# Example using model averaging on the pronghorn dataset
# 256 sites, two sampling occasions per site
# Covariates used in this example:
# 1. sagebrush (continuos) - Sagebrush density
# 2. aspect (Categorical) - Compass direction slope faces (N,S,E,W)
# 2018-11-26 Code contributed by Neil Faught
# RPresence package
library(readxl)
library(RPresence)
## Warning: package 'RPresence' was built under R version 3.5.0
library(ggplot2)
# 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("..","pronghorn.csv"), header=TRUE, as.is=TRUE, strip.white=TRUE)
head(input.data)
## Plot Survey.1 Survey.2 sagebrush slope DW aspect
## 1 1 0 0 9.0 0 25 W
## 2 2 0 1 18.0 5 150 S
## 3 3 0 0 8.4 45 150 W
## 4 4 0 0 3.2 65 375 E
## 5 5 0 1 12.0 5 375 S
## 6 6 1 1 7.8 5 150 S
input.history <- input.data[,c(2,3)] # the history extracted
head(input.history)
## Survey.1 Survey.2
## 1 0 0
## 2 0 1
## 3 0 0
## 4 0 0
## 5 0 1
## 6 1 1
# do some basic checks on your data
# e.g. check number of sites; number of visits etc
nrow(input.history)
## [1] 256
ncol(input.history)
## [1] 2
range(input.history, na.rm=TRUE) # check that all values are either 0 or 1
## [1] 0 1
sum(is.na(input.history)) # are there any missing values?
## [1] 0
# Get the sagebrush and aspect information. These are the covariates that will
# be used in the analysis. These are unit-level covariates.
unit.cov = input.data[,c(4,7)]
head(unit.cov)
## sagebrush aspect
## 1 9.0 W
## 2 18.0 S
## 3 8.4 W
## 4 3.2 E
## 5 12.0 S
## 6 7.8 S
# Create the *.pao file
prong.pao <- RPresence::createPao(input.history,
unitcov=unit.cov,
title='Pronghorn SSSS')
prong.pao
## $nunits
## [1] 256
##
## $nsurveys
## [1] 2
##
## $nseasons
## [1] 1
##
## $nmethods
## [1] 1
##
## $det.data
## Survey.1 Survey.2
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##
## $nunitcov
## [1] 2
##
## $unitcov
## sagebrush aspect
## 1 9.0 W
## 2 18.0 S
## 3 8.4 W
## 4 3.2 E
## 5 12.0 S
## 6 7.8 S
## 7 5.4 N
## 8 10.0 N
## 9 12.0 S
## 10 12.0 S
## 11 0.6 E
## 12 7.6 N
## 13 4.2 S
## 14 12.0 S
## 15 8.2 S
## 16 4.0 E
## 17 10.0 W
## 18 4.0 W
## 19 3.4 E
## 20 6.4 W
## 21 4.0 W
## 22 7.8 W
## 23 10.0 W
## 24 10.0 W
## 25 3.8 N
## 26 1.2 S
## 27 2.0 W
## 28 5.8 S
## 29 7.4 N
## 30 7.2 S
## 31 4.0 S
## 32 18.0 W
## 33 1.6 S
## 34 3.4 W
## 35 0.8 N
## 36 5.9 N
## 37 0.2 S
## 38 12.0 S
## 39 9.0 S
## 40 2.0 W
## 41 9.2 N
## 42 6.2 W
## 43 0.2 W
## 44 7.4 S
## 45 3.2 E
## 46 2.2 W
## 47 9.6 E
## 48 13.0 W
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## 55 3.0 S
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##
## $nsurvcov
## [1] 1
##
## $survcov
## SURVEY
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##
## $nsurveyseason
## [1] 2
##
## $title
## [1] "Pronghorn SSSS"
##
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## [225] "unit225" "unit226" "unit227" "unit228" "unit229" "unit230" "unit231"
## [232] "unit232" "unit233" "unit234" "unit235" "unit236" "unit237" "unit238"
## [239] "unit239" "unit240" "unit241" "unit242" "unit243" "unit244" "unit245"
## [246] "unit246" "unit247" "unit248" "unit249" "unit250" "unit251" "unit252"
## [253] "unit253" "unit254" "unit255" "unit256"
##
## $surveynames
## [1] "1-1" "1-2"
##
## $paoname
## [1] "pres.pao"
##
## $frq
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [106] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [141] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [176] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [211] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [246] 1 1 1 1 1 1 1 1 1 1 1
##
## attr(,"class")
## [1] "pao"
# define the list of models to fit
# Notice the commas between the column and the placement of the quotes
model.list.csv <- textConnection("
p, psi
~1, ~1
~SURVEY, ~1
~1, ~aspect
~1, ~sagebrush")
model.list <- read.csv(model.list.csv, header=TRUE, as.is=TRUE, strip.white=TRUE)
model.list
## p psi
## 1 ~1 ~1
## 2 ~SURVEY ~1
## 3 ~1 ~aspect
## 4 ~1 ~sagebrush
# fit the models
model.fits <- plyr::alply(model.list, 1, function(x,detect.pao){
cat("\n\n***** Starting ", unlist(x), "\n")
fit <- RPresence::occMod(model=list(as.formula(paste("psi",x$psi)),
as.formula(paste("p" ,x$p ))),
data=detect.pao,type="so")
fit
},detect.pao=prong.pao)
