Mark-Recapture Distance Sampling

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Documentation for package ‘mrds’ version 3.0.1

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A B C D F G H I K L M N P Q R S T V

mrds-package Mark-Recapture Distance Sampling (mrds)

-- A --

add.df.covar.line Add covariate levels detection function plots
add_df_covar_line Add covariate levels detection function plots
adj.check.order Check order of adjustment terms
adj.cos Cosine adjustment term, not the series.
adj.herm Hermite polynomial adjustment term, not the series.
adj.poly Simple polynomial adjustment term, not the series.
adj.series.grad.cos Series of the gradient of the cosine adjustment series w.r.t. the scaled distance.
adj.series.grad.herm Series of the gradient of the Hermite polynomial adjustment series w.r.t. the scaled distance.
adj.series.grad.poly Series of the gradient of the simple polynomial adjustment series w.r.t. the scaled distance.
AIC.ddf Akaike's An Information Criterion for detection functions
AIC.ds Akaike's An Information Criterion for detection functions
AIC.io Akaike's An Information Criterion for detection functions
AIC.io.fi Akaike's An Information Criterion for detection functions
AIC.rem Akaike's An Information Criterion for detection functions
AIC.rem.fi Akaike's An Information Criterion for detection functions
AIC.trial Akaike's An Information Criterion for detection functions
AIC.trial.fi Akaike's An Information Criterion for detection functions
apex.gamma Get the apex for a gamma detection function
assign.default.values Assign default values to list elements that have not been already assigned
average.line Average detection function line for plotting
average.line.cond Average conditional detection function line for plotting

-- B --

book.tee.data Golf tee data used in chapter 6 of Advanced Distance Sampling examples

-- C --

calc.se.Np Find se of average p and N
cdf.ds Cumulative distribution function (cdf) for fitted distance sampling detection function
cds CDS function definition
check.bounds Check parameters bounds during optimisations
check.mono Check that a detection function is monotone
coef.ds Extract coefficients
coef.io Extract coefficients
coef.io.fi Extract coefficients
coef.rem Extract coefficients
coef.rem.fi Extract coefficients
coef.trial Extract coefficients
coef.trial.fi Extract coefficients
coefficients Extract coefficients
compute.Nht Horvitz-Thompson estimates 1/p_i or s_i/p_i
covered.region.dht Covered region estimate of abundance from Horvitz-Thompson-like estimator
covn Compute empirical variance of encounter rate
create.bins Create bins from a set of binned distances and a set of cutpoints.
create.command.file create.command.file
create.model.frame Create a model frame for ddf fitting
create.varstructure Creates structures needed to compute abundance and variance

-- D --

ddf Distance Detection Function Fitting
ddf.ds CDS/MCDS Distance Detection Function Fitting
ddf.gof Goodness of fit tests for distance sampling models
ddf.io Mark-Recapture Distance Sampling (MRDS) IO - PI
ddf.io.fi Mark-Recapture Distance Sampling (MRDS) IO - FI
ddf.rem Mark-Recapture Distance Sampling (MRDS) Removal - PI
ddf.rem.fi Mark-Recapture Distance Sampling (MRDS) Removal - FI
ddf.trial Mark-Recapture Distance Sampling (MRDS) Trial Configuration - PI
ddf.trial.fi Mark-Recapture Analysis of Trial Configuration - FI
DeltaMethod Numeric Delta Method approximation for the variance-covariance matrix
det.tables Observation detection tables
detfct.fit Fit detection function using key-adjustment functions
detfct.fit.opt Fit detection function using key-adjustment functions
dht Density and abundance estimates and variances
dht.deriv Computes abundance estimates at specified parameter values using Horvitz-Thompson-like estimator
dht.se Variance and confidence intervals for density and abundance estimates
distpdf.grad Gradient of the non-normalised pdf of distances or the detection function for the distances.
ds.function Distance Sampling Functions

-- F --

flnl Log-likelihood computation for distance sampling data
flnl.constr.grad.neg (Negative) gradients of constraint function
flnl.grad Gradient of the negative log likelihood function
flpt.lnl Log-likelihood computation for distance sampling data
flt.var Hessian computation for fitted distance detection function model parameters

-- G --

g0 Compute value of p(0) using a logit formulation
getpar Extraction and assignment of parameters to vector
gof.ds Compute chi-square goodness-of-fit test for ds models
gof.io Goodness of fit tests for distance sampling models
gof.io.fi Goodness of fit tests for distance sampling models
gof.rem Goodness of fit tests for distance sampling models
gof.rem.fi Goodness of fit tests for distance sampling models
gof.trial Goodness of fit tests for distance sampling models
gof.trial.fi Goodness of fit tests for distance sampling models
gstdint Integral of pdf of distances

-- H --

histline Plot histogram line

-- I --

integratedetfct.logistic Integrate a logistic detection function
integratelogistic.analytic Analytically integrate logistic detection function
integratepdf Numerically integrate pdf of observed distances over specified ranges
integratepdf.grad Numerically integrates the non-normalised pdf or the detection function of observed distances over specified ranges.
io.glm Iterative offset GLM/GAM for fitting detection function
is.linear.logistic Collection of functions for logistic detection functions
is.logistic.constant Is a logit model constant for all observations?

