hSDM.ZIP.RdThe hSDM.ZIP function can be used to model species
distribution including different processes in a hierarchical Bayesian
framework: a \(\mathcal{B}ernoulli\) suitability process
(refering to various ecological variables explaining environmental
suitability or not) and a \(\mathcal{P}oisson\) abundance
process (refering to various ecological variables explaining the
species abundance when the habitat is suitable). The hSDM.ZIP
function calls a Gibbs sampler written in C code which uses a
Metropolis algorithm to estimate the conditional posterior
distribution of hierarchical model's parameters.
hSDM.ZIP(counts, suitability, abundance, data,
suitability.pred=NULL, burnin = 5000, mcmc = 10000, thin = 10,
beta.start, gamma.start, mubeta = 0, Vbeta = 1e+06, mugamma = 0, Vgamma
= 1e+06, seed = 1234, verbose = 1, save.p = 0)A vector indicating the count for each observation.
A one-sided formula of the form \(\sim x_1+...+x_p\) with \(p\) terms specifying the explicative variables for the suitability process.
A one-sided formula of the form \(\sim w_1+...+w_q\) with \(q\) terms specifying the explicative variables for the abundance process.
A data frame containing the model's variables.
An optional data frame in which to look for variables with which to predict. If NULL, the observations are used.
The number of burnin iterations for the sampler.
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.
The thinning interval used in the simulation. The number of mcmc iterations must be divisible by this value.
Starting values for \(\beta\) parameters of the suitability process. This can either be a scalar or a \(p\)-length vector.
Starting values for \(\beta\) parameters of the abundance process. This can either be a scalar or a \(q\)-length vector.
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.
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.
Means of the Normal priors for the \(\gamma\)
parameters of the abundance process. mugamma must be either
a scalar or a p-length vector. If mugamma takes a scalar value,
then that value will serve as the prior mean for all of the
gammas. The default value is set to 0 for an uninformative prior.
Variances of the Normal priors for the
\(\gamma\) parameters of the abundance
process. Vgamma must be either a scalar or a p-length
vector. If Vgamma takes a scalar value, then that value will
serve as the prior variance for all of the gammas. The default
variance is large and set to 1.0E6 for an uninformative flat prior.
The seed for the random number generator. Default set to 1234.
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.
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 prob.p.pred
vector. Be careful, setting save.p to 1 might require a large
amount of memory.
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 \(D=-2\log(\prod_i P(y_i,z_i|...))\), is also provided.
If save.p is set to 0 (default),
prob.p.pred is the predictive posterior mean of the
probability associated to the suitability process for each
prediction. If save.p is set to 1, prob.p.pred is an
mcmc object with sampled values of the probability associated
to the suitability process for each prediction.
Predictive posterior mean of the probability associated to the suitability process for each observation.
Predictive posterior mean of the probability associated to the abundance process for each observation.
The model integrates two processes, an ecological process associated to habitat suitability (habitat is suitable or not for the species) and an abundance process that takes into account ecological variables explaining the species abundance when the habitat is suitable.
Suitability process: $$z_i \sim \mathcal{B}ernoulli(\theta_i)$$ $$logit(\theta_i) = X_i \beta$$
Abundance process: $$y_i \sim \mathcal{P}oisson(z_i * \lambda_i)$$ $$log(\lambda_i) = W_i \gamma$$
Flores, O.; Rossi, V. and Mortier, F. (2009) Autocorrelation offsets zero-inflation in models of tropical saplings density. Ecological Modelling, 220, 1797-1809.
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.
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.
if (FALSE) {
#==============================================
# hSDM.ZIP()
# Example with simulated data
#==============================================
#============
#== Preambule
library(hSDM)
#==================
#== Data simulation
# Set seed for repeatability
seed <- 1234
# Number of observations
nobs <- 1000
# Target parameters
beta.target <- matrix(c(0.2,0.5,0.5),ncol=1)
gamma.target <- matrix(c(1),ncol=1)
## Uncomment if you want covariates on the abundance process
## gamma.target <- matrix(c(0.2,0.5,0.5),ncol=1)
# Covariates for "suitability" process
set.seed(seed)
X1 <- rnorm(n=nobs,0,1)
set.seed(2*seed)
X2 <- rnorm(n=nobs,0,1)
X <- cbind(rep(1,nobs),X1,X2)
# Covariates for "abundance" process
W <- cbind(rep(1,nobs))
## Uncomment if you want covariates on the abundance process
## set.seed(3*seed)
## W1 <- rnorm(n=nobs,0,1)
## set.seed(4*seed)
## W2 <- rnorm(n=nobs,0,1)
## W <- cbind(rep(1,nobs),W1,W2)
#== Simulating latent variables
# Suitability
logit.theta <- X %*% beta.target
theta <- inv.logit(logit.theta)
set.seed(seed)
y.1 <- rbinom(nobs,1,theta)
# Abundance
set.seed(seed)
log.lambda <- W %*% gamma.target
lambda <- exp(log.lambda)
set.seed(seed)
y.2 <- rpois(nobs,lambda)
#== Simulating response variable
Y <- y.2*y.1
#== Data-set
Data <- data.frame(Y,X1,X2)
## Uncomment if you want covariates on the abundance process
## Data <- data.frame(Y,X1,X2,W1,W2)
#==================================
#== ZIP model
mod.hSDM.ZIP <- hSDM.ZIP(counts=Data$Y,
suitability=~X1+X2,
abundance=~1, #=~1+W1+W2 if covariates
data=Data,
suitability.pred=NULL,
burnin=1000, mcmc=1000, thin=5,
beta.start=0,
gamma.start=0,
mubeta=0, Vbeta=1.0E6,
mugamma=0, Vgamma=1.0E6,
seed=1234, verbose=1,
save.p=0)
#==========
#== Outputs
pdf(file="Posteriors_hSDM.ZIP.pdf")
plot(mod.hSDM.ZIP$mcmc)
dev.off()
summary(mod.hSDM.ZIP$prob.p.latent)
summary(mod.hSDM.ZIP$prob.q.latent)
summary(mod.hSDM.ZIP$prob.p.pred)
}