In crushing05/BayesCorrOcc: Fit Bayesian Correlated Occupancy Model

library(ggplot2)
library(mapdata)
library(maps)

knitr::opts_chunk$set(fig.width=12, fig.height=8, 
                      echo=FALSE, warning=FALSE, message=FALSE)

opts_knit$set(upload.fun = image_uri)

Summary report for: r spp_summ$spp_name

Overview of BBS data

Number of routes: r spp_summ$n.routes

Number of buffered routes: r spp_summ$n.buffer

BayesCorrOcc::PlotRoutes(spp_summ$spp_alpha)

Figure 1: BBS routes included in analysis. Red points indicate routes with at least one detection during the time period, grey points indicate routes with no detections but that were included in the analysis. Circled routes are likely outliers

Results

kable(spp_summ$g.tab, "markdown", align="c", padding=2)

Table 1: Indicator variable posterior means for 10 climate covariates included in the initial occupancy ($\psi$), colonization ($\gamma$), and extinction ($\epsilon$) models

   

kable(spp_summ$psi.betas, "markdown", align="c", padding=2)

Table 2: Estimated coefficients (and 95% credible intervals) for the probability of occupancy ($\psi$), the probability of colonization ($\gamma$), & the probability of extinction ($\epsilon$). All coefficients are on the logit scale

 

 

kable(spp_summ$p.betas, "markdown", align="c", padding=2)

Table 3: Estimated coefficients (and 95% credible intervals) for the detection model and the spatial correlation terms ($\theta$ & $\theta'$). All coefficients are on the logit scale

Occupancy probability

BayesCorrOcc::MapDiff(alpha = spp_summ$spp_alpha)

Figure 2: Probability of occupancy and difference in occupancy probability betwen the first and last years included in the analysis

 

 

BayesCorrOcc::PlotLat(alpha = spp_summ$spp_alpha)

Figure 3: Annual change in latitude indices. Solid line shows estimated mean breeding latitude in each year. Long dash lines show the estimated 25th and 75th occupancy percentiles (i.e., core breeding range). Short dash lines show the estimated 5th and 95th occupancy percentiles (i.e., range limits)

 

 

BayesCorrOcc::PlotLon(alpha = spp_summ$spp_alpha)

Figure 4: Annual change in mean breeding longitudinal

BayesCorrOcc::PostCheck(alpha = spp_summ$spp_alpha)

Figure 5: Posterior predictive check. Bayesian P-value = r round(spp_summ$p, digits = 2)

crushing05/BayesCorrOcc documentation built on May 9, 2019, 8:33 a.m.