seed

The seed for the random number generator. Default to 1234.

verbose

A switch (0,1) which determines whether or not the progress of the sampler is printed to the screen. Default is 1: a progress bar is printed, indicating the step (in %) reached by the Gibbs sampler.

save.p

A switch (0,1) which determines whether or not the sampled values for predictions are saved. Default is 0: the posterior mean is computed and returned in the lambda.pred vector. Be careful, setting save.p to 1 might require a large amount of memory.

Author

Ghislain Vieilledent <[e-mail]>

Details

We model the abundance of the species as a function of environmental variables.

Ecological process: $$y_i \sim \mathcal{P}oisson(\lambda_i)$$ $$log(\lambda_i) = X_i \beta$$

References

Latimer, A. M.; Wu, S. S.; Gelfand, A. E. and Silander, J. A. (2006) Building statistical models to analyze species distributions. Ecological Applications, 16, 33-50.

Gelfand, A. E.; Schmidt, A. M.; Wu, S.; Silander, J. A.; Latimer, A. and Rebelo, A. G. (2005) Modelling species diversity through species level hierarchical modelling. Applied Statistics, 54, 1-20.

See Also

plot.mcmc, summary.mcmc

Examples

if (FALSE) {

#==============================================
# hSDM.poisson()
# Example with simulated data
#==============================================

#=================
#== Load libraries
library(hSDM)

#==================
#== Data simulation

#= Number of sites
nsite <- 200

#= Set seed for repeatability
seed <- 1234

#= Ecological process (suitability)
set.seed(seed)
x1 <- rnorm(nsite,0,1)
set.seed(2*seed)
x2 <- rnorm(nsite,0,1)
X <- cbind(rep(1,nsite),x1,x2)
beta.target <- c(-1,1,-1)
log.lambda <- X %*% beta.target
lambda <- exp(log.lambda)
set.seed(seed)
Y <- rpois(nsite,lambda)

#= Data-sets
data.obs <- data.frame(Y,x1,x2)

#==================================
#== Site-occupancy model

mod.hSDM.poisson <- hSDM.poisson(counts=data.obs$Y,
                                 suitability=~x1+x2,
                                 data=data.obs,
                                 suitability.pred=NULL,
                                 burnin=1000, mcmc=1000, thin=1,
                                 beta.start=0,
                                 mubeta=0, Vbeta=1.0E6,
                                 seed=1234, verbose=1,
                                 save.p=0)

#==========
#== Outputs

#= Parameter estimates
summary(mod.hSDM.poisson$mcmc)
pdf(file="Posteriors_hSDM.poisson.pdf")
plot(mod.hSDM.poisson$mcmc)
dev.off()

#== glm resolution to compare
mod.glm <- glm(Y~x1+x2,family="poisson",data=data.obs)
summary(mod.glm)

#= Predictions
summary(mod.hSDM.poisson$lambda.latent)
summary(mod.hSDM.poisson$lambda.pred)
pdf(file="Pred-Init.pdf")
plot(lambda,mod.hSDM.poisson$lambda.pred)
abline(a=0,b=1,col="red")
dev.off()

}