coefficientsModel1: Model of Identical Habitat use by the Roe Deer in 3 Sites.

In ClementCalenge/roedeer3sites: Habitat Selection by the Roe Deer in three French Study Sites

Description

This help page describes how we fitted the model f1, supposing the same habitat use by the roe deer in Chize, la Petite Pierre and Trois-Fontaines. The R and JAGS code used to fit the model is included in the Examples section of this help page, and the dataset coefficientModel1 is an object of class mcmc.list (package rjags) containing the sampled values of the coefficients.

Usage

data("coefficientsModel1")

Format

This object is an object of class mcmc.list containing the values of the coefficients sampled for three chains.

Details

The fitted model was the following (see paper) for the multinomial logit of the probability of use of the scrubs:

log(P(scrubs)/P(CWS)) = a0f + apf * PoleStageInHomeRange + aff * ScrubsInHomeRange + eps1

Where eps1 is a normal overdispersion residual, and for the probability of use of the pole stage:

log(P(pole stage)/P(CWS)) = a0p + app * PoleStageInHomeRange + apf * ScrubsInHomeRange

Where eps2 is a normal overdispersion residual.

Source

Sonia Said, Centre national d'etude et de recherche appliquee "Cervides-Sangliers", Office national de la chasse et de la faune sauvage, Birieux, Ain, France.

Examples

## We load the data used for the fit:
data(HS3sites)

## We remove the information concerning the meadows
## (negligible, see the paper)
HS3sites$locs$meadows <- NULL
HS3sites$hr$meadows <- NULL

## The site is not required for this model
HS3sites$site <- NULL

## Calculates the total number of relocations
HS3sites$N <- apply(HS3sites$locs,1,sum)

## stores the number of animals
HS3sites$J <- nrow(HS3sites$locs)

## For a better mixing, we scale the covariates
HS3sites$hr <- scale(HS3sites$hr)


## Not run: 

## We define the following starting values
init <- list(
  list(a0f=-10,apf=0,aff=0, afp=0,app=0),
  list(a0f=-10,apf=1,aff=1, afp=-1,app=-1),
  list(a0f=-10,apf=-1,aff=1, afp=1,app=-1))


## We write the JAGS model in a file named model1.jags
## in the working directory

cat("model {

  a0f ~ dnorm(0,0.001)
  a0p ~ dnorm(0,0.001)
  aff ~ dnorm(0,0.001)
  afp ~ dnorm(0,0.001)
  apf ~ dnorm(0,0.001)
  app ~ dnorm(0,0.001)
  sig ~ dunif(0,100)

  for (j in 1:J) {

    eps1[j]~dnorm(0,sig)
    eps2[j]~dnorm(0,sig)
    eps3[j]~dnorm(0,sig)

    ep[j,1] <- a0f + aff*hr[j,1] + apf*hr[j,2] + eps1[j]
    ep[j,2] <- a0p + afp*hr[j,1] + app*hr[j,2] + eps2[j]
    p[j,1] <- exp(ep[j,1])/(1+exp(ep[j,1])+exp(ep[j,2]))
    p[j,2] <- exp(ep[j,2])/(1+exp(ep[j,1])+exp(ep[j,2]))
    p[j,3] <- 1/(1+exp(ep[j,1])+exp(ep[j,2]))

    locs[j,]~dmulti(p[j,], N[j])

  }

}
", file = "model1.jags")


## initialization
mo1 <- jags.model("model1.jags", n.adapt=20000, n.chain=3, data=HS3sites, inits=init)

## We draw 500 000 realization of this model (takes a long time!!!)
coefficientsModel1 <- coda.samples(mo1,
                                   variable.names=c("a0f", "apf", "aff", "afp", "a0p",
                                                    "app",  "sig"),
                    n.iter=500000, thin=50)


## End(Not run)

## To avoid waiting a long time for the fit, we have stored the results
## in the dataset coefficientsModel1
data(coefficientsModel1)

ClementCalenge/roedeer3sites documentation built on May 16, 2019, 6:58 p.m.