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 |