Distance-Sampling Analyses for Density and Abundance Estimation


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Documentation for package ‘Rdistance’ version 2.1.3

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Rdistance-package Rdistance - Distance Sampling Analyses for Abundance Estimation 'Rdistance' contains functions and associated routines to analyze distance-sampling data collected on point or line transects. Some of 'Rdistance"s features include: • Accommodation of both point and line transect analyses in one routine ('dfuncEstim'). • Regression-like formula for inclusion of covariate in distance functions ('dfuncEstim'). • Automatic bootstrap confidence intervals ('abundEstim'). • Availability of both study-area and site-level abundance estimates ('abundEstim'). • Classical, parametric distance functions ('halfnorm.like', 'hazrate.like'), and expansion functions ('cosine.expansion', 'hermite.expansion', 'simple.expansion'). • Non-classic distance functions ('Gamma.like', 'negexp.like', 'uniform.like') and a non-parametric smoother 'dfuncSmu'). • User defined distance functions. • Automated distance function fits and selection 'autoDistSamp'. • Extended vignettes. • 'print', 'plot', 'predict', 'coef', and 'summary' methods for distance function objects and abundance classes.
abundEstim Estimate abundance from distance-sampling data
AIC.dfunc AICc and related fit statistics for detection function objects
autoDistSamp Automated classical distance analysis
coef.dfunc Coefficients of an estimated detection function
cosine.expansion calculation of cosine expansion for detection function likelihoods
dfuncEstim Estimate a detection function from distance-sampling data
dfuncSmu Estimate a non-parametric smooth detection function from distance-sampling data
distance Rdistance - Distance Sampling Analyses for Abundance Estimation 'Rdistance' contains functions and associated routines to analyze distance-sampling data collected on point or line transects. Some of 'Rdistance"s features include: • Accommodation of both point and line transect analyses in one routine ('dfuncEstim'). • Regression-like formula for inclusion of covariate in distance functions ('dfuncEstim'). • Automatic bootstrap confidence intervals ('abundEstim'). • Availability of both study-area and site-level abundance estimates ('abundEstim'). • Classical, parametric distance functions ('halfnorm.like', 'hazrate.like'), and expansion functions ('cosine.expansion', 'hermite.expansion', 'simple.expansion'). • Non-classic distance functions ('Gamma.like', 'negexp.like', 'uniform.like') and a non-parametric smoother 'dfuncSmu'). • User defined distance functions. • Automated distance function fits and selection 'autoDistSamp'. • Extended vignettes. • 'print', 'plot', 'predict', 'coef', and 'summary' methods for distance function objects and abundance classes.
EDR Effective Detection Radius (EDR) for estimated detection functions with point transects
effectiveDistance Calculates the effective sampling distance for estimated detection functions
estimateN Abundance point estimates
ESW Effective Strip Width for line transect data
F.double.obs.prob Compute double observer probability of detection (No external covariates allowed)
F.gx.estim Estimate g(0) or g(x)
F.maximize.g Find the coordinate of the maximum of a distance function
F.nLL Return the negative log likelihood for a set of distance values
F.start.limits Set starting values and limits for parameters of Rdistance functions
Gamma.like Gamma distance function for distance analyses
getDfuncModelFrame Return model frame for dfunc
halfnorm.like Half-normal likelihood function for distance analyses
hazrate.like Hazard rate likelihood function for distance analyses
hermite.expansion Calculation of Hermite expansion for detection function likelihoods
integration.constant Compute the integration constant for distance density functions
likeParamNames Likelihood parameter names
line-transect Rdistance - Distance Sampling Analyses for Abundance Estimation 'Rdistance' contains functions and associated routines to analyze distance-sampling data collected on point or line transects. Some of 'Rdistance"s features include: • Accommodation of both point and line transect analyses in one routine ('dfuncEstim'). • Regression-like formula for inclusion of covariate in distance functions ('dfuncEstim'). • Automatic bootstrap confidence intervals ('abundEstim'). • Availability of both study-area and site-level abundance estimates ('abundEstim'). • Classical, parametric distance functions ('halfnorm.like', 'hazrate.like'), and expansion functions ('cosine.expansion', 'hermite.expansion', 'simple.expansion'). • Non-classic distance functions ('Gamma.like', 'negexp.like', 'uniform.like') and a non-parametric smoother 'dfuncSmu'). • User defined distance functions. • Automated distance function fits and selection 'autoDistSamp'. • Extended vignettes. • 'print', 'plot', 'predict', 'coef', and 'summary' methods for distance function objects and abundance classes.
