# Sunfish
# Redbreast sunfish occupancy of streams segments over 8 seasons = 4 years * summer*spring
#3 quadrats were sampled per segment via electrofishing total 24 sampling occasions
#15 covariates: LN(link magnitude) and standardized Q (streamflow) for the sampling intervals;
#minimum discharge for intervals 1-7, maximum discharge intervals 1-7
# Fitting several models using the RPresence Package
#Single Species Multiple Seasons
library(readxl)
library(RPresence)
## Warning: package 'RPresence' was built under R version 3.5.0
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 <- readxl::read_excel(file.path("..","sunfish.xls"),
skip=1, col_names=TRUE)#No NAs in this data set, make sure to use "na =" statement if there are
head(input.data)
## # A tibble: 6 x 39
## S11 S12 S13 S21 S22 S23 S31 S32 S33 S41 S42 S43
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.00 0 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 2 0 1.00 1.00 1.00 0 1.00 0 0 0 0 1.00 0
## 3 0 0 0 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00
## 4 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 5 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00 0 0 0
## 6 1.00 1.00 1.00 0 0 0 0 0 0 1.00 1.00 0
## # ... with 27 more variables: S51 <dbl>, S52 <dbl>, S53 <dbl>, S61 <dbl>,
## # S62 <dbl>, S63 <dbl>, S71 <dbl>, S72 <dbl>, S73 <dbl>, S81 <dbl>, S82
## # <dbl>, S83 <dbl>, LN <dbl>, minQ1 <dbl>, minQ2 <dbl>, minQ3 <dbl>,
## # minQ4 <dbl>, minQ5 <dbl>, minQ6 <dbl>, minQ7 <dbl>, maxQ1 <dbl>, maxQ2
## # <dbl>, maxQ3 <dbl>, maxQ4 <dbl>, maxQ5 <dbl>, maxQ6 <dbl>, maxQ7 <dbl>
input.history <- input.data[, 1:24]
head(input.history)
## # A tibble: 6 x 24
## S11 S12 S13 S21 S22 S23 S31 S32 S33 S41 S42 S43
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.00 0 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 2 0 1.00 1.00 1.00 0 1.00 0 0 0 0 1.00 0
## 3 0 0 0 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00
## 4 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 5 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00 0 0 0
## 6 1.00 1.00 1.00 0 0 0 0 0 0 1.00 1.00 0
## # ... with 12 more variables: S51 <dbl>, S52 <dbl>, S53 <dbl>, S61 <dbl>,
## # S62 <dbl>, S63 <dbl>, S71 <dbl>, S72 <dbl>, S73 <dbl>, S81 <dbl>, S82
## # <dbl>, S83 <dbl>
# do some basic checks on your data
# e.g. check number of sites; number of visits etc
nrow(input.history)
