posteriorSummaryOfDetection: Posterior Summary of a Multi-scale Occupancy Model's...

In RobertDorazio/eDNAoccupancy: Multi-scale Occupancy Modeling of Environmental DNA Data

Posterior Summary of a Multi-scale Occupancy Model's Detection Probabilities

Description

Estimates the posterior mean, median, and 95% credible limits for a multi-scale occupancy model's sample-specific detection probabilities.

Usage

posteriorSummaryOfDetection(fit, burnin = 1, mcError = FALSE)

Arguments

fit	object of class occModel that contains data and previous state of the model's Markov chain
burnin	initial no. iterations of Markov chain to be omitted from calculations
mcError	logical switch to estimate Monte Carlo standard errors

Details

This function estimates the posterior mean, median, and 95% credible limits of the sample-specific detection probabilities of a multi-scale occupancy model.

Value

Computes estimates of summary statistics for a multi-scale occupancy model's sample-specific detection probabilities. If mcError=TRUE, the Monte Carlo standard errors of these estimates are computed. All posterior summaries are returned in a list.

Examples

data(gobyDetectionData)
detections = occData(gobyDetectionData, "site", "sample")
data(gobySurveyData)
gobySurveyData = scaleData(gobySurveyData)  # center and scale numeric covariates

fit1 = occModel(formulaSite          = ~ veg,
                formulaSiteAndSample = ~ sal + twg,
                formulaReplicate     = ~ sal + fish,
                detectionMats        = detections,
                siteData             = gobySurveyData,
                niter                = 1100,
                niterInterval        = 100,
                siteColName = 'site',
                )

p = posteriorSummaryOfSampleOccupancy(fit1, burnin=100)
plot(gobySurveyData[, 'sal'], p$median[,1])

RobertDorazio/eDNAoccupancy documentation built on Sept. 5, 2023, 9:57 a.m.