# Single Species, Multi Season Occupancy analyais
# Nighingale detection..
# 55 sites x 10 years x 8 visits/site
# No covariates
# 2018-11-15 Code contributed by Carl James Schwarz (cschwarz.stat.sfu.cs@gmail.com)
# RPresence package
library(readxl)
library(RPresence)
library(ggplot2)
# Get the RPResence additional functions
source(file.path("..","..","..","AdditionalFunctions","Rpresence.additional.functions.R"))
# get the data read in
# Data for detections should be a data frame with rows corresponding to sites
# and columns to visits.
# The usual 1=detected; 0=not detected; NA=not visited is used.
input.data <- read.csv(file.path("..","nightingales.csv"), header=TRUE, as.is=TRUE, strip.white=TRUE)
head(input.data)
## Site V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19
## 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1
## 2 2 0 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0
## 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 4 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 5 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 6 6 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 1 0 0 1
## V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37
## 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0
## 2 0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 6 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
## V38 V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55
## 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 1 0 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 1 0 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0
## V56 V57 V58 V59 V60 V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71 V72 V73
## 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0
## 2 0 0 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0
## V74 V75 V76 V77 V78 V79 V80
## 1 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 1 0 0 1 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 1 1 1 1 0 0
input.history <- input.data[, -1]
head(input.history)
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1
## 2 0 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0
## 6 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 1 0 0 1 1
## V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38
## 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1
## 2 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1
## 6 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1
## V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 1 0 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 1
## V57 V58 V59 V60 V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71 V72 V73 V74
## 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0
## 2 0 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0
## V75 V76 V77 V78 V79 V80
## 1 1 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 1 0 0 1 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 1 1 1 1 0 0
# do some basic checks on your data
# e.g. check number of sites; number of visits etc
nrow(input.history)
## [1] 55
ncol(input.history)
## [1] 80
range(input.history, na.rm=TRUE)
## [1] 0 1
sum(is.na(input.history))
## [1] 0
# site covariates - none.
#site.covar <- NULL
# Number of visits in each season
Nvisits.per.season <- rep(8,10)
# Create the *.pao file
nighingale.pao <- RPresence::createPao(input.history,
nsurveyseason=Nvisits.per.season,
#unitcov=site.covar, # no site covariates
title='Nighingale SSMS')
nighingale.pao
## $nunits
## [1] 55
##
## $nsurveys
## [1] 80
##
## $nseasons
## [1] 10
##
## $nmethods
## [1] 1
##
## $det.data
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1
## 2 0 1 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0
## 6 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 1 0 0 1 1
## 7 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 1 1
## 8 0 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 0 0
## 9 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1
## 10 1 1 1 1 1 0 1 1 0 0 0 0 0 1 0 0 1 1 1 1
## 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 12 1 1 1 1 1 1 0 1 0 0 0 0 0 1 0 1 0 1 1 1
## 13 1 0 1 1 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0
## 14 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1
## 15 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 16 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1
## 18 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 19 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1 1
## 20 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 21 0 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 1 1
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0