## [1] 26
ncol(input.history)
## [1] 24
range(input.history, na.rm=TRUE)
## [1] 0 1
sum(is.na(input.history))
## [1] 0
#Create data frame of site covariates
site.cov <- data.frame(Site=1:nrow(input.data), input.data[,25])
head(site.cov)
## Site LN
## 1 1 1.32
## 2 2 4.97
## 3 3 1.02
## 4 4 2.20
## 5 5 2.89
## 6 6 2.87
#Create data frame of survey covariates
minQ.data = input.data[,26:32]
minQ.data = cbind(minQ.data,rep(NA,nrow(minQ.data)))
names(minQ.data)[8] = "minQ8"
head(minQ.data)
## minQ1 minQ2 minQ3 minQ4 minQ5 minQ6 minQ7 minQ8
## 1 0.61 0.16 0.64 0.92 1.00 0.10 0.27 NA
## 2 0.45 0.06 0.93 0.34 0.20 0.67 0.77 NA
## 3 0.12 0.21 0.03 0.26 0.99 0.81 0.47 NA
## 4 0.32 0.57 0.67 0.37 0.40 0.87 0.30 NA
## 5 0.22 0.85 0.02 0.78 0.47 0.43 0.45 NA
## 6 0.55 0.42 0.45 0.99 0.85 0.50 0.64 NA
maxQ.data = input.data[,33:39]
maxQ.data = cbind(maxQ.data,rep(NA,nrow(maxQ.data)))
names(maxQ.data)[8] = "maxQ8"
head(maxQ.data)
## maxQ1 maxQ2 maxQ3 maxQ4 maxQ5 maxQ6 maxQ7 maxQ8
## 1 5.59 7.58 7.03 1.22 6.73 4.77 1.87 NA
## 2 6.63 1.47 3.49 3.36 2.64 3.21 0.86 NA
## 3 3.19 6.73 6.35 1.46 4.71 1.32 1.01 NA
## 4 6.40 5.92 2.39 0.81 5.06 5.05 5.63 NA
## 5 6.36 6.87 4.86 7.44 5.63 4.13 7.58 NA
## 6 4.97 1.53 3.12 2.14 0.91 1.07 2.70 NA
# Create survey covariate table. You need to stack the data by columns
survey.cov <- data.frame(Site=1:nrow(minQ.data),
Visit=as.factor(rep(1:ncol(minQ.data), each=nrow(minQ.data))),
minQ =unlist(minQ.data),
maxQ =unlist(maxQ.data),
stringsAsFactors=FALSE)
head(survey.cov)
## Site Visit minQ maxQ
## minQ11 1 1 0.61 5.59
## minQ12 2 1 0.45 6.63
## minQ13 3 1 0.12 3.19
## minQ14 4 1 0.32 6.40
## minQ15 5 1 0.22 6.36
## minQ16 6 1 0.55 4.97
# Eight seasons with 3 visits. Don't need same number of visits/season
Nvisits.per.season <- rep(3,8) # 8 seasons with 3 visits. Don't need same number of visits/season
# Create the *.pao file
sunfish.pao <- RPresence::createPao(input.history,
unitcov=site.cov,
survcov=survey.cov,
nsurveyseason=Nvisits.per.season,
title='sunfish SSMS')
## Warning in data.frame(survcov, srvcov): row names were found from a short
## variable and have been discarded
sunfish.pao
## $nunits
## [1] 26
##
## $nsurveys
## [1] 24
##
## $nseasons
## [1] 8
##
## $nmethods
## [1] 1
##
## $det.data
## # A tibble: 26 x 24
## S11 S12 S13 S21 S22 S23 S31 S32 S33 S41 S42 S43
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.00 0 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 2 0 1.00 1.00 1.00 0 1.00 0 0 0 0 1.00 0