negexp.like Negative exponential distance function for distance analyses
perpDists Compute off-transect distances from sighting distances and angles
plot.dfunc Plot a distance (detection) function
point-transect Rdistance - Distance Sampling Analyses for Abundance Estimation 'Rdistance' contains functions and associated routines to analyze distance-sampling data collected on point or line transects. Some of 'Rdistance"s features include: • Accommodation of both point and line transect analyses in one routine ('dfuncEstim'). • Regression-like formula for inclusion of covariate in distance functions ('dfuncEstim'). • Automatic bootstrap confidence intervals ('abundEstim'). • Availability of both study-area and site-level abundance estimates ('abundEstim'). • Classical, parametric distance functions ('halfnorm.like', 'hazrate.like'), and expansion functions ('cosine.expansion', 'hermite.expansion', 'simple.expansion'). • Non-classic distance functions ('Gamma.like', 'negexp.like', 'uniform.like') and a non-parametric smoother 'dfuncSmu'). • User defined distance functions. • Automated distance function fits and selection 'autoDistSamp'. • Extended vignettes. • 'print', 'plot', 'predict', 'coef', and 'summary' methods for distance function objects and abundance classes.
predict.dfunc Predict method for dfunc objects
print.abund Print abundance estimates
print.dfunc Print a distance function object
Rdistance Rdistance - Distance Sampling Analyses for Abundance Estimation 'Rdistance' contains functions and associated routines to analyze distance-sampling data collected on point or line transects. Some of 'Rdistance"s features include: • Accommodation of both point and line transect analyses in one routine ('dfuncEstim'). • Regression-like formula for inclusion of covariate in distance functions ('dfuncEstim'). • Automatic bootstrap confidence intervals ('abundEstim'). • Availability of both study-area and site-level abundance estimates ('abundEstim'). • Classical, parametric distance functions ('halfnorm.like', 'hazrate.like'), and expansion functions ('cosine.expansion', 'hermite.expansion', 'simple.expansion'). • Non-classic distance functions ('Gamma.like', 'negexp.like', 'uniform.like') and a non-parametric smoother 'dfuncSmu'). • User defined distance functions. • Automated distance function fits and selection 'autoDistSamp'. • Extended vignettes. • 'print', 'plot', 'predict', 'coef', and 'summary' methods for distance function objects and abundance classes.
RdistanceControls Control parameters for 'Rdistance' optimization.
secondDeriv Numeric second derivatives
simple.expansion Calculate simple polynomial expansion for detection function likelihoods
smu.like Smoothed likelihood function for distance analyses
sparrowDetectionData Brewer's Sparrow detection data (line-transect survey) 'Rdistance' contains four example datasets: two collected using a line-transect survey (i.e., 'sparrowDetectionData' and 'sparrowSiteData') and two collected using a point-transect (sometimes called a point count) survey (i.e., 'thrasherDetectionData' and 'thrasherSiteData'). These datasets demonstrate the type and format of input data required by 'Rdistance' to estimate a detection function and abundance from distance sampling data collected by surveying line transects or point transects. They also allow the user to step through the tutorials described in the package vignettes. Only the detection data is needed to fit a detection function (if there are no covariates in the detection function; see 'dfuncEstim'), but both detection and the additional site data are needed to estimate abundance (or to include site-level covariates in the detection function; see 'abundEstim'). Line transect (sparrow) data come from 72 transects, each 500 meters long, surveyed for Brewer's Sparrows by the Wyoming Cooperative Fish & Wildlife Research Unit in 2012. Point transect (thrasher) data come from 120 points surveyed for Sage Thrashers by the Wyoming Cooperative Fish & Wildlife Research Unit in 2013. See the package vignettes for 'Rdistance' tutorials using these datasets.