## 24 0 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1
## 25 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 1 1 1
## 26 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1
## 27 0 1 1 1 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1
## 28 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1
## 29 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1
## 30 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 33 1 1 0 1 1 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1
## 34 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 35 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 1 1
## 36 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1
## 37 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 1
## 38 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1
## 39 0 1 1 1 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0
## 40 1 1 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1
## 41 1 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 42 0 1 1 1 1 0 0 0 0 1 1 1 0 1 0 0 1 0 1 1
## 43 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 45 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 46 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 49 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 50 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1
## 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 52 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0
## 53 0 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 1 1
## 54 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1
## 55 0 1 0 1 1 0 0 0 0 1 0 1 0 0 0 1 1 1 1 1
## V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38
## 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1
## 2 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1
## 6 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1
## 7 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 9 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 10 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 1 1
## 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 12 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1
## 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 14 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 15 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 1 1 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 19 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 0
## 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 1 1 1 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0
## 22 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 25 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 26 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0
## 27 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 0 1
## 28 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 0
## 29 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 1
## 33 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 34 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1
## 35 1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 1 1 1
## 36 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 0 1
## 37 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0
## 38 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0
## 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 40 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1
## 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 42 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1
## 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 45 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 49 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 0 0
## 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 52 1 1 1 1 1 1 1 0 1 0 0 0 0 1 1 0 1 1
## 53 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
## 54 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
## 55 1 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 1 0
## V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 1 0 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 1
## 7 1 0 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1 0
## 8 1 0 1 1 1 1 0 1 0 0 0 0 1 1 0 0 1 0
## 9 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 1 1
## 10 0 0 1 1 0 1 0 1 0 0 1 1 1 1 0 1 1 1
## 11 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1
## 12 0 0 0 0 1 0 0 1 0 1 1 0 1 1 0 0 1 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 19 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1
## 20 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0
## 21 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0
## 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 27 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0
## 28 1 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0
## 29 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 31 1 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0
## 32 1 0 0 0 1 0 1 0 0 1 0 1 1 1 1 1 1 0
## 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 34 1 0 0 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0
## 35 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 1 0 1
## 36 1 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0
## 37 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1
## 38 1 0 0 0 1 1 1 1 1 1 0 1 1 1 0 1 0 0
## 39 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 40 1 0 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 1
## 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 42 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
## 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 44 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 