distsamp: Fit the hierarchical distance sampling model of Royle et al....

In ianfiske/unmarked: Models for Data from Unmarked Animals

Description

Fit the hierarchical distance sampling model of Royle et al. (2004) to line or point transect data recorded in discrete distance intervals.

Usage

distsamp(formula, data, keyfun=c("halfnorm", "exp",
    "hazard", "uniform"), output=c("density", "abund"),
    unitsOut=c("ha", "kmsq"), starts, method="BFGS",
    control=list(), se=TRUE)

Arguments

formula	Double right-hand formula describing detection covariates followed by abundance covariates. ~1 ~1 would be a null model.
data	object of class unmarkedFrameDS, containing response matrix, covariates, distance interval cut points, survey type ("line" or "point"), transect lengths (for survey = "line"), and units ("m" or "km") for cut points and transect lengths. See example for set up.
keyfun	One of the following detection functions: "halfnorm", "hazard", "exp", or "uniform." See details.
output	Model either "density" or "abund"
unitsOut	Units of density. Either "ha" or "kmsq" for hectares and square kilometers, respectively.
starts	Vector of starting values for parameters.
method	Optimization method used by optim.
control	Other arguments passed to optim.
se	logical specifying whether or not to compute standard errors.

Details

Unlike conventional distance sampling, which uses the 'conditional on detection' likelihood formulation, this model is based upon the unconditional likelihood and thus allows for modeling both abundance and detection function parameters.

The latent transect-level abundance distribution f(N | theta) is currently assumed to be Poisson with mean lambda.

The detection process is modeled as multinomial: y_ij ~ Multinomial(N_i, pi_i1, pi_i2, ..., pi_iJ), where pi_ij is the multinomial cell probability for transect i in distance class j. These are computed based upon a detection function g(x | sigma), such as the half-normal, negative exponential, or hazard rate.

Parameters lambda and sigma can be vectors affected by transect-specific covariates using the log link.

Value

unmarkedFitDS object (child class of unmarkedFit-class) describing the model fit.

Note

You cannot use obsCovs.

Author(s)

Richard Chandler [e-mail]

References

Royle, J. A., D. K. Dawson, and S. Bates (2004) Modeling abundance effects in distance sampling. Ecology 85, pp. 1591-1597.

See Also

unmarkedFit-class fitList, formatDistData, parboot, sight2perpdist, detFuns. Also look at vignette("distsamp").

Examples

## Line transect examples

data(linetran)

ltUMF <- with(linetran, {
	unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4), 
	siteCovs = data.frame(Length, area, habitat), 
	dist.breaks = c(0, 5, 10, 15, 20),
	tlength = linetran$Length * 1000, survey = "line", unitsIn = "m")
	})

ltUMF
summary(ltUMF)
hist(ltUMF)

# Half-normal detection function. Density output (log scale). No covariates. 
(fm1 <- distsamp(~ 1 ~ 1, ltUMF))

# Some methods to use on fitted model
summary(fm1)
backTransform(fm1, type="state")       # animals / ha
exp(coef(fm1, type="state", altNames=TRUE))     # same 
backTransform(fm1, type="det")         # half-normal SD
hist(fm1, xlab="Distance (m)")	# Only works when there are no detection covars

# Halfnormal. Covariates affecting both density and and detection.  
(fm2 <- distsamp(~area + habitat ~ habitat, ltUMF))

# Hazard-rate detection function.
(fm3 <- distsamp(~ 1 ~ 1, ltUMF, keyfun="hazard"))

# Plot detection function.
fmhz.shape <- exp(coef(fm3, type="det"))
fmhz.scale <- exp(coef(fm3, type="scale"))
plot(function(x) gxhaz(x, shape=fmhz.shape, scale=fmhz.scale), 0, 25, 
	xlab="Distance (m)", ylab="Detection probability")

## Point transect example

## Not run: 
data(pointtran)

ptUMF <- with(pointtran, {
	unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4, dc5), 
	siteCovs = data.frame(area, habitat), 
	dist.breaks = seq(0, 25, by=5), survey = "point", unitsIn = "m")
	})

# Half-normal.
(fmp1 <- distsamp(~ 1 ~ 1, ptUMF))
hist(fmp1, ylim=c(0, 0.07), xlab="Distance (m)")

## End(Not run)

ianfiske/unmarked documentation built on May 18, 2019, 1:28 a.m.