## 3 0 0 0 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00
## 4 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
## 5 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00 0 0 0
## 6 1.00 1.00 1.00 0 0 0 0 0 0 1.00 1.00 0
## 7 0 0 0 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00
## 8 0 0 0 1.00 1.00 1.00 1.00 1.00 1.00 0 1.00 1.00
## 9 1.00 1.00 0 1.00 1.00 1.00 0 0 0 1.00 1.00 1.00
## 10 1.00 1.00 1.00 1.00 0 1.00 0 0 0 0 0 0
## # ... with 16 more rows, and 12 more variables: S51 <dbl>, S52 <dbl>, S53
## # <dbl>, S61 <dbl>, S62 <dbl>, S63 <dbl>, S71 <dbl>, S72 <dbl>, S73
## # <dbl>, S81 <dbl>, S82 <dbl>, S83 <dbl>
##
## $nunitcov
## [1] 2
##
## $unitcov
## Site LN
## 1 1 1.32
## 2 2 4.97
## 3 3 1.02
## 4 4 2.20
## 5 5 2.89
## 6 6 2.87
## 7 7 1.24
## 8 8 1.03
## 9 9 1.14
## 10 10 0.73
## 11 11 5.11
## 12 12 5.80
## 13 13 3.65
## 14 14 6.65
## 15 15 1.76
## 16 16 2.21
## 17 17 4.38
## 18 18 1.37
## 19 19 1.29
## 20 20 1.13
## 21 21 4.66
## 22 22 2.73
## 23 23 2.27
## 24 24 0.90
## 25 25 3.00
## 26 26 4.50
##
## $nsurvcov
## [1] 5
##
## $survcov
## Site Visit minQ maxQ SURVEY
## 1 1 1 0.61 5.59 1
## 2 2 1 0.45 6.63 1
## 3 3 1 0.12 3.19 1
## 4 4 1 0.32 6.40 1
## 5 5 1 0.22 6.36 1
## 6 6 1 0.55 4.97 1
## 7 7 1 0.37 7.44 1
## 8 8 1 0.57 5.72 1
## 9 9 1 0.84 1.59 1
## 10 10 1 0.56 5.60 1
## 11 11 1 0.46 6.06 1
## 12 12 1 0.70 3.39 1
## 13 13 1 0.20 4.79 1
## 14 14 1 0.17 2.72 1
## 15 15 1 0.79 3.49 1
## 16 16 1 0.64 2.01 1
## 17 17 1 0.92 7.15 1
## 18 18 1 0.62 1.02 1
## 19 19 1 0.44 6.36 1
## 20 20 1 0.81 6.84 1
## 21 21 1 0.10 3.64 1
## 22 22 1 0.99 2.02 1
## 23 23 1 0.63 7.42 1
## 24 24 1 0.29 2.31 1
## 25 25 1 0.48 4.18 1
## 26 26 1 0.47 7.75 1
## 27 1 2 0.16 7.58 2
## 28 2 2 0.06 1.47 2
## 29 3 2 0.21 6.73 2
## 30 4 2 0.57 5.92 2
## 31 5 2 0.85 6.87 2
## 32 6 2 0.42 1.53 2
## 33 7 2 0.12 4.67 2
## 34 8 2 0.96 3.55 2
## 35 9 2 0.05 5.30 2
## 36 10 2 0.26 1.63 2
## 37 11 2 0.15 7.52 2
## 38 12 2 0.69 6.08 2
## 39 13 2 0.97 6.39 2
## 40 14 2 0.32 6.94 2
## 41 15 2 0.69 3.55 2
## 42 16 2 0.51 5.89 2
## 43 17 2 0.11 3.53 2
## 44 18 2 0.46 6.64 2
## 45 19 2 0.91 5.77 2
## 46 20 2 0.12 3.82 2
## 47 21 2 0.50 6.73 2
## 48 22 2 0.94 1.67 2
## 49 23 2 0.81 1.19 2
## 50 24 2 0.70 6.35 2
## 51 25 2 0.69 7.47 2
## 52 26 2 0.49 6.23 2
## 53 1 3 0.64 7.03 3
## 54 2 