##
##
## ***** Starting ~1 ~1
## PRESENCE Version 2.12.21.
##
##
## ***** Starting ~SURVEY ~1
## PRESENCE Version 2.12.21.
##
##
## ***** Starting ~1 ~aspect
## PRESENCE Version 2.12.21.
##
##
## ***** Starting ~1 ~sagebrush
## PRESENCE Version 2.12.21.
# Look the output from a specific model
check.model <- 1
names(model.fits[[check.model]])
## [1] "modname" "model" "dmat" "data" "outfile"
## [6] "neg2loglike" "npar" "aic" "beta" "real"
## [11] "derived" "gof" "warnings" "version"
model.fits[[check.model]]$beta
## $psi
## est se
## A1_psi 0.886474 0.331229
##
## $psi.VC
## [,1]
## [1,] 0.109713
##
## $p
## est se
## B1_p1 -0.169076 0.193449
##
## $p.VC
## [,1]
## [1,] 0.037423
##
## $VC
## A1_psi B1_p1
## A1_psi 0.109713 -0.048881
## B1_p1 -0.048881 0.037423
names(model.fits[[check.model]]$real)
## [1] "psi" "p"
model.fits[[check.model]]$real$psi[1:5,]
## est se lower_0.95 upper_0.95
## psi_unit1 0.708162 0.06845474 0.5590423 0.8228347
## psi_unit2 0.708162 0.06845474 0.5590423 0.8228347
## psi_unit3 0.708162 0.06845474 0.5590423 0.8228347
## psi_unit4 0.708162 0.06845474 0.5590423 0.8228347
## psi_unit5 0.708162 0.06845474 0.5590423 0.8228347
model.fits[[check.model]]$real$p[1:5,]
## est se lower_0.95 upper_0.95
## p1_unit1 0.4578314 0.04801857 0.3662748 0.5523276
## p1_unit2 0.4578314 0.04801857 0.3662748 0.5523276
## p1_unit3 0.4578314 0.04801857 0.3662748 0.5523276
## p1_unit4 0.4578314 0.04801857 0.3662748 0.5523276
## p1_unit5 0.4578314 0.04801857 0.3662748 0.5523276
names(model.fits[[check.model]]$derived)
## [1] "psi_c"
model.fits[[check.model]]$derived$psi_c[1:10,]
## est se lower_0.95 upper_0.95
## unit1 0.4163239 0.1166916 0.2177056 0.6464161
## unit2 1.0000000 0.0000000 1.0000000 1.0000000
## unit3 0.4163239 0.1166916 0.2177056 0.6464161
## unit4 0.4163239 0.1166916 0.2177056 0.6464161
## unit5 1.0000000 0.0000000 1.0000000 1.0000000
## unit6 1.0000000 0.0000000 1.0000000 1.0000000
## unit7 0.4163239 0.1166916 0.2177056 0.6464161
## unit8 1.0000000 0.0000000 1.0000000 1.0000000
## unit9 1.0000000 0.0000000 1.0000000 1.0000000
## unit10 1.0000000 0.0000000 1.0000000 1.0000000
tail(model.fits[[check.model]]$derived$psi_c)
## est se lower_0.95 upper_0.95
## unit251 1.0000000 0.0000000 1.0000000 1.0000000
## unit252 1.0000000 0.0000000 1.0000000 1.0000000
## unit253 0.4163239 0.1166916 0.2177056 0.6464161
## unit254 0.4163239 0.1166916 0.2177056 0.6464161
## unit255 0.4163239 0.1166916 0.2177056 0.6464161
## unit256 0.4163239 0.1166916 0.2177056 0.6464161
# Model averaging
aic.table <- RPresence::createAicTable(model.fits)
