dht  R Documentation 
Compute density and abundance estimates and variances based on HorvitzThompsonlike estimator.
dht(
model,
region.table,
sample.table,
obs.table = NULL,
subset = NULL,
se = TRUE,
options = list()
)
model 
ddf model object 
region.table 

sample.table 

obs.table 

subset 
subset statement to create 
se 
if 
options 
a list of options that can be set, see " 
Density and abundance within the sampled region is computed based on a
HorvitzThompsonlike estimator for groups and individuals (if a clustered
population) and this is extrapolated to the entire survey region based on
any defined regional stratification. The variance is based on replicate
samples within any regional stratification. For clustered populations,
E(s)
and its standard error are also output.
Abundance is estimated with a HorvitzThompsonlike estimator (Huggins 1989,
1991; Borchers et al 1998; Borchers and Burnham 2004). The abundance in the
sampled region is simply 1/p_1 + 1/p_2 + ... + 1/p_n
where p_i
is the estimated detection probability for the i
th detection of
n
total observations. It is not strictly a HorvitzThompson estimator
because the p_i
are estimated and not known. For animals observed in
tight clusters, that estimator gives the abundance of groups
(group=TRUE
in options
) and the abundance of individuals is
estimated as s_1/p_1 + s_2/p_2 + ... + s_n/p_n
, where s_i
is the
size (e.g., number of animals in the group) of each observation
(group=FALSE
in options
).
Extrapolation and estimation of abundance to the entire survey region is
based on either a random sampling design or a stratified random sampling
design. Replicate samples (lines) are specified within regional strata
region.table
, if any. If there is no stratification,
region.table
should contain only a single record with the Area
for the entire survey region. The sample.table
is linked to the
region.table
with the Region.Label
. The obs.table
is
linked to the sample.table
with the Sample.Label
and
Region.Label
. Abundance can be restricted to a subset (e.g., for a
particular species) of the population by limiting the list the observations
in obs.table
to those in the desired subset. Alternatively, if
Sample.Label
and Region.Label
are in the data.frame
used to fit the model, then a subset
argument can be given in place
of the obs.table
. To use the subset
argument but include all
of the observations, use subset=1==1
to avoid creating an
obs.table
.
In extrapolating to the entire survey region it is important that the unit
measurements be consistent or converted for consistency. A conversion factor
can be specified with the convert.units
variable in the
options
list. The values of Area
in region.table
, must
be made consistent with the units for Effort
in sample.table
and the units of distance
in the data.frame
that was analyzed.
It is easiest to do if the units of Area
is the square of the units
of Effort
and then it is only necessary to convert the units of
distance
to the units of Effort
. For example, if Effort
was entered in kilometres and Area
in square kilometres and
distance
in metres then using
options=list(convert.units=0.001)
would convert metres to kilometres,
density would be expressed in square kilometres which would then be
consistent with units for Area
. However, they can all be in different
units as long as the appropriate composite value for convert.units
is
chosen. Abundance for a survey region can be expressed as: A*N/a
where A
is Area
for the survey region, N
is the
abundance in the covered (sampled) region, and a
is the area of the
sampled region and is in units of Effort * distance
. The sampled
region a
is multiplied by convert.units
, so it should be
chosen such that the result is in the same units of Area
. For
example, if Effort
was entered in kilometres, Area
in hectares
(100m x 100m) and distance
in metres, then using
options=list(convert.units=10)
will convert a
to units of
hectares (100 to convert metres to 100 metres for distance and .1 to convert
km to 100m units).
The argument options
is a list of variable=value
pairs that
set options for the analysis. All but two of these have been described above.
pdelta
should not need to be changed but was included for
completeness. It controls the precision of the first derivative calculation
for the delta method variance. If the option areas.supplied
is
TRUE
then the covered area is assumed to be supplied in the
CoveredArea
column of the sample data.frame
.
list object of class dht
with elements:
clusters 
result list for object clusters 
individuals 
result list for individuals 
Expected.S 