sparrowSiteData Brewer's Sparrow site data (line-transect survey) 'Rdistance' contains four example datasets: two collected using a line-transect survey (i.e., 'sparrowDetectionData' and 'sparrowSiteData') and two collected using a point-transect (sometimes called a point count) survey (i.e., 'thrasherDetectionData' and 'thrasherSiteData'). These datasets demonstrate the type and format of input data required by 'Rdistance' to estimate a detection function and abundance from distance sampling data collected by surveying line transects or point transects. They also allow the user to step through the tutorials described in the package vignettes. Only the detection data is needed to fit a detection function (if there are no covariates in the detection function; see 'dfuncEstim'), but both detection and the additional site data are needed to estimate abundance (or to include site-level covariates in the detection function; see 'abundEstim'). Line transect (sparrow) data come from 72 transects, each 500 meters long, surveyed for Brewer's Sparrows by the Wyoming Cooperative Fish & Wildlife Research Unit in 2012. Point transect (thrasher) data come from 120 points surveyed for Sage Thrashers by the Wyoming Cooperative Fish & Wildlife Research Unit in 2013. See the package vignettes for 'Rdistance' tutorials using these datasets.
thrasherDetectionData Sage Thrasher detection data (point-transect survey) 'Rdistance' contains four example datasets: two collected using a line-transect survey (i.e., 'sparrowDetectionData' and 'sparrowSiteData') and two collected using a point-transect (sometimes called a point count) survey (i.e., 'thrasherDetectionData' and 'thrasherSiteData'). These datasets demonstrate the type and format of input data required by 'Rdistance' to estimate a detection function and abundance from distance sampling data collected by surveying line transects or point transects. They also allow the user to step through the tutorials described in the package vignettes. Only the detection data is needed to fit a detection function (if there are no covariates in the detection function; see 'dfuncEstim'), but both detection and the additional site data are needed to estimate abundance (or to include site-level covariates in the detection function; see 'abundEstim'). Line transect (sparrow) data come from 72 transects, each 500 meters long, surveyed for Brewer's Sparrows by the Wyoming Cooperative Fish & Wildlife Research Unit in 2012. Point transect (thrasher) data come from 120 points surveyed for Sage Thrashers by the Wyoming Cooperative Fish & Wildlife Research Unit in 2013. See the package vignettes for 'Rdistance' tutorials using these datasets.
thrasherSiteData Sage Thrasher site data (point-transect survey) 'Rdistance' contains four example datasets: two collected using a line-transect survey (i.e., 'sparrowDetectionData' and 'sparrowSiteData') and two collected using a point-transect (sometimes called a point count) survey (i.e., 'thrasherDetectionData' and 'thrasherSiteData'). These datasets demonstrate the type and format of input data required by 'Rdistance' to estimate a detection function and abundance from distance sampling data collected by surveying line transects or point transects. They also allow the user to step through the tutorials described in the package vignettes. Only the detection data is needed to fit a detection function (if there are no covariates in the detection function; see 'dfuncEstim'), but both detection and the additional site data are needed to estimate abundance (or to include site-level covariates in the detection function; see 'abundEstim'). Line transect (sparrow) data come from 72 transects, each 500 meters long, surveyed for Brewer's Sparrows by the Wyoming Cooperative Fish & Wildlife Research Unit in 2012. Point transect (thrasher) data come from 120 points surveyed for Sage Thrashers by the Wyoming Cooperative Fish & Wildlife Research Unit in 2013. See the package vignettes for 'Rdistance' tutorials using these datasets.
uniform.like Uniform likelihood function for distance analyses