SightabilityPopR: R function that interfaces with the SightabilityModel package...

View source: R/SightabilityPopR.R

SightabilityPopRR Documentation

R function that interfaces with the SightabilityModel package and gives similar functionality as the AerialSurvey program


A stratified random sample of blocks in a survey area is conducted. In each block, groups of moose are observed (usually through an aerial survey). For each group of moose, the number of moose is recorded along with attributes such as sex or age.

The SightabilityPopR() function adjusts for sightability < 100%.


  density = NULL,
  abundance = NULL,
  numerator = NULL,
  denominator = NULL,
  sight.formula = observed ~ 1,
  sight.beta = 10,
  sight.beta.cov = matrix(0, nrow = 1, ncol = 1),
  sight.logCI = TRUE,
  sight.var.method = c("Wong", "SS")[1], = "Block.ID",
  block.area.var = "Block.Area",
  stratum.var = "Stratum",
  stratum.blocks.var = "Stratum.Blocks",
  stratum.area.var = "Stratum.Area",
  conf.level = 0.9


A data frame containing counts of moose in each group along with a variable identifying the stratum (see stratum.var) and block (see


A data frame containing for each block, the block id (see, the area of the block (see block.area.var). The data frame can contain information for other blocks that were not surveyed (e.g. for the entire population of blocks) and information from these additional blocks will be ignored.

A data frame containing for each stratum, the stratum id (see stratum.var), the total number of blocks in the stratum (see stratum.blocks.var) and the total area of the stratum (see stratum.area.var)

density, abundance, numerator, denominator

Right-handed formula identifying the variable(s) in the data frame for which the density, abundance, or ratio (numerator/denominator) are to be estimated.


A formula that identifies the model used to estimate sightability. For example observed ~ VegCoverClass would indicate that sightability is a function of the VegCoverClass variable in the survey data. The left hand variable is arbitrary. The right hand variables must be present in the data frame.


The vector of estimated coefficients for the logistic regression sightability model.


The covariance matrix of sight.beta


Should confidence intervals for the sightability adjusted estimates be computed using a normal-based confidence interval on log(abundance)


What method should be used to estimate the variances after adjusting for sightability.

Name of the variable in the data frames that identifies the (the sampling unit)


Name of the variable in data frames that contains the area of the blocks (area of sampling unit)


Name of the variable in the data frames that identifies the classical stratum


Name of the variable in the data frame that contains the total number of blocks in the stratum.


Name of the variable in the data.frame that contains the total stratum area.


Confidence level used to create confidence intervals.


A data frame containing for each stratum and for all strata (identified as stratum id .OVERALL), the density, or abundance or ratio estimate along with its estimated standard error and large-sample normal-based confidence interval. Additional information on the components of variance is also reported.


Schwarz, C. J.


To Be Added.


##---- See the vignettes for examples on how to run this analysis.

SightabilityModel documentation built on March 7, 2023, 6:15 p.m.