-- K --

keyfct.grad.hn The gradient of the half-normal key function
keyfct.grad.hz The gradient of the hazard-rate key function
keyfct.th1 Threshold key function
keyfct.th2 Threshold key function
keyfct.tpn Two-part normal key function

-- L --

lfbcvi Black-capped vireo mark-recapture distance sampling analysis
lfgcwa Golden-cheeked warbler mark-recapture distance sampling analysis
logisticbyx Logistic as a function of covariates
logisticbyz Logistic as a function of distance
logisticdetfct Logistic detection function
logisticdupbyx Logistic for duplicates as a function of covariates
logisticdupbyx_fast Logistic for duplicates as a function of covariates (fast)
logit Logit function
logLik.ddf log-likelihood value for a fitted detection function
logLik.ds log-likelihood value for a fitted detection function
logLik.io log-likelihood value for a fitted detection function
logLik.io.fi log-likelihood value for a fitted detection function
logLik.rem log-likelihood value for a fitted detection function
logLik.rem.fi log-likelihood value for a fitted detection function
logLik.trial log-likelihood value for a fitted detection function
logLik.trial.fi log-likelihood value for a fitted detection function

-- M --

MCDS Run MCDS.exe as a backend for mrds
mcds MCDS function definition
MCDS.exe Run MCDS.exe as a backend for mrds
mcds_dot_exe Run MCDS.exe as a backend for mrds
mrds Mark-Recapture Distance Sampling (mrds)
mrds_opt Tips on optimisation issues in 'mrds' models

-- N --

NCovered Compute estimated abundance in covered (sampled) region
NCovered.ds Compute estimated abundance in covered (sampled) region
NCovered.io Compute estimated abundance in covered (sampled) region
NCovered.io.fi Compute estimated abundance in covered (sampled) region
NCovered.rem Compute estimated abundance in covered (sampled) region
NCovered.rem.fi Compute estimated abundance in covered (sampled) region
NCovered.trial Compute estimated abundance in covered (sampled) region
NCovered.trial.fi Compute estimated abundance in covered (sampled) region
nlminb_wrapper Wrapper around 'nlminb'

-- P --

p.det Double-platform detection probability
p.dist.table Distribution of probabilities of detection
parse.optimx Parse optimx results and present a nice object
pdot.dsr.integrate.logistic Compute probability that a object was detected by at least one observer
plot.det.tables Observation detection tables
plot.ds Plot fit of detection functions and histograms of data from distance sampling model
plot.io Plot fit of detection functions and histograms of data from distance sampling independent observer ('io') model
plot.io.fi Plot fit of detection functions and histograms of data from distance sampling independent observer model with full independence ('io.fi')
plot.rem Plot fit of detection functions and histograms of data from removal distance sampling model
plot.rem.fi Plot fit of detection functions and histograms of data from removal distance sampling model
plot.trial Plot fit of detection functions and histograms of data from distance sampling trial observer model
plot.trial.fi Plot fit of detection functions and histograms of data from distance sampling trial observer model
plot_cond Plot conditional detection function from distance sampling model
plot_layout Layout for plot methods in mrds
plot_uncond Plot unconditional detection function from distance sampling model
predict Predictions from 'mrds' models
predict.ddf Predictions from 'mrds' models
predict.ds Predictions from 'mrds' models
predict.io Predictions from 'mrds' models
predict.io.fi Predictions from 'mrds' models
predict.rem Predictions from 'mrds' models
predict.rem.fi Predictions from 'mrds' models
predict.trial Predictions from 'mrds' models
predict.trial.fi Predictions from 'mrds' models
print.ddf Simple pretty printer for distance sampling analyses
print.ddf.gof Prints results of goodness of fit tests for detection functions
print.det.tables Print results of observer detection tables
print.dht Prints density and abundance estimates
print.p_dist_table Print distribution of probabilities of detection
print.summary.ds Print summary of distance detection function model object
print.summary.io Print summary of distance detection function model object
print.summary.io.fi Print summary of distance detection function model object
print.summary.rem Print summary of distance detection function model object
print.summary.rem.fi Print summary of distance detection function model object
print.summary.trial Print summary of distance detection function model object
print.summary.trial.fi Print summary of distance detection function model object
prob.deriv Derivatives for variance of average p and average p(0) variance
prob.se Average p and average p(0) variance
process.data Process data for fitting distance sampling detection function
pronghorn Pronghorn aerial survey data from Wyoming
ptdata.distance Single observer point count data example from Distance
ptdata.dual Simulated dual observer point count data
ptdata.removal Simulated removal observer point count data
ptdata.single Simulated single observer point count data
p_dist_table Distribution of probabilities of detection

-- Q --

qqplot.ddf Quantile-quantile plot and goodness of fit tests for detection functions

-- R --

rem.glm Iterative offset model fitting of mark-recapture with removal model
rescale_pars Calculate the parameter rescaling for parameters associated with covariates

-- S --

sample_ddf Generate data from a fitted detection function and refit the model
setbounds Set parameter bounds
setcov Creates design matrix for covariates in detection function
sethazard Set initial values for detection function based on distance sampling
setinitial.ds Set initial values for detection function based on distance sampling
sim.mix Simulation of distance sampling data via mixture models Allows one to simulate line transect distance sampling data using a mixture of half-normal detection functions.
solvecov Invert of covariance matrices
stake77 Wooden stake data from 1977 survey
stake78 Wooden stake data from 1978 survey
summary.ds Summary of distance detection function model object
summary.io Summary of distance detection function model object
summary.io.fi Summary of distance detection function model object
summary.rem Summary of distance detection function model object
summary.rem.fi Summary of distance detection function model object
summary.trial Summary of distance detection function model object
summary.trial.fi Summary of distance detection function model object
survey.region.dht Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region

-- T --

test.breaks Test validity for histogram breaks(cutpoints)
two-part-normal Two-part normal key function

-- V --

varn Compute empirical variance of encounter rate