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\name{gdistsamp} | ||
\alias{gdistsamp} | ||
\title{ | ||
Fit the generalized distance sampling model of Chandler et al. (2011). | ||
Fit the generalized distance sampling model of Chandler et al. (2011). | ||
} | ||
\description{ | ||
Extends the distance sampling model of Royle et al. (2004) to estimate | ||
the probability of being available for detection. Also allows abundance | ||
to be modeled using the negative binomial distribution. | ||
Extends the distance sampling model of Royle et al. (2004) to estimate | ||
the probability of being available for detection. Also allows | ||
abundance to be modeled using the negative binomial and zero-inflated | ||
Poisson distributions. | ||
} | ||
\usage{ | ||
gdistsamp(lambdaformula, phiformula, pformula, data, keyfun = | ||
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@@ -72,49 +73,87 @@ starts, method = "BFGS", se = TRUE, engine=c("C","R"), rel.tol=1e-4, threads=1, | |
bounds} | ||
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} | ||
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\details{ | ||
This model extends the model of Royle et al. (2004) by estimating the | ||
probability of being available for detection \eqn{\phi}{phi}. This | ||
effectively relaxes the assumption that \eqn{g(0)=1}. In other words, | ||
inividuals at a distance of 0 are not assumed to be detected with | ||
certainty. To estimate this additional parameter, replicate distance | ||
sampling data must be collected at each transect. Thus the data are | ||
collected at i = 1, 2, ..., R transects on t = 1, 2, ..., T | ||
occassions. As with the model of Royle et al. (2004), the detections | ||
must be binned into distance classes. These data must be formatted in | ||
a matrix with R rows, and JT columns where J is the number of distance | ||
classses. See \code{\link{unmarkedFrameGDS}} for more information. | ||
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Extends the model of Royle et al. (2004) by estimating the probability | ||
of being available for detection \eqn{\phi}{phi}. To estimate this | ||
additional parameter, replicate distance sampling data must be | ||
collected at each transect. Thus the data are collected at i = 1, 2, | ||
..., R transects on t = 1, 2, ..., T occassions. As with the model of | ||
Royle et al. (2004), the detections must be binned into distance | ||
classes. These data must be formatted in a matrix with R rows, and JT | ||
columns where J is the number of distance classses. See | ||
\code{\link{unmarkedFrameGDS}} for more information about data | ||
formatting. | ||
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The definition of availability depends on the context. The model is | ||
\deqn{M_i \sim \text{Pois}(\lambda)}{M(i)~Pois(lambda)} | ||
\deqn{N_{i,t} \sim \text{Bin}(M_i, \phi)}{N(i,t)~Bin(M(i), phi)} | ||
\deqn{y_{i,1,t}, \dots, y_{i,J,t} \sim \text{Multinomial}(N_{i,t}, | ||
\pi_{i,1,t}, \dots, \pi_{i,J,t})}{y(i,1,t), ..., y(i,J,t) ~ | ||
Multinomial(N(i,t), pi(i,1,t), ..., pi(i,J,t))} | ||
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If there is no movement, then \eqn{M_i}{M(i)} is local abundance, and | ||
\eqn{N_{i,t}}{N(i,t)} is the number of individuals that are available | ||
to be detected. In this case, \eqn{\phi=g_0}{phi=g(0)}. Animals might | ||
be missed on the transect line because they are difficult to see or | ||
detected. This relaxes the assumption of conventional distance | ||
sampling that \eqn{g_0=1}{g(0)=1}. | ||
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However, when there is movement in the form of temporary emigration, | ||
local abundance is \eqn{N_{i,t}}{N(i,t)}; it's the fraction of | ||
\eqn{M_i}{M(i)} that are on the plot at time t. In this case, | ||
\eqn{\phi}{phi} is the temporary emigration parameter, and we need to | ||
assume that \eqn{g_0=1}{g(0)=1} in order to interpret | ||
\eqn{N_{i,t}}{N(i,t)} as local abundance. See Chandler et al. (2011) | ||
for an analysis of the model under this form of temporary emigration. | ||
If there is movement and \eqn{g_0<1}{g(0)<1} then it | ||
isn't possible to estimate local abundance at time t. In this case, | ||
\eqn{M_i}{M(i)} would be the total number of individuals that ever use | ||
plot i (the super-population), and \eqn{N_{i,t}}{N(i,t)} would be the | ||
number available to be detected at time t. Since a fraction of the | ||
unavailable individuals could be off the plot, and another fraction | ||
could be on the plot, it isn't possible to infer local abundance and | ||
density during occasion t. | ||
} | ||
\note{ | ||
If you aren't interested in estimating phi, but you want to | ||
use the negative binomial distribution, simply set numPrimary=1 when | ||
formatting the data. | ||
} | ||
If you aren't interested in estimating \eqn{\phi}{phi}, but you want | ||
to use the negative binomial or ZIP distributions, set numPrimary=1 | ||
when formatting the data. | ||
} | ||
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\value{ | ||
An object of class unmarkedFitGDS. | ||
} | ||
An object of class unmarkedFitGDS. | ||
} | ||
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\references{ | ||
Royle, J. A., D. K. Dawson, and S. Bates. 2004. Modeling | ||
abundance effects in distance sampling. \emph{Ecology} | ||
85:1591-1597. | ||
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Chandler, R. B, J. A. Royle, and D. I. King. 2011. Inference about | ||
Royle, J. A., D. K. Dawson, and S. Bates. 2004. Modeling abundance | ||
effects in distance sampling. \emph{Ecology} 85:1591-1597. | ||
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Chandler, R. B, J. A. Royle, and D. I. King. 2011. Inference about | ||
density and temporary emigration in unmarked | ||
populations. \emph{Ecology} 92:1429--1435. | ||
} | ||
populations. \emph{Ecology} 92:1429--1435. | ||
} | ||
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\author{ | ||
Richard Chandler \email{[email protected]} | ||
} | ||
Richard Chandler \email{rbchan@uga.edu} | ||
} | ||
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\note{ | ||
You cannot use obsCovs, but you can use yearlySiteCovs (a confusing name | ||
since this model isn't for multi-year data. It's just a hold-over | ||
from the colext methods of formatting data upon which it is based.) | ||
} | ||
You cannot use obsCovs, but you can use yearlySiteCovs (a confusing | ||
name since this model isn't for multi-year data. It's just a hold-over | ||
from the colext methods of formatting data upon which it is based.) | ||
} | ||
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\seealso{ | ||
\code{\link{distsamp}} | ||
} | ||
\code{\link{distsamp}} | ||
} | ||
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\examples{ | ||
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