1 0 0 0 1 1 0 1 0 1 0 1 1 1 0 0 0 0
## 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 52 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 53 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0
## 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 55 0 0 1 1 1 0 1 0 0 0 0 1 1 1 0 1 0 0
## V57 V58 V59 V60 V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71 V72 V73 V74
## 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0
## 2 0 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0
## 7 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0
## 8 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 1
## 9 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 0 0 1
## 10 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1
## 11 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1
## 12 0 1 1 1 0 0 0 0 1 0 1 1 1 1 1 0 1 1
## 13 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1
## 14 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1
## 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 20 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1
## 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 33 0 1 1 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0
## 34 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
## 35 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 0 0 0
## 36 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0
## 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 39 0 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1
## 40 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 42 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0
## 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 44 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 46 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 48 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 52 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1
## 53 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0
## 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V75 V76 V77 V78 V79 V80
## 1 1 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 1 0 0 1 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## 6 1 1 1 1 0 0
## 7 0 0 0 0 0 0
## 8 1 0 0 0 0 0
## 9 0 0 1 1 0 0
## 10 1 1 0 1 0 0
## 11 1 0 1 0 1 0
## 12 1 0 0 0 0 0
## 13 1 0 0 1 1 1
## 14 1 1 0 0 0 0
## 15 0 0 0 0 0 0
## 16 0 0 0 0 0 0
## 17 0 0 0 0 0 0
## 18 1 1 0 0 0 1
## 19 0 0 0 0 0 0
## 20 0 0 0 0 0 0
## 21 0 0 0 0 0 0
## 22 0 0 0 0 0 0
## 23 0 0 0 0 0 0
## 24 0 0 0 0 0 0
## 25 1 0 1 1 1 0
## 26 0 0 0 0 0 0
## 27 0 0 0 0 0 0
## 28 1 1 0 1 1 1
## 29 0 0 0 0 0 0
## 30 0 0 1 1 0 1
## 31 0 0 0 0 0 0
## 32 0 0 0 0 0 0
## 33 0 0 0 0 0 0
## 34 0 0 1 1 0 0
## 35 1 1 0 0 0 0
## 36 0 0 0 0 0 0
## 37 1 0 0 1 0 0
## 38 0 0 0 0 0 0
## 39 0 1 1 1 0 0
## 40 0 0 0 0 0 0
## 41 0 1 1 1 1 0
## 42 0 0 0 0 0 0
## 43 0 0 0 0 0 0
## 44 0 0 0 0 0 0
## 45 0 0 0 0 0 0
## 46 0 0 0 0 0 0
## 47 0 1 0 1 0 1
## 48 0 0 0 0 0 0
## 49 0 0 0 0 0 0
## 50 0 0 0 0 0 0
## 51 1 0 1 1 0 0
## 52 1 1 1 1 0 0
## 53 0 0 0 0 0 0
## 54 0 0 0 0 0 0
## 55 0 0 0 0 0 0
##
## $nunitcov
## [1] 1
##
## $unitcov
## TEMP
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## 6 6
## 7 7
## 8 8
## 9 9
## 10 10
## 11 11
## 12 12
## 13 13
## 14 14
## 15 15
## 16 16
## 17 17
## 18 18
## 19 19
## 20 20
## 21 21
## 22 22
## 23 23
## 24 24
## 25 25
## 26 26
## 27 27
## 28 28
## 29 29
## 30 30
## 31 31
## 32 32
## 33 33
## 34 34
## 35 35
## 36 36
## 37 37
## 38 38
## 39 39
## 40 40
## 41 41
## 42 42
## 43 43
## 44 44
## 45 45
## 46 46
## 47 47
## 48 48
## 49 49
## 50 50
## 51 51
## 52 52
## 53 53
## 54 54
## 55 55
##
## $nsurvcov
## [1] 1
##
## $survcov
## SURVEY
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
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##
## $nsurveyseason
## [1] 8 8 8 8 8 8 8 8 8 8
##
## $title
## [1] "Nighingale SSMS"
##
## $unitnames
## [1] "unit1" "unit2" "unit3" "unit4" "unit5" "unit6" "unit7"
## [8] "unit8" "unit9" "unit10" "unit11" "unit12" "unit13" "unit14"
## [15] "unit15" "unit16" "unit17" "unit18" "unit19" "unit20" "unit21"
## [22] "unit22" "unit23" "unit24" "unit25" "unit26" "unit27" "unit28"
## [29] "unit29" "unit30" "unit31" "unit32" "unit33" "unit34" "unit35"
## [36] "unit36" "unit37" "unit38" "unit39" "unit40" "unit41" "unit42"
## [43] "unit43" "unit44" "unit45" "unit46" "unit47" "unit48" "unit49"
## [50] "unit50" "unit51" "unit52" "unit53" "unit54" "unit55"
##
## $surveynames
## [1] "1-1" "1-2" "1-3" "1-4" "1-5" "1-6" "1-7" "1-8" "2-1" "2-2"
## [11] "2-3" "2-4" "2-5" "2-6" "2-7" "2-8" "3-1" "3-2" "3-3" "3-4"
## [21] "3-5" "3-6" "3-7" "3-8" "4-1" "4-2" "4-3" "4-4" "4-5" "4-6"
## [31] "4-7" "4-8" "5-1" "5-2" "5-3" "5-4" "5-5" "5-6" "5-7" "5-8"
## [41] "6-1" "6-2" "6-3" "6-4" "6-5" "6-6" "6-7" "6-8" "7-1" "7-2"
## [51] "7-3" "7-4" "7-5" "7-6" "7-7" "7-8" "8-1" "8-2" "8-3" "8-4"
## [61] "8-5" "8-6" "8-7" "8-8" "9-1" "9-2" "9-3" "9-4" "9-5" "9-6"
## [71] "9-7" "9-8" "10-1" "10-2" "10-3" "10-4" "10-5" "10-6" "10-7" "10-8"
##
## $paoname
## [1] "pres.pao"
##
## $frq
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## attr(,"class")
## [1] "pao"
summary(nighingale.pao)
