Simulation-class {dsims}

Class "Simulation"

Description

Class "Simulation" is an S4 class containing descriptions of the region, population, survey design and analyses the user wishes to investigate. Once the simulation has been run the N.D.Estimates will contain multiple estimates of abundance and density obtained by repeatedly generating populations, simulating the survey and completing the analyses.

Slots

reps

Object of class "numeric"; the number of times the simulation should be repeated.

single.transect.set

Object of class "logical"; if TRUE the same set of transects are used in each repetition.

design

Object of class "Survey.Design"; the survey design.

population.description

Object of class "Population.Description"; the population.description.

detectability

Object of class "Detectability"; a description of the detectability of the population.

ds.analysis

Object of class "DS.Analysis"

add.options

a list to expand simulation options in the future.

ddf.param.ests

Object of class "array"; stores the parameters associated with the detection function.

results

A "list" with elements 'individuals' (and optionally 'clusters' and 'expected.size') as well as 'Detection'.

The 'individuals' and 'clusters' elements are a list of three 3-dimensional arrays. The first is a summary array containing values for 'Area' (strata area), 'CoveredArea' (the area covered in the strata by the survey), Effort' (the line length or number of points surveyed), 'n' (the number of sightings), 'n.miss.dists' (the number of missing distances - only applicable to mixed detector types and not yet implemented in dsims), 'k' (the number of transects), 'ER' (encounter rate), 'se.ER' (standard error of the encounter rate), 'cv.ER' (coefficient of variation of the encounter rate). A value is provided for each of these for each strata as well as the region as a whole and for each simulation repetition as well as storing the mean and standard deviation of these values across simulation repetitions.

The second array 'N' is the abundance estimates table. It contains values for the 'Estimate' (estimated abundance based on data from iteration i), 'se' (standard error associated with the estimate), 'cv' (coefficient of variation of estimate), 'lcl' (lower 95% confidence interval value), 'ucl' (upper 95% confidence interval value), 'df' the degrees of freedom associated with the estimate. A value is provided for each of these for each strata as well as the region as a whole and for each simulation repetition as well as storing the mean and standard deviation of these values across simulation repetitions.

The third array 'D' is the density estimates table. It contains values for the 'Estimate' (estimated density based on data from iteration i), 'se' (standard error associated with the estimate), 'cv' (coefficient of variation of estimate), 'lcl' (lower 95% confidence interval value), 'ucl' (upper 95% confidence interval value), 'df' the degrees of freedom associated with the estimate. A value is provided for each of these for each strata as well as the region as a whole and for each simulation repetition as well as storing the mean and standard deviation of these values across simulation repetitions.

When animals occur in clusters the expected.size element of the results list contains a 3-dimensional array. It gives values for 'Expected.S' (expected cluster size), 'se.Expected.S' (the standard error of the expected cluster size), 'cv.Expected.S' (the coefficient of variation for the expected cluster size). Values are given for each analysis strata as well as a value for the survey region as a whole and across each simulation repetition as well as overall means and standard deviations across repetitions.

The Detection element of the results list is a 3-dimensional array with values for 'True.Pa' (the proportion of animals in the covered region which were detected), 'Pa' (the estimated proportion of animals detected in the covered region), 'ESW' (the estimated strip width), 'f(0)' (The estimated value of the detection function pdf at distance 0), 'SelectedModel' (the index of the model which had the best fit to the dataset for the repetition), 'DeltaCriteria' (the difference in information criteria between the best and second best fitting models where two or more models were fitted and converged), 'SuccessfulModels' (the number of models which successfully converged). Currently detection functions are pooled across all strata so there is only one global value for each simulated dataset as well as a mean value and standard deviation where appropriate.

warnings

A "list" to store warnings and error messages encountered during runtime.

Methods

summary

signature=(object = "Simulation"): produces a summary of the simulation and its results.

generate.population

signature = (object = "Simulation"): generates a single instance of a population.

generate.transects

signature = (object = "Simulation"): generates a single set of transects.

run.survey

signature = (object = "Simulation"): carries out the simulation process as far as generating the distance data and returns an object containing the population, transects and data.

run.simulation

signature = (simulation = "Simulation"): runs the whole simulation for the specified number of repetitions.

See Also

make.simulation