DS {spAbundance} | R Documentation |
Function for Fitting Single-Species Hierarchical Distance Sampling Models
Description
Function for fitting single-sepcies hierarchical distance sampling models
Usage
DS(abund.formula, det.formula, data, inits, priors, tuning,
n.batch, batch.length, accept.rate = 0.43, family = 'Poisson',
transect = 'line', det.func = 'halfnormal',
n.omp.threads = 1, verbose = TRUE,
n.report = 100, n.burn = round(.10 * n.batch * batch.length), n.thin = 1,
n.chains = 1, ...)
Arguments
abund.formula |
a symbolic description of the model to be fit for the abundance portion of the model using R's model syntax. Only right-hand side of formula is specified. See example below. Random intercepts and slopes are allowed using lme4 syntax (Bates et al. 2015). |
det.formula |
a symbolic description of the model to be fit for the detection portion of the model using R's model syntax. Only right-hand side of formula is specified. See example below. Random intercepts and slopes are allowed using lme4 syntax (Bates et al. 2015). |
data |
a list containing data necessary for model fitting.
Valid tags are |
inits |
a list with each tag corresponding to a parameter name.
Valid tags are |
priors |
a list with each tag corresponding to a parameter name.
Valid tags are |
tuning |
a single numeric value representing the initial variance of the
adaptive sampler for |
n.batch |
the number of MCMC batches in each chain to run for the adaptive MCMC sampler. See Roberts and Rosenthal (2009) for details. |
batch.length |
the number of MCMC samples in each batch in each chain to run for the Adaptive MCMC sampler. See Roberts and Rosenthal (2009) for details. |
accept.rate |
target acceptance rate for Adaptive MCMC. Default is 0.43. See Roberts and Rosenthal (2009) for details. |
family |
the distribution to use for the latent abundance process. Currently
supports |
transect |
the type of transect. Currently supports line transects ( |
det.func |
the detection model used to describe how detection probability varies
with distance. In other software, this is often referred to as the key function. Currently
supports two functions: half normal ( |
n.omp.threads |
a positive integer indicating the number of threads
to use for SMP parallel processing. The package must be compiled for
OpenMP support. For most Intel-based machines, we recommend setting
|
verbose |
if |
n.report |
the interval to report MCMC progress. |
n.burn |
the number of samples out of the total |
n.thin |
the thinning interval for collection of MCMC samples. The
thinning occurs after the |
n.chains |
the number of chains to run in sequence. |
... |
currently no additional arguments |
Value
An object of class DS
that is a list comprised of:
beta.samples |
a |
alpha.samples |
a |
kappa.samples |
a |
N.samples |
a |
mu.samples |
a |
sigma.sq.mu.samples |
a |
sigma.sq.p.samples |
a |
beta.star.samples |
a |
alpha.star.samples |
a |
y.rep.samples |
a three-dimensional array of fitted values. Array dimensions correspond to MCMC samples, sites, and distance band. |
pi.samples |
a three-dimensional array of cell-specific detection probabilities. Array dimensions correspond to MCMC samples, sites, and distance band. |
rhat |
a list of Gelman-Rubin diagnostic values for some of the model parameters. |
ESS |
a list of effective sample sizes for some of the model parameters. |
run.time |
execution time reported using |
The return object will include additional objects used for subsequent prediction and/or model fit evaluation.
Author(s)
Jeffrey W. Doser doserjef@msu.edu,
References
Bates, Douglas, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.
Royle, J. A., Dawson, D. K., & Bates, S. (2004). Modeling abundance effects in distance sampling. Ecology, 85(6), 1591-1597.
Examples
set.seed(123)
J.x <- 10
J.y <- 10
J <- J.x * J.y
# Number of distance bins from which to simulate data.
n.bins <- 5
# Length of each bin. This should be of length n.bins
bin.width <- c(.10, .10, .20, .3, .1)
# Abundance coefficients
beta <- c(1.0, 0.2, 0.3, -0.2)
p.abund <- length(beta)
# Detection coefficients
alpha <- c(-1.0, -0.3)
p.det <- length(alpha)
# Detection decay function
det.func <- 'halfnormal'
mu.RE <- list()
p.RE <- list()
sp <- FALSE
family <- 'NB'
kappa <- 0.1
offset <- 1.8
transect <- 'point'
dat <- simDS(J.x = J.x, J.y = J.y, n.bins = n.bins, bin.width = bin.width,
beta = beta, alpha = alpha, det.func = det.func, kappa = kappa,
mu.RE = mu.RE, p.RE = p.RE, sp = sp,
sigma.sq = sigma.sq, phi = phi, nu = nu, family = family,
offset = offset, transect = transect)
y <- dat$y
X <- dat$X
X.re <- dat$X.re
X.p <- dat$X.p
X.p.re <- dat$X.p.re
dist.breaks <- dat$dist.breaks
covs <- cbind(X, X.p)
colnames(covs) <- c('int.abund', 'abund.cov.1', 'abund.cov.2', 'abund.cov.3',
'int.det', 'det.cov.1')
data.list <- list(y = y,
covs = covs,
dist.breaks = dist.breaks,
offset = offset)
# Priors
prior.list <- list(beta.normal = list(mean = 0, var = 10),
alpha.normal = list(mean = 0,
var = 10),
kappa.unif = c(0, 100))
# Starting values
inits.list <- list(alpha = 0,
beta = 0,
kappa = 1)
# Tuning values
tuning <- list(beta = 0.1, alpha = 0.1, beta.star = 0.3, alpha.star = 0.1,
kappa = 0.2)
out <- DS(abund.formula = ~ abund.cov.1 + abund.cov.2 + abund.cov.3,
det.formula = ~ det.cov.1,
data = data.list,
n.batch = 10,
batch.length = 25,
inits = inits.list,
family = 'NB',
det.func = 'halfnormal',
transect = 'point',
tuning = tuning,
priors = prior.list,
accept.rate = 0.43,
n.omp.threads = 1,
verbose = TRUE,
n.report = 100,
n.burn = 100,
n.thin = 1,
n.chains = 1)
summary(out)