## paoname=pres.pao
## title=Nighingale SSMS
## Naive occ=1
## nunits nsurveys nseasons
## "55" "80" "10"
## nsurveyseason nmethods nunitcov
## "8,8,8,8,8,8,8,8,8,8" "1" "1"
## nsurvcov
## "1"
## unit covariates : TEMP
## survey covariates: SURVEY
# Define the models.
# model.type do.1 is dynamic occupancy first parameterization
# do.4 is dynamic occupancy 4th parameterization (random occupancy)
# Random occupancy are fit using type="do.4" in the call.
# Parameters are psi, p with gamma=1-epsilon enforced internally
model.list.csv <- textConnection("
p, psi, gamma, epsilon, model.type
~1, ~1, ~1, ~1, do.1
~SEASON, ~1, ~1, ~1, do.1
~SEASON, ~1, ~SEASON, ~SEASON, do.1")
model.list <- read.csv(model.list.csv, header=TRUE, as.is=TRUE, strip.white=TRUE)
model.list
## p psi gamma epsilon model.type
## 1 ~1 ~1 ~1 ~1 do.1
## 2 ~SEASON ~1 ~1 ~1 do.1
## 3 ~SEASON ~1 ~SEASON ~SEASON do.1
# fit the model
model.fits <- plyr::alply(model.list, 1, function(x,detect.pao){
cat("\n\n***** Starting ", unlist(x), "\n")
if(x$model.type == 'do.1'){
fit <- RPresence::occMod(model=list(as.formula(paste("psi",x$psi)),
as.formula(paste("p" ,x$p )),
as.formula(paste("gamma",x$gamma)),
as.formula(paste("epsilon",x$epsilon))),
data=detect.pao,type="do.1")
}
if(x$model.type == 'do.4'){
fit <- RPresence::occMod(model=list(as.formula(paste("psi",x$psi)),
as.formula(paste("p" ,x$p ))),
data=detect.pao,type="do.4")
}
fit <- RPresence.add.derived(fit)
fit
},detect.pao=nighingale.pao)
##
##
## ***** Starting ~1 ~1 ~1 ~1 do.1
## PRESENCE Version 2.12.21.
## Loading required package: plyr
##
##
## ***** Starting ~SEASON ~1 ~1 ~1 do.1
## PRESENCE Version 2.12.21.
##
##
## ***** Starting ~SEASON ~1 ~SEASON ~SEASON do.1
## PRESENCE Version 2.12.21.
# Look at output from a specified model
model.number <- 2
names(model.fits[[model.number]])
## [1] "modname" "model" "dmat" "data" "outfile"
## [6] "neg2loglike" "aic" "npar" "beta" "real"
## [11] "derived" "gof" "warnings" "version"
names(model.fits[[model.number]]$real)
## [1] "psi" "gamma" "epsilon" "p" "theta" "th0pi"
model.fits[[model.number]]$beta
## $psi
## psi.coeff
## 1 1.417196
##
## $psi.VC
## [1] 0.119573
##
## $gamma
## gamma.coeff
## 1 -1.126984
##
## $gamma.VC
## [1] 0.025656
##
## $epsilon
## epsilon.coeff
## 1 -0.921775
##
## $epsilon.VC
## [1] 0.020099
##
## $p
## p.coeff
## 1 0.033009
## 2 -1.191609
## 3 1.009219
## 4 1.124369
## 5 -0.282558
## 6 -0.306108
## 7 -0.001993
## 8 -1.118839
## 9 -0.094218
## 10 -0.115418
##
## $p.VC
## D1 D2 D3 D4 D5 D6 D7
## D1 0.011893 -0.011875 -0.011892 -0.011892 -0.011893 -0.011891 -0.011890
## D2 -0.011875 0.029880 0.011875 0.011875 0.011874 0.011889 0.011893
## D3 -0.011892 0.011875 0.030425 0.011892 0.011893 0.011891 0.011890
## D4 -0.011892 0.011875 0.011892 0.041774 0.011893 0.011891 0.011890
## D5 -0.011893 0.011874 0.011893 0.011893 0.029769 0.011895 0.011890
## D6 -0.011891 0.011889 0.011891 0.011891 0.011895 0.028955 0.011895
## D7 -0.011890 0.011893 0.011890 0.011890 0.011890 0.011895 0.035836
## D8 -0.011882 0.012007 0.011882 0.011882 0.011882 0.011906 0.011974
## D9 -0.011891 0.011887 0.011891 0.011891 0.011891 0.011892 0.011892
## D10 -0.011892 0.011885 0.011892 0.011892 0.011893 0.011893 0.011893
## D8 D9 D10
## D1 -0.011882 -0.011891 -0.011892
## D2 0.012007 0.011887 0.011885
## D3 0.011882 0.011891 0.011892
## D4 0.011882 0.011891 0.011892
## D5 0.011882 0.011891 0.011893
## D6 0.011906 0.011892 0.011893
## D7 0.011974 0.011892 0.011893
## D8 