sampleMME: Determine the optimal number of surveys to acheive a...

In jmcalabrese/mmeDetect: Calculate the probability of detection of a mass mortality event

Description

Given a detectability model, rate parameter, maximum possible sampling effort, and target probability of dection, this function determines the value of n that comes closest to acheiving the user-specified target detection probability.

Usage

sampleMME(lambda, nMax, pTar, mod='Exp')

Arguments

lambda	For the exponential detection model, lambda is decay rate in detectability. For the Laplace model, lambda is both the increase rate up to peak detectability, and the decay rate thereafter.
nMax	The maximum possible number of regularly spaced surveys that could be conducted.
pTar	The target detection probability the user wishes to acheive.
mod	The detectability model.

Details

The growth/decay rate, lambda, must be >0.

nMax represents the maximum possible sampling effort and must be user determined, for example based on logistical considerations. This number must be >= 2, and can be large, but must be finite. For more information about how sampling schedules are determined from n, the total number of observations, see the help file for pDetect().

The user must also specify the target probability of MME detection that they wish to acheive, pTar, where 0 <= pTar <= 1.

Allowable values for mod include 'Exp' for the exponential model, and 'Lpl' for the Laplace model.

Value

A dataframe

nOpt	the number of regularly spaced observations that comes closest to acheiving pTar.
pDet	The realized probability of MME detection for nOpt surveys

Justin M. Calabrese

jmcalabrese/mmeDetect documentation built on May 21, 2019, 1:44 p.m.