Description Usage Arguments Details Value Author(s) See Also Examples
Perform automated classical detection function selection and estimation of abundance.
1 2 3 4 5 6  autoDistSamp(formula, detectionData, siteData, w.lo = 0, w.hi = NULL,
likelihoods = c("halfnorm", "hazrate", "uniform", "negexp", "Gamma"),
series = c("cosine", "hermite", "simple"), expansions = 0:3,
pointSurvey = FALSE, warn = TRUE, area = 1, ci = 0.95, R = 500,
bySite = FALSE, plot.bs = FALSE, showProgress = TRUE,
plot = TRUE, criterion = "AICc", ...)

formula 
This parameter is passed to 
detectionData 
This parameter is passed to 
siteData 
This parameter is passed to 
w.lo 
This parameter is passed to 
w.hi 
This parameter is passed to 
likelihoods 
Vector of strings specifying the likelihoods to consider during model selection. Valid values at present are "uniform", "halfnorm", "hazrate", "negexp", and "Gamma". See Details for the models this routine considers. 
series 
Vector of series types to consider during model selection. Valid values are 'simple', 'hermite', and 'cosine'. See Details for the models this routine considers. 
expansions 
Vector of the number of expansion terms to consider during model selection. Valid values are 0 through 3. See Details for the models this routine considers. Note, expansion terms are not currently allowed in models with covariates. 
pointSurvey 
This parameter is passed to 
warn 
This parameter is passed to 
area 
This parameter is passed to 
ci 
This parameter is passed to 
R 
This parameter is passed to 
bySite 
This parameter is passed to 
plot.bs 
Logical for whether to plot bootstrap
iterations after the top model has been selected and
during final estimation of confidence intervals.
This parameter is passed unchanged to 
showProgress 
Logical for whether to
suppress intermediate output. If 
plot 
Logical scalar specifying whether to plot models during model selection.
If 
criterion 
A string specifying the criterion to use when assessing model fit.
The best fitting model from this routine is the one with lowest value
of this fit criterion. This must be one of "AICc" (the default),
"AIC", or "BIC". See 
... 
Additional parameters passed to 
During model selection, each series and number of expansions is crossed with
each of the likelihoods. For example, if likelihoods
has 3 elements,
series
has 2 elements, and expansions
has 4 elements,
the total number of models fitted is 3 (likelihoods) * 2 (series) * 4 (expansions)
= 24 models. The default specification fits 41 detection functions
from the "halfnorm", "hazrate", "uniform", "negexp", and "Gamma" likelihoods
(note that Gamma does not currently implement expansions, see
Gamma.like
). Note, expansion terms are not currently
allowed in models with covariates. The model with lowest AIC is selected
as 'best', and estimation of abundance proceeds using that model.
Suppress all intermediate output using plot.bs=FALSE
,
showProgress=FALSE
, and plot=FALSE
.
If bySite==FALSE
, an 'abundance estimate' object is returned.
See abundEstim
and dfuncEstim
for an explanation of components.
Returned abundance estimates are based
on the best fitting distance function among those fitted.
A fit table, sorted by the criterion, is returned as component
$fitTable
. The fit table component contains columns
like
(likelihood), series
, expansions
,
converge
(0=converged,1=not), scale
(1=passed scale
check,0=did not pass), and
aic
(the criterion used).
If bySite==TRUE
, a data frame containing sitelevel
abundance based on the bestfitting detection function is returned.
See abundEstim
for description of columns in
the data frame. The bestfitting likelihood form, series,
and number of expansions are returned as attributes of the
data frame (e.g., bestfitting likelihood is attr(out,"like.form")
).
Trent McDonald, WEST Inc., tmcdonald@westinc.com
Aidan McDonald, WEST Inc., aidan@mcdcentral.org
Jason Carlisle, University of Wyoming and WEST Inc., jcarlisle@westinc.com
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # Load example sparrow data (line transect survey type)
data(sparrowDetectionData)
data(sparrowSiteData)
# Automate fitting multiple detection functions, and estimate abundance
# (density per ha in this case), given the 'best' detection function
# Note, area=10000 converts to density per ha (for distances measured in m)
# Note, users should do more than R=20 iterations of the bootstrap
autoDistSamp(formula=dist ~ 1,
detectionData=sparrowDetectionData, siteData=sparrowSiteData,
likelihood=c("halfnorm", "hazrate"), w.hi=100,
series=c("cosine", "simple"), expansions=c(0, 1),
area=10000, R=20, ci=0.95, bySite=FALSE,
plot.bs=TRUE, plot=FALSE, pointSurvey=FALSE)

Rdistance (version 2.1.3)
Likelihood Series Expans Converged? Scale? AICc
halfnorm cosine 0 Yes Ok 2970.6052
halfnorm cosine 1 Yes Ok 2970.6774
halfnorm simple 1 Yes Ok 2970.6186
hazrate cosine 0 Yes Ok 2972.5328
hazrate cosine 1 Yes Ok 2972.7198
hazrate simple 1 Yes Ok 2973.0355
Computing bootstrap confidence interval on N...

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 Abundance Estimate Based on TopRanked Detection Function 
Call: dfuncEstim(formula = formula, detectionData = detectionData,
siteData = siteData, likelihood = fit.table$like[1], pointSurvey =
pointSurvey, w.lo = w.lo, w.hi = w.hi, expansions =
fit.table$expansions[1], series = fit.table$series[1])
Coefficients:
Estimate SE z p(>z)
Sigma 46.3587 2.549913 18.1805 7.365789e74
Convergence: Success
Function: HALFNORM
Strip: 0 to 100
Effective strip width (ESW): 56.30084
Probability of detection: 0.5630084
Scaling: g(0) = 1
Log likelihood: 1484.297
AICc: 2970.605
Abundance estimate: 0.8634171 ; 95% CI=( 0.7595291 to 1.025431 )
Call: dfuncEstim(formula = formula, detectionData = detectionData,
siteData = siteData, likelihood = fit.table$like[1], pointSurvey =
pointSurvey, w.lo = w.lo, w.hi = w.hi, expansions =
fit.table$expansions[1], series = fit.table$series[1])
Coefficients:
Estimate SE z p(>z)
Sigma 46.3587 2.549913 18.1805 7.365789e74
Convergence: Success
Function: HALFNORM
Strip: 0 to 100
Effective strip width (ESW): 56.30084
Probability of detection: 0.5630084
Scaling: g(0) = 1
Log likelihood: 1484.297
AICc: 2970.605
Abundance estimate: 0.8634171 ; 95% CI=( 0.7595291 to 1.025431 )
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