0.052647 0.011962 0.011903
## D9 0.011962 0.036838 0.011896
## D10 0.011903 0.011896 0.035771
##
## $VC
## A1 B1 C1 D1 D2 D3 D4
## A1 0.119573 -0.000550 -0.000154 -0.001505 0.001460 0.001505 0.001505
## B1 -0.000550 0.025656 0.001467 0.000218 0.000521 -0.000219 -0.000218
## C1 -0.000154 0.001467 0.020099 0.000061 0.001150 -0.000061 -0.000060
## D1 -0.001505 0.000218 0.000061 0.011893 -0.011875 -0.011892 -0.011892
## D2 0.001460 0.000521 0.001150 -0.011875 0.029880 0.011875 0.011875
## D3 0.001505 -0.000219 -0.000061 -0.011892 0.011875 0.030425 0.011892
## D4 0.001505 -0.000218 -0.000060 -0.011892 0.011875 0.011892 0.041774
## D5 0.001506 -0.000292 -0.000048 -0.011893 0.011874 0.011893 0.011893
## D6 0.001501 -0.000110 0.000161 -0.011891 0.011889 0.011891 0.011891
## D7 0.001499 -0.000056 0.000228 -0.011890 0.011893 0.011890 0.011890
## D8 0.001478 0.000418 0.002112 -0.011882 0.012007 0.011882 0.011882
## D9 0.001502 -0.000149 0.000144 -0.011891 0.011887 0.011891 0.011891
## D10 0.001505 -0.000301 0.000153 -0.011892 0.011885 0.011892 0.011892
## D5 D6 D7 D8 D9 D10
## A1 0.001506 0.001501 0.001499 0.001478 0.001502 0.001505
## B1 -0.000292 -0.000110 -0.000056 0.000418 -0.000149 -0.000301
## C1 -0.000048 0.000161 0.000228 0.002112 0.000144 0.000153
## D1 -0.011893 -0.011891 -0.011890 -0.011882 -0.011891 -0.011892
## D2 0.011874 0.011889 0.011893 0.012007 0.011887 0.011885
## D3 0.011893 0.011891 0.011890 0.011882 0.011891 0.011892
## D4 0.011893 0.011891 0.011890 0.011882 0.011891 0.011892
## D5 0.029769 0.011895 0.011890 0.011882 0.011891 0.011893
## D6 0.011895 0.028955 0.011895 0.011906 0.011892 0.011893
## D7 0.011890 0.011895 0.035836 0.011974 0.011892 0.011893
## D8 0.011882 0.011906 0.011974 0.052647 0.011962 0.011903
## D9 0.011891 0.011892 0.011892 0.011962 0.036838 0.011896
## D10 0.011893 0.011893 0.011893 0.011903 0.011896 0.035771
names(model.fits[[model.number]]$derived)
## [1] "psi" "all_psi" "lambda" "lambdap"
model.fits[[model.number]]$derived$psi[1:10,]
## est se lower_0.95 upper_0.95
## unit1_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit2_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit3_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit4_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit5_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit6_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit7_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit8_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit9_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit10_2 0.6235722 0.03478336 0.5533713 0.6889414
model.fits[[model.number]]$real$gamma[1:5,]
## est se lower_0.95 upper_0.95
## gamma1_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma1_unit2 0.2447181 0.02960531 0.1914028 0.3072416
## gamma1_unit3 0.2447181 0.02960531 0.1914028 0.3072416
## gamma1_unit4 0.2447181 0.02960531 0.1914028 0.3072416
## gamma1_unit5 0.2447181 0.02960531 0.1914028 0.3072416
model.fits[[model.number]]$real$epsilon[1:5,]
## est se lower_0.95 upper_0.95
## epsilon1_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon1_unit2 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon1_unit3 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon1_unit4 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon1_unit5 0.2845964 0.02886474 0.2315391 0.3443634
# Estimate of initial occupance
model.fits[[model.number]]$real$psi[grepl('unit1_', row.names(model.fits[[model.number]]$real$psi)),]