aic.table$table
## Model AIC neg2ll npar warn.conv warn.VC DAIC
## 2 psi()p(SURVEY) 635.9227 629.9227 3 0 0 0.0000
## 1 psi()p() 639.3553 635.3553 2 0 0 3.4326
## 4 psi(sagebrush)p() 640.5521 634.5521 3 0 0 4.6294
## 3 psi(aspect)p() 641.3945 631.3945 5 0 0 5.4718
## modlike wgt
## 2 1.0000 0.7444
## 1 0.1797 0.1338
## 4 0.0988 0.0735
## 3 0.0648 0.0483
names(aic.table)
## [1] "table" "models" "ess"
# plot occupancy as a function of sagebrush density
psi.ma <- RPresence::modAvg(aic.table, param="psi")
head(psi.ma)
## est se lower_0.95 upper_0.95
## psi_unit1 0.7017261 0.07033499 0.5490686 0.8196759
## psi_unit2 0.7081633 0.08302050 0.5247658 0.8420840
## psi_unit3 0.7013316 0.06979655 0.5499732 0.8185763
## psi_unit4 0.6904153 0.07821991 0.5211882 0.8204383
## psi_unit5 0.7048222 0.07620245 0.5380474 0.8303698
## psi_unit6 0.7021407 0.07220938 0.5450765 0.8226242
psi.ma$Site <- as.numeric(substring(row.names(psi.ma), 4+regexpr("unit",row.names(psi.ma), fixed=TRUE)))
plotdata <- data.frame(psi.ma, sagebrush = unit.cov$sagebrush)
head(plotdata)
## est se lower_0.95 upper_0.95 Site sagebrush
## psi_unit1 0.7017261 0.07033499 0.5490686 0.8196759 1 9.0
## psi_unit2 0.7081633 0.08302050 0.5247658 0.8420840 2 18.0
## psi_unit3 0.7013316 0.06979655 0.5499732 0.8185763 3 8.4
## psi_unit4 0.6904153 0.07821991 0.5211882 0.8204383 4 3.2
## psi_unit5 0.7048222 0.07620245 0.5380474 0.8303698 5 12.0
## psi_unit6 0.7021407 0.07220938 0.5450765 0.8226242 6 7.8
ggplot(data=plotdata, aes(x=sagebrush, y=est))+
ggtitle("Occupancy as a function of sagebrush density")+
geom_point()+
geom_ribbon(aes(ymin=lower_0.95, ymax=upper_0.95), alpha=0.2)+
ylim(0,1)+
ylab("Estimated occupancy")
# Plot occupancy as a function of aspect
plotdata <- data.frame(psi.ma, aspect = unit.cov$aspect)
head(plotdata)
## est se lower_0.95 upper_0.95 Site aspect
## psi_unit1 0.7017261 0.07033499 0.5490686 0.8196759 1 W
## psi_unit2 0.7081633 0.08302050 0.5247658 0.8420840 2 S
## psi_unit3 0.7013316 0.06979655 0.5499732 0.8185763 3 W
## psi_unit4 0.6904153 0.07821991 0.5211882 0.8204383 4 E
## psi_unit5 0.7048222 0.07620245 0.5380474 0.8303698 5 S
## psi_unit6 0.7021407 0.07220938 0.5450765 0.8226242 6 S
ggplot(data=plotdata, aes(x=aspect, y=est))+
ggtitle("Occupancy as a function of aspect")+
geom_point()+
geom_errorbar(aes(ymin=lower_0.95, ymax=upper_0.95), alpha=0.2)+
ylim(0,1)+
ylab("Estimated occupancy")