3 0.93 3.49 3
## 55 3 3 0.03 6.35 3
## 56 4 3 0.67 2.39 3
## 57 5 3 0.02 4.86 3
## 58 6 3 0.45 3.12 3
## 59 7 3 0.34 3.55 3
## 60 8 3 0.89 7.27 3
## 61 9 3 0.61 7.24 3
## 62 10 3 0.14 1.35 3
## 63 11 3 0.30 2.64 3
## 64 12 3 0.34 6.52 3
## 65 13 3 0.55 5.65 3
## 66 14 3 1.01 3.98 3
## 67 15 3 0.58 2.64 3
## 68 16 3 0.27 3.33 3
## 69 17 3 0.77 3.88 3
## 70 18 3 0.38 1.17 3
## 71 19 3 0.37 5.92 3
## 72 20 3 0.39 1.94 3
## 73 21 3 0.21 7.65 3
## 74 22 3 0.44 7.57 3
## 75 23 3 0.09 1.08 3
## 76 24 3 0.75 2.03 3
## 77 25 3 0.84 7.74 3
## 78 26 3 0.73 2.96 3
## 79 1 4 0.92 1.22 4
## 80 2 4 0.34 3.36 4
## 81 3 4 0.26 1.46 4
## 82 4 4 0.37 0.81 4
## 83 5 4 0.78 7.44 4
## 84 6 4 0.99 2.14 4
## 85 7 4 0.49 7.30 4
## 86 8 4 0.52 3.98 4
## 87 9 4 0.68 6.56 4
## 88 10 4 0.01 1.75 4
## 89 11 4 0.46 2.43 4
## 90 12 4 0.72 5.27 4
## 91 13 4 0.32 6.32 4
## 92 14 4 0.60 1.14 4
## 93 15 4 0.93 2.25 4
## 94 16 4 0.57 6.83 4
## 95 17 4 1.01 7.26 4
## 96 18 4 0.99 3.69 4
## 97 19 4 0.23 6.24 4
## 98 20 4 0.23 2.52 4
## 99 21 4 0.06 2.76 4
## 100 22 4 0.34 6.58 4
## 101 23 4 0.05 2.77 4
## 102 24 4 0.80 2.80 4
## 103 25 4 0.60 5.86 4
## 104 26 4 0.55 4.29 4
## 105 1 5 1.00 6.73 5
## 106 2 5 0.20 2.64 5
## 107 3 5 0.99 4.71 5
## 108 4 5 0.40 5.06 5
## 109 5 5 0.47 5.63 5
## 110 6 5 0.85 0.91 5
## 111 7 5 0.21 1.35 5
## 112 8 5 0.67 6.47 5
## 113 9 5 0.06 3.12 5
## 114 10 5 0.48 2.14 5
## 115 11 5 0.15 6.06 5
## 116 12 5 0.91 4.69 5
## 117 13 5 0.67 3.27 5
## 118 14 5 0.87 7.35 5
## 119 15 5 0.50 4.62 5
## 120 16 5 0.00 3.96 5
## 121 17 5 0.88 6.06 5
## 122 18 5 0.90 6.80 5
## 123 19 5 0.83 2.12 5
## 124 20 5 0.75 7.36 5
## 125 21 5 0.27 3.32 5
## 126 22 5 0.35 1.75 5
## 127 23 5 0.64 4.36 5
## 128 24 5 0.30 6.18 5
## 129 25 5 0.36 4.85 5
## 130 26 5 0.91 3.45 5
## 131 1 6 0.10 4.77 6
## 132 2 6 0.67 3.21 6
## 133 3 6 0.81 1.32 6
## 134 4 6 0.87 5.05 6
## 135 5 6 0.43 4.13 6
## 136 6 6 0.50 1.07 6
## 137 7 6 0.73 1.03 6
## 138 8 6 0.68 4.25 6
## 139 9 6 0.74 1.88 6
## 140 10 6 0.22 1.92 6
## 141 11 6 0.05 1.86 6
## 142 12 6 0.19 6.91 6
## 143 13 6 0.25 2.61 6
## 144 14 6 0.69 6.68 6
## 145 15 6 0.94 7.31 6
## 146 16 6 0.60 5.22 6
## 147 17 6 0.36 7.68 6
## 148 18 6 0.14 5.18 6
## 149 19 6 0.79 7.07 6
## 150 20 6 0.27 7.75 6
## 151 21 6 0.93 4.60 6
## 152 22 6 0.33 4.60 6
## 153 23 6 0.49 2.37 6
## 154 24 6 0.61 5.27 6
## 155 25 6 0.52 2.75 6
## 156 26 6 0.50 2.04 6
## 157 1 7 0.27 