## est se lower_0.95 upper_0.95
## unit1_1 0.8048985 0.05430232 0.6768764 0.8904102
# Derived parameters - estimated occupancy for each unit in years 2....
names(model.fits[[model.number]]$derived)
## [1] "psi" "all_psi" "lambda" "lambdap"
model.fits[[model.number]]$derived$psi[ grepl('unit1_', row.names(model.fits[[model.number]]$derived$psi)),]
## est se lower_0.95 upper_0.95
## unit1_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit1_3 0.5382245 0.03348405 0.4723142 0.6028251
## unit1_4 0.4980526 0.03482302 0.4302573 0.5659195
## unit1_5 0.4791443 0.03607063 0.4093259 0.5497870
## unit1_6 0.4702444 0.03690760 0.3990220 0.5426996
## unit1_7 0.4660553 0.03740402 0.3939860 0.5395701
## unit1_8 0.4640836 0.03767859 0.3915408 0.5381789
## unit1_9 0.4631556 0.03782398 0.3903601 0.5375562
## unit1_10 0.4627187 0.03789886 0.3897924 0.5372758
# Derived parameters - all of the psi stacked together
model.fits[[model.number]]$derived$all_psi[ grepl('unit1_', row.names(model.fits[[model.number]]$derived$all_psi)),]
## est se lower_0.95 upper_0.95
## unit1_1 0.8048985 0.05430232 0.6768764 0.8904102
## unit1_2 0.6235722 0.03478336 0.5533713 0.6889414
## unit1_3 0.5382245 0.03348405 0.4723142 0.6028251
## unit1_4 0.4980526 0.03482302 0.4302573 0.5659195
## unit1_5 0.4791443 0.03607063 0.4093259 0.5497870
## unit1_6 0.4702444 0.03690760 0.3990220 0.5426996
## unit1_7 0.4660553 0.03740402 0.3939860 0.5395701
## unit1_8 0.4640836 0.03767859 0.3915408 0.5381789
## unit1_9 0.4631556 0.03782398 0.3903601 0.5375562
## unit1_10 0.4627187 0.03789886 0.3897924 0.5372758
# Estimate of local extinction probability for each unit
model.fits[[model.number]]$real$epsilon[ seq(1, by=nrow(input.history), length.out=length(Nvisits.per.season)-1),]
## est se lower_0.95 upper_0.95
## epsilon1_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon2_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon3_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon4_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon5_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon6_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon7_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon8_unit1 0.2845964 0.02886474 0.2315391 0.3443634
## epsilon9_unit1 0.2845964 0.02886474 0.2315391 0.3443634
# Estimate of local colonization probability for each unit
model.fits[[model.number]]$real$gamma[ seq(1, by=nrow(input.history), length.out=length(Nvisits.per.season)-1),]
## est se lower_0.95 upper_0.95
## gamma1_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma2_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma3_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma4_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma5_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma6_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma7_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma8_unit1 0.2447181 0.02960531 0.1914028 0.3072416
## gamma9_unit1 0.2447181 0.02960531 0.1914028 0.3072416
# Estimate of probability of detection at each time point for each unit
model.fits[[model.number]]$real$p[ grepl('unit1_', row.names(model.fits[[model.number]]$real$p), fixed=TRUE),]
