mcmc.genCatSMR.df: Fit the generalized categorical spatial mark resight model...

In benaug/SPIM: This package contains MCMC algorithms in R and (some) Rcpp for various Spatial Partial Identity Models. Prospective users should email Ben before using.

Fit the generalized categorical spatial mark resight model with detection function parameters that vary by the category levels of one categorical identity covariate, e.g. sex

Description

This function fits the generalized categorical spatial mark resight model with detection function parameters that vary by the category levels of one categorical identity covariate, e.g. sex. Modelling the marking process relaxes the assumption that the distribution of marked individuals across the landscape is spatially uniform. Category level-specific detection function parameters reduces individual heterogeneity and improves the probabilistic association of latent and partial identity samples. This is an expanded version of the sampler located in mcmc.genCatSMR.df(). A version of that sampler that allows individual activity centers to move between marking and sighting processes is in mcmc.conCatSMR.move(). Email Ben if you need both of these features simultaneously.

the data list should be formatted to match the list outputted by sim.genCatSMR.df(), but not all elements of that object are necessary. y.mark, y.sight.marked, y.sight.unmarked, G.marked, and G.unmarked are necessary list elements. y.sight.x and G.x for x=unk and marke.noID are necessary if there are samples of unknown marked status or samples from marked samples without individual identities.

An element "X1", a matrix of marking coordinates, an element "X2", a matrix of sighting coordinates, , an element "K1", the integer number of marking occasions, and an element "K2", the integer number of sighting occasions are necessary.

IDlist is a list containing elements ncat and IDcovs. ncat is an integer for the number of categorical identity covariates and IDcovs is a list of length ncat with elements containing the values each categorical identity covariate may take.

An element "locs", an n.marked x nloc x 2 array of telemetry locations is optional. This array can have missing values if not all individuals have the same number of locations and the entry for individuals with no telemetry should all be missing values (coded NA).

An element "markedS" is required if marking and sighting sessions are interspersed. This is a n_marked x K2 matrix with 0 indicating an individual was not marked on occasion k and 1 if it was.

I will write a function to build the data object with "secr-like" input in the near future.

Usage

mcmc.genCatSMR.df(data, niter = 2400, nburn = 1200, nthin = 5,
  M = 200, inits = NA, obstype = c("bernoulli", "poisson"),
  nswap = NA, proppars = list(lam0 = 0.05, sigma = 0.1, sx = 0.2, sy =
  0.2), storeLatent = TRUE, storeGamma = TRUE, IDup = "Gibbs",
  tf1 = NA, tf2 = NA)

Arguments

data	a data list as formatted by sim.genCatSMR(). See description for more details.
niter	the number of MCMC iterations to perform
nburn	the number of MCMC iterations to discard as burnin
nthin	the MCMC thinning interval. Keep every nthin iterations.
M	the level of data augmentation
inits	a list of initial values for lam0.mark,lam0.sight, sigma, gamma, and psi. The list element for gamma is itself a list with ncat elements. List elements for lam0.mark, lam0.sight, and sigma are themselves a list, housing starting vectors the same length as the first list element in IDcovs. See the example below.
obstype	a vector of length two indicating the observation model, "bernoulli" or "poisson", for the marking and sighting process
nswap	an integer indicating how many samples for which the latent identities are updated on each iteration.
storeLatent	a logical indicator for whether or not the posteriors of the latent individual identities, z, and s are stored and returned
storeGamma	a logical indicator for whether or not the posteriors for gamma are stored and returned
IDup	a character string indicating whether the latent identity update is done by Gibbs or Metropolis- Hastings, "Gibbs", or "MH". For obstype="bernoulli", only "MH" is available because the full conditional is not known.
tf1	a trap operation vector or matrix for the marking process. If exposure to capture does not vary by indiviudal, tf1 should be a vector of length J1 indicating how many of the K1 occasions each marking location was operational. If exposure to capture varies by individual or by trap and individual, tf1 should be a matrix of dimension M x J1 indicating how many of the K1 occasions individual i was exposed to at trap j. This allows known additions or removals during the marking process to be accounted for. Exposure for the n.marked+1 ... M uncaptured individuals should be the same as the number of occasions each trap was operational. We can't account for unknown additions and removals.
tf2	a trap operation vector or matrix for the sighting process. If exposure to capture does not vary by indiviudal, tf1 should be a vector of length J2 indicating how many of the K2 occasions each sighting location was operational. If exposure to capture varies by individual or by trap and individual, tf2 should be a matrix of dimension M x J2 indicating how many of the K2 occasions individual i was exposed to at trap j. This allows known additions or removals between the marking and sighting processes and during the sighting process to be accounted for. Exposure for the n.marked+1 ... M uncaptured individuals should be the same as the number of occasions each trap was operational. We can't account for unknown additions and removals.
propars	a list of proposal distribution tuning parameters for lam0.mark, lam0.sight, sigma, s, and st, for the the activity centers of untelemetered and telemetered individuals, respectively. The tuning parameter should be smaller for individuals with telemetry and increasingly so as the number of locations per individual increases. List elements for lam0.mark, lam0.sight, and sigma are themselves a list, housing vectors of tuning parameters the same length as the first list element in IDcovs.