1.87 7
## 158 2 7 0.77 0.86 7
## 159 3 7 0.47 1.01 7
## 160 4 7 0.30 5.63 7
## 161 5 7 0.45 7.58 7
## 162 6 7 0.64 2.70 7
## 163 7 7 0.95 0.84 7
## 164 8 7 0.83 2.27 7
## 165 9 7 0.20 2.76 7
## 166 10 7 0.35 2.70 7
## 167 11 7 0.81 6.64 7
## 168 12 7 0.18 5.61 7
## 169 13 7 0.35 2.84 7
## 170 14 7 0.39 1.40 7
## 171 15 7 0.23 1.93 7
## 172 16 7 0.48 5.67 7
## 173 17 7 0.38 4.61 7
## 174 18 7 0.39 7.15 7
## 175 19 7 0.14 4.91 7
## 176 20 7 0.08 2.26 7
## 177 21 7 0.88 5.98 7
## 178 22 7 0.71 1.73 7
## 179 23 7 0.22 0.80 7
## 180 24 7 0.32 3.17 7
## 181 25 7 0.02 5.44 7
## 182 26 7 0.16 1.14 7
## 183 1 8 NA NA 8
## 184 2 8 NA NA 8
## 185 3 8 NA NA 8
## 186 4 8 NA NA 8
## 187 5 8 NA NA 8
## 188 6 8 NA NA 8
## 189 7 8 NA NA 8
## 190 8 8 NA NA 8
## 191 9 8 NA NA 8
## 192 10 8 NA NA 8
## 193 11 8 NA NA 8
## 194 12 8 NA NA 8
## 195 13 8 NA NA 8
## 196 14 8 NA NA 8
## 197 15 8 NA NA 8
## 198 16 8 NA NA 8
## 199 17 8 NA NA 8
## 200 18 8 NA NA 8
## 201 19 8 NA NA 8
## 202 20 8 NA NA 8
## 203 21 8 NA NA 8
## 204 22 8 NA NA 8
## 205 23 8 NA NA 8
## 206 24 8 NA NA 8
## 207 25 8 NA NA 8
## 208 26 8 NA NA 8
## 209 1 1 0.61 5.59 9
## 210 2 1 0.45 6.63 9
## 211 3 1 0.12 3.19 9
## 212 4 1 0.32 6.40 9
## 213 5 1 0.22 6.36 9
## 214 6 1 0.55 4.97 9
## 215 7 1 0.37 7.44 9
## 216 8 1 0.57 5.72 9
## 217 9 1 0.84 1.59 9
## 218 10 1 0.56 5.60 9
## 219 11 1 0.46 6.06 9
## 220 12 1 0.70 3.39 9
## 221 13 1 0.20 4.79 9
## 222 14 1 0.17 2.72 9
## 223 15 1 0.79 3.49 9
## 224 16 1 0.64 2.01 9
## 225 17 1 0.92 7.15 9
## 226 18 1 0.62 1.02 9
## 227 19 1 0.44 6.36 9
## 228 20 1 0.81 6.84 9
## 229 21 1 0.10 3.64 9
## 230 22 1 0.99 2.02 9
## 231 23 1 0.63 7.42 9
## 232 24 1 0.29 2.31 9
## 233 25 1 0.48 4.18 9
## 234 26 1 0.47 7.75 9
## 235 1 2 0.16 7.58 10
## 236 2 2 0.06 1.47 10
## 237 3 2 0.21 6.73 10
## 238 4 2 0.57 5.92 10
## 239 5 2 0.85 6.87 10
## 240 6 2 0.42 1.53 10
## 241 7 2 0.12 4.67 10
## 242 8 2 0.96 3.55 10
## 243 9 2 0.05 5.30 10
## 244 10 2 0.26 1.63 10
## 245 11 2 0.15 7.52 10
## 246 12 2 0.69 6.08 10
## 247 13 2 0.97 6.39 10
## 248 14 2 0.32 6.94 10
## 249 15 2 0.69 3.55 10
## 250 16 2 0.51 5.89 10
## 251 17 2 0.11 3.53 10
## 252 18 2 0.46 6.64 10
## 253 19 2 0.91 5.77 10
## 254 20 2 0.12 3.82 10
## 255 21 2 0.50 6.73 10
## 256 22 2 0.94 1.67 10
## 257 23 2 0.81 1.19 10
## 258 24 2 0.70 6.35 10
## 259 25 2 0.69 7.47 10
## 260 26 2 0.49 6.23 10
## 261 1 3 0.64 7.03 11
## 