## [1] est se lower_0.95 upper_0.95
## <0 rows> (or 0-length row.names)
# Get the change in occupancy
# Not yet possible to estimate the se of these values. May have to use bootstrapping.
model.fits[[model.number]]$derived$lambda [grepl('unit1_', row.names(model.fits[[model.number]]$derived$lambda), fixed=TRUE),]
## est se lower_0.95 upper_0.95
## unit1_1 0.7747215 NA NA NA
## unit1_2 0.8631311 NA NA NA
## unit1_3 0.9253622 NA NA NA
## unit1_4 0.9620355 NA NA NA
## unit1_5 0.9814255 NA NA NA
## unit1_6 0.9910918 NA NA NA
## unit1_7 0.9957693 NA NA NA
## unit1_8 0.9980002 NA NA NA
## unit1_9 0.9990569 NA NA NA
model.fits[[model.number]]$derived$lambdap[grepl('unit1_', row.names(model.fits[[model.number]]$derived$lambdap), fixed=TRUE),]
## est se lower_0.95 upper_0.95
## unit1_1 0.4015361 NA NA NA
## unit1_2 0.7036029 NA NA NA
## unit1_3 0.8513035 NA NA NA
## unit1_4 0.9271112 NA NA NA
## unit1_5 0.9649376 NA NA NA
## unit1_6 0.9833162 NA NA NA
## unit1_7 0.9921057 NA NA NA
## unit1_8 0.9962750 NA NA NA
## unit1_9 0.9982446 NA NA NA
# collect models and make AIC table
aic.table <- RPresence::createAicTable(model.fits)
aic.table$table
## Model AIC neg2ll npar
## 3 psi()p(SEASON)gamma(SEASON)epsilon(SEASON) 3591.994 3533.994 29
## 2 psi()p(SEASON)gamma()epsilon() 3592.397 3566.397 13
## 1 psi()p()gamma()epsilon() 3818.899 3810.899 4
## warn.conv warn.VC DAIC modlike wgt
## 3 0 0 0.0000 1.0000 0.5502
## 2 0 0 0.4033 0.8174 0.4498
## 1 0 0 226.9048 0.0000 0.0000
# model averaging in the usual way
# initial occupancy
RPresence::modAvg(aic.table, param="psi")[1:5,]
## est se lower_0.95 upper_0.95
## unit1_1 0.8036792 0.05421865 0.6761062 0.8892365
## unit2_1 0.8036792 0.05421865 0.6761062 0.8892365
## unit3_1 0.8036792 0.05421865 0.6761062 0.8892365
## unit4_1 0.8036792 0.05421865 0.6761062 0.8892365
## unit5_1 0.8036792 0.05421865 0.6761062 0.8892365
# model averaging of derived parameters such as the occupancy at each time step
ma_all_psi <- RPresence.modAvg.derived(aic.table, param="all_psi")
psi.est <- ma_all_psi[grepl('unit1_', row.names(ma_all_psi), fixed=TRUE),]
psi.est$Year <- as.numeric(substring(row.names(psi.est),1+regexpr("_",row.names(psi.est))))
psi.est$parameter <- 'psi'
psi.est
## est se lower_0.95 upper_0.95 Year parameter
## unit1_1 0.8036793 0.05421858 0.6974128 0.9099458 1 psi
## unit1_2 0.7497435 0.12758896 0.4996737 0.9998133 2 psi
## unit1_3 0.5922258 0.07174707 0.4516042 0.7328475 3 psi
## unit1_4 0.4541100 0.06681018 0.3231645 0.5850556 4 psi
## unit1_5 0.5071273 0.05984257 0.3898380 0.6244166 5 psi
## unit1_6 0.5257168 0.07486316 0.3789877 0.6724459 6 psi
## unit1_7 0.4305352 0.06303138 0.3069960 0.5540745 7 psi
## unit1_8 0.4234689 0.07023120 0.2858183 0.5611195 8 psi
## unit1_9 0.4194630 0.06721162 0.2877307 0.5511954 9 psi
## unit1_10 0.4293867 0.06220362 0.3074699 0.5513036 10 psi
# likely more interested in colonization and extinction probabilities
epsilon.ma <- RPresence::modAvg(aic.table, param="epsilon")