Author(s)

Ben Augustine

Examples

## Not run: 
#Using categorical identity covariates
N=100
lam0.mark=c(0.075,0.01)
lam0.sight=c(0.3,0.2)
sigma=c(0.6,0.5)
K1=10 #number of marking occasions
K2=10 #number of sighting occasions
buff=2 #state space buffer
X1<- expand.grid(3:11,3:11) #marking locations
X2<- expand.grid(3:11+0.5,3:11+0.5) #sighting locations
pMarkID=c(0.8,0.8) #probability of observing marked status of marked and unmarked individuals
pID=0.8 #probability of determining identity of marked individuals
obstype=c("bernoulli","poisson") #observation model of both processes
ncat=3  #number of categorical identity covariates
gamma=IDcovs=vector("list",ncat) 
nlevels=rep(2,ncat) #number of IDcovs per loci
for(i in 1:ncat){ 
  gamma[[i]]=rep(1/nlevels[i],nlevels[i])
  IDcovs[[i]]=1:nlevels[i]
}
#inspect ID covariates and level probabilities
str(IDcovs) #3 covariates with 2 levels
str(gamma) #each of the two levels are equally probable
pIDcat=rep(1,ncat) #category observation probabilities
#Example of interspersed marking and sighting. 
Korder=c("M","M","S","S","S","S","M","M","S","M","M","S","M","M","S","S","S","S","M","M")
#Example with no interspersed marking and sighting.
Korder=c(rep("M",10),rep("S",10))
tlocs=5
data=sim.genCatSMR.df(N=N,lam0.mark=lam0.mark,lam0.sight=lam0.sight,sigma=sigma,K1=K1,
                      K2=K2,Korder=Korder,X1=X1,X2=X2,buff=buff,obstype=obstype,ncat=ncat,
                      pIDcat=pIDcat,IDcovs=IDcovs,gamma=gamma,pMarkID=pMarkID,pID=pID,tlocs=tlocs)


inits=list(lam0.mark=lam0.mark,lam0.sight=lam0.sight,sigma=sigma,gamma=gamma,psi=0.7)
proppars=list(lam0.mark=c(0.05,0.025),lam0.sight=c(0.09,0.09),sigma=c(0.04,0.045),s=0.45,st=0.45)
M=150
storeLatent=TRUE
storeGamma=FALSE
niter=500
nburn=0
nthin=1
IDup="Gibbs"
out=mcmc.genCatSMR.df(data,niter=niter,nburn=nburn, nthin=nthin, M = M, inits=inits,obstype=obstype,
                      proppars=proppars,storeLatent=TRUE,storeGamma=TRUE,IDup=IDup)

plot(mcmc(out$out))
1-rejectionRate(mcmc(out$out))#shoot for between 0.2 and 0.4 for df parameters
1-rejectionRate(mcmc(out$sxout))#make sure none are <0.1?
length(unique(data$IDum)) #true number of unmarked individuals captured

#####regular generalized SMR with no identity covariates
N=100
lam0.mark=c(0.075,0.01)
lam0.sight=c(0.3,0.2)
sigma=c(0.6,0.5)
K1=10 #number of marking occasions
K2=10 #number of sighting occasions
buff=2 #state space buffer
X1<- expand.grid(3:11,3:11) #marking locations
X2<- expand.grid(3:11+0.5,3:11+0.5) #sighting locations
pMarkID=c(0.8,0.8) #probability of observing marked status of marked and unmarked individuals
pID=0.8 #probability of determining identity of marked individuals
obstype=c("bernoulli","poisson") #observation model of both processes
ncat=1  #just 1 covariate
gamma=IDcovs=vector("list",ncat) 
nlevels=rep(1,ncat) #just 1 value. We have simplified to regular generalized SMR.
for(i in 1:ncat){ 
  gamma[[i]]=rep(1/nlevels[i],nlevels[i])
  IDcovs[[i]]=1:nlevels[i]
}
pIDcat=rep(1,ncat) #category observation probabilities
#Example of interspersed marking and sighting. 
Korder=c("M","M","S","S","S","S","M","M","S","M","M","S","M","M","S","S","S","S","M","M")
#Example with no interspersed marking and sighting.
Korder=c(rep("M",10),rep("S",10))
tlocs=5
data=sim.genCatSMR.df(N=N,lam0.mark=lam0.mark,lam0.sight=lam0.sight,sigma=sigma,K1=K1,
                      K2=K2,Korder=Korder,X1=X1,X2=X2,buff=buff,obstype=obstype,ncat=ncat,
                      pIDcat=pIDcat,IDcovs=IDcovs,gamma=gamma,pMarkID=pMarkID,pID=pID,tlocs=tlocs)


inits=list(lam0.mark=lam0.mark,lam0.sight=lam0.sight,sigma=sigma,gamma=gamma,psi=0.7)
proppars=list(lam0.mark=c(0.05,0.025),lam0.sight=c(0.09,0.09),sigma=c(0.04,0.045),s=0.45,st=0.45)
M=150
storeLatent=TRUE
storeGamma=FALSE
niter=500
nburn=0
nthin=1
IDup="Gibbs"
out=mcmc.genCatSMR.df(data,niter=niter,nburn=nburn, nthin=nthin, M = M, inits=inits,obstype=obstype,
                      proppars=proppars,storeLatent=TRUE,storeGamma=TRUE,IDup=IDup)
plot(mcmc(out$out))

## End(Not run)

benaug/SPIM documentation built on April 28, 2024, 7:27 a.m.