262 2 3 0.93 3.49 11
## 263 3 3 0.03 6.35 11
## 264 4 3 0.67 2.39 11
## 265 5 3 0.02 4.86 11
## 266 6 3 0.45 3.12 11
## 267 7 3 0.34 3.55 11
## 268 8 3 0.89 7.27 11
## 269 9 3 0.61 7.24 11
## 270 10 3 0.14 1.35 11
## 271 11 3 0.30 2.64 11
## 272 12 3 0.34 6.52 11
## 273 13 3 0.55 5.65 11
## 274 14 3 1.01 3.98 11
## 275 15 3 0.58 2.64 11
## 276 16 3 0.27 3.33 11
## 277 17 3 0.77 3.88 11
## 278 18 3 0.38 1.17 11
## 279 19 3 0.37 5.92 11
## 280 20 3 0.39 1.94 11
## 281 21 3 0.21 7.65 11
## 282 22 3 0.44 7.57 11
## 283 23 3 0.09 1.08 11
## 284 24 3 0.75 2.03 11
## 285 25 3 0.84 7.74 11
## 286 26 3 0.73 2.96 11
## 287 1 4 0.92 1.22 12
## 288 2 4 0.34 3.36 12
## 289 3 4 0.26 1.46 12
## 290 4 4 0.37 0.81 12
## 291 5 4 0.78 7.44 12
## 292 6 4 0.99 2.14 12
## 293 7 4 0.49 7.30 12
## 294 8 4 0.52 3.98 12
## 295 9 4 0.68 6.56 12
## 296 10 4 0.01 1.75 12
## 297 11 4 0.46 2.43 12
## 298 12 4 0.72 5.27 12
## 299 13 4 0.32 6.32 12
## 300 14 4 0.60 1.14 12
## 301 15 4 0.93 2.25 12
## 302 16 4 0.57 6.83 12
## 303 17 4 1.01 7.26 12
## 304 18 4 0.99 3.69 12
## 305 19 4 0.23 6.24 12
## 306 20 4 0.23 2.52 12
## 307 21 4 0.06 2.76 12
## 308 22 4 0.34 6.58 12
## 309 23 4 0.05 2.77 12
## 310 24 4 0.80 2.80 12
## 311 25 4 0.60 5.86 12
## 312 26 4 0.55 4.29 12
## 313 1 5 1.00 6.73 13
## 314 2 5 0.20 2.64 13
## 315 3 5 0.99 4.71 13
## 316 4 5 0.40 5.06 13
## 317 5 5 0.47 5.63 13
## 318 6 5 0.85 0.91 13
## 319 7 5 0.21 1.35 13
## 320 8 5 0.67 6.47 13
## 321 9 5 0.06 3.12 13
## 322 10 5 0.48 2.14 13
## 323 11 5 0.15 6.06 13
## 324 12 5 0.91 4.69 13
## 325 13 5 0.67 3.27 13
## 326 14 5 0.87 7.35 13
## 327 15 5 0.50 4.62 13
## 328 16 5 0.00 3.96 13
## 329 17 5 0.88 6.06 13
## 330 18 5 0.90 6.80 13
## 331 19 5 0.83 2.12 13
## 332 20 5 0.75 7.36 13
## 333 21 5 0.27 3.32 13
## 334 22 5 0.35 1.75 13
## 335 23 5 0.64 4.36 13
## 336 24 5 0.30 6.18 13
## 337 25 5 0.36 4.85 13
## 338 26 5 0.91 3.45 13
## 339 1 6 0.10 4.77 14
## 340 2 6 0.67 3.21 14
## 341 3 6 0.81 1.32 14
## 342 4 6 0.87 5.05 14
## 343 5 6 0.43 4.13 14
## 344 6 6 0.50 1.07 14
## 345 7 6 0.73 1.03 14
## 346 8 6 0.68 4.25 14
## 347 9 6 0.74 1.88 14
## 348 10 6 0.22 1.92 14
## 349 11 6 0.05 1.86 14
## 350 12 6 0.19 6.91 14
## 351 13 6 0.25 2.61 14
## 352 14 6 0.69 6.68 14
## 353 15 6 0.94 7.31 14
## 354 16 6 0.60 5.22 14
## 355 17 6 0.36 7.68 14
## 356 18 6 0.14 5.18 14
## 357 19 6 0.79 7.07 14
## 358 20 6 0.27 7.75 14
## 359 21 