The list structure of clusters and individuals are the same:
bysample 

summary 

N 

D 

average.p 
average detection probability estimate 
cormat 
correlation matrix of regional abundance/density estimates and total (if more than one region) 
vc 
list of 3: total variancecovariance matrix, detection function component of variance and encounter rate component of variance. For detection the vc matrix and partial vector are returned 
Nhat.by.sample 
another summary of 
If the argument se=TRUE
, standard errors for density and abundance is
computed. Coefficient of variation and lognormal confidence intervals are
constructed using a Satterthwaite approximation for degrees of freedom
(Buckland et al. 2001 p. 90). The function dht.se
computes the
variance and interval estimates.
The variance has two components:
variation due to uncertainty from estimation of the detection function parameters;
variation in abundance due to random sample selection;
The first component (model parameter uncertainty) is computed using a delta
method estimate of variance (Huggins 1989, 1991, Borchers et al. 1998) in
which the first derivatives of the abundance estimator with respect to the
parameters in the detection function are computed numerically (see
DeltaMethod
).
The second component (encounter rate variance) can be computed in one of
several ways depending on the form taken for the encounter rate and the
estimator used. To begin with there three possible values for varflag
to calculate encounter rate:
0
uses a binomial variance for the number of observations
(equation 13 of Borchers et al. 1998). This estimator is only useful if the
sampled region is the survey region and the objects are not clustered; this
situation will not occur very often;
1
uses the encounter rate n/L
(objects observed per unit
transect) from Buckland et al. (2001) pg 7879 (equation 3.78) for line
transects (see also Fewster et al, 2009 estimator R2). This variance
estimator is not appropriate if size
or a derivative of size
is used in the detection function;
2
is the default and uses the encounter rate estimator
\hat{N}/L
(estimated abundance per unit transect) suggested by Innes
et al (2002) and Marques & Buckland (2004).
In general if any covariates are used in the models, the default
varflag=2
is preferable as the estimated abundance will take into
account variability due to covariate effects. If the population is clustered
the mean group size and standard error is also reported.
For options 1
and 2
, it is then possible to choose one of the
estimator forms given in Fewster et al (2009) for line transects:
"R2"
, "R3"
, "R4"
, "S1"
, "S2"
,
"O1"
, "O2"
or "O3"
by specifying the ervar=
option (default "R2"
). For points estimator "P3"
is the only
option. See varn
and Fewster et al (2009) for further details
on these estimators.
dht
optionsSeveral options are available to control calculations and output:
ci.width
Confidence interval width, expressed as a decimal
between 0 and 1 (default 0.95
, giving a 95% CI)
pdelta
delta value for computing numerical first derivatives (Default: 0.001)
varflag
0,1,2 (see "Uncertainty") (Default: 2
)
convert.units
multiplier for width to convert to units of
length (Default: 1
)
ervar
encounter rate variance type (see "Uncertainty" and
type
argument of varn
). (Default: "R2"
for
lines and "P3"
for points)
Jeff Laake, David L Miller
Borchers, D.L., S.T. Buckland, P.W. Goedhart, E.D. Clarke, and S.L. Hedley. 1998. HorvitzThompson estimators for doubleplatform line transect surveys. Biometrics 54: 12211237.
Borchers, D.L. and K.P. Burnham. General formulation for distance sampling pp 1011 In: Advanced Distance Sampling, eds. S.T. Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.
Buckland, S.T., D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. 2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. Oxford University Press.
Fewster, R.M., S.T. Buckland, K.P. Burnham, D.L. Borchers, P.E. Jupp, J.L. Laake and L. Thomas. 2009. Estimating the encounter rate variance in distance sampling. Biometrics 65: 225236.
Huggins, R.M. 1989. On the statistical analysis of capture experiments. Biometrika 76:133140.
Huggins, R.M. 1991. Some practical aspects of a conditional likelihood approach to capture experiments. Biometrics 47: 725732.
Innes, S., M.P. HeideJorgensen, J.L. Laake, K.L. Laidre, H.J. Cleator, P. Richard, and R.E.A. Stewart. 2002. Surveys of belugas and narwhals in the Canadian High Arctic in 1996. NAMMCO Scientific Publications 4: 169190.
Marques, F.F.C. and S.T. Buckland. 2004. Covariate models for the detection function. In: Advanced Distance Sampling, eds. S.T. Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.
print.dht dht.se
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