epsilon.ma <- epsilon.ma[grepl('unit1$', row.names(epsilon.ma)),]
epsilon.ma$Year <- as.numeric(substr(row.names(epsilon.ma),7+regexpr("epsilon",row.names(epsilon.ma)),-1+regexpr("_",row.names(epsilon.ma))))
epsilon.ma$parameter <- 'epsilon'
epsilon.ma
## est se lower_0.95 upper_0.95 Year parameter
## epsilon1_unit1 0.1776739 0.11025696 0.04692023 0.4867218 1 epsilon
## epsilon2_unit1 0.2776391 0.05528658 0.18294803 0.3974969 2 epsilon
## epsilon3_unit1 0.3323731 0.07688312 0.20156381 0.4954017 3 epsilon
## epsilon4_unit1 0.1743040 0.11059096 0.04472452 0.4876593 4 epsilon
## epsilon5_unit1 0.2366162 0.07421635 0.12167895 0.4095039 5 epsilon
## epsilon6_unit1 0.3745666 0.10685055 0.19676640 0.5941813 6 epsilon
## epsilon7_unit1 0.3317518 0.10506266 0.16395068 0.5568968 7 epsilon
## epsilon8_unit1 0.2943305 0.08078551 0.16290407 0.4720017 8 epsilon
## epsilon9_unit1 0.3620801 0.10848250 0.18439117 0.5876310 9 epsilon
gamma.ma <-RPresence::modAvg(aic.table, param="gamma")
gamma.ma <- gamma.ma[grepl('unit1$', row.names(gamma.ma)),]
gamma.ma$Year <- as.numeric(substr(row.names(gamma.ma),5+regexpr("gamma",row.names(gamma.ma)),-1+regexpr("_",row.names(gamma.ma))))
gamma.ma$parameter <- 'gamma'
gamma.ma
## est se lower_0.95 upper_0.95 Year parameter
## gamma1_unit1 0.4521509 0.22915656 0.11866396 0.8349572 1 gamma
## gamma2_unit1 0.1677687 0.19971581 0.01206799 0.7688819 2 gamma
## gamma3_unit1 0.1375705 0.10528609 0.02725462 0.4759350 3 gamma
## gamma4_unit1 0.2490961 0.06086332 0.14916165 0.3856378 4 gamma
## gamma5_unit1 0.2806222 0.07813643 0.15446597 0.4544367 5 gamma
## gamma6_unit1 0.2208078 0.06930743 0.11400234 0.3842768 6 gamma
## gamma7_unit1 0.2364225 0.06172421 0.13675254 0.3770097 7 gamma
## gamma8_unit1 0.2106397 0.06861518 0.10622492 0.3746657 8 gamma
## gamma9_unit1 0.2722494 0.06656983 0.16222426 0.4195285 9 gamma
all.est <- rbind(psi.est, epsilon.ma, gamma.ma)
ggplot(data=all.est, aes(x=Year,y=est, color=parameter))+
ggtitle("Estimated occupancy, extinction, colonization, over time")+
geom_point(position=position_dodge(w=0.2))+
geom_line(position=position_dodge(w=0.2))+
ylim(0,1)+
geom_errorbar(aes(ymin=lower_0.95, ymax=upper_0.95), width=.1,position=position_dodge(w=0.2))+
scale_x_continuous(breaks=1:10)