6 0.93 4.60 14
## 360 22 6 0.33 4.60 14
## 361 23 6 0.49 2.37 14
## 362 24 6 0.61 5.27 14
## 363 25 6 0.52 2.75 14
## 364 26 6 0.50 2.04 14
## 365 1 7 0.27 1.87 15
## 366 2 7 0.77 0.86 15
## 367 3 7 0.47 1.01 15
## 368 4 7 0.30 5.63 15
## 369 5 7 0.45 7.58 15
## 370 6 7 0.64 2.70 15
## 371 7 7 0.95 0.84 15
## 372 8 7 0.83 2.27 15
## 373 9 7 0.20 2.76 15
## 374 10 7 0.35 2.70 15
## 375 11 7 0.81 6.64 15
## 376 12 7 0.18 5.61 15
## 377 13 7 0.35 2.84 15
## 378 14 7 0.39 1.40 15
## 379 15 7 0.23 1.93 15
## 380 16 7 0.48 5.67 15
## 381 17 7 0.38 4.61 15
## 382 18 7 0.39 7.15 15
## 383 19 7 0.14 4.91 15
## 384 20 7 0.08 2.26 15
## 385 21 7 0.88 5.98 15
## 386 22 7 0.71 1.73 15
## 387 23 7 0.22 0.80 15
## 388 24 7 0.32 3.17 15
## 389 25 7 0.02 5.44 15
## 390 26 7 0.16 1.14 15
## 391 1 8 NA NA 16
## 392 2 8 NA NA 16
## 393 3 8 NA NA 16
## 394 4 8 NA NA 16
## 395 5 8 NA NA 16
## 396 6 8 NA NA 16
## 397 7 8 NA NA 16
## 398 8 8 NA NA 16
## 399 9 8 NA NA 16
## 400 10 8 NA NA 16
## 401 11 8 NA NA 16
## 402 12 8 NA NA 16
## 403 13 8 NA NA 16
## 404 14 8 NA NA 16
## 405 15 8 NA NA 16
## 406 16 8 NA NA 16
## 407 17 8 NA NA 16
## 408 18 8 NA NA 16
## 409 19 8 NA NA 16
## 410 20 8 NA NA 16
## 411 21 8 NA NA 16
## 412 22 8 NA NA 16
## 413 23 8 NA NA 16
## 414 24 8 NA NA 16
## 415 25 8 NA NA 16
## 416 26 8 NA NA 16
## 417 1 1 0.61 5.59 17
## 418 2 1 0.45 6.63 17
## 419 3 1 0.12 3.19 17
## 420 4 1 0.32 6.40 17
## 421 5 1 0.22 6.36 17
## 422 6 1 0.55 4.97 17
## 423 7 1 0.37 7.44 17
## 424 8 1 0.57 5.72 17
## 425 9 1 0.84 1.59 17
## 426 10 1 0.56 5.60 17
## 427 11 1 0.46 6.06 17
## 428 12 1 0.70 3.39 17
## 429 13 1 0.20 4.79 17
## 430 14 1 0.17 2.72 17
## 431 15 1 0.79 3.49 17
## 432 16 1 0.64 2.01 17
## 433 17 1 0.92 7.15 17
## 434 18 1 0.62 1.02 17
## 435 19 1 0.44 6.36 17
## 436 20 1 0.81 6.84 17
## 437 21 1 0.10 3.64 17
## 438 22 1 0.99 2.02 17
## 439 23 1 0.63 7.42 17
## 440 24 1 0.29 2.31 17
## 441 25 1 0.48 4.18 17
## 442 26 1 0.47 7.75 17
## 443 1 2 0.16 7.58 18
## 444 2 2 0.06 1.47 18
## 445 3 2 0.21 6.73 18
## 446 4 2 0.57 5.92 18
## 447 5 2 0.85 6.87 18
## 448 6 2 0.42 1.53 18
## 449 7 2 0.12 4.67 18
## 450 8 2 0.96 3.55 18
## 451 9 2 0.05 5.30 18
## 452 10 2 0.26 1.63 18
## 453 11 2 0.15 7.52 18
## 454 12 2 0.69 6.08 18
## 455 13 2 0.97 6.39 18
## 456 14 2 0.32 6.94 18
## 457 15 2 0.69 3.55 18
## 458 16 2 0.51 5.89 18
## 459 17 2 0.11 3.53 18
## 460 18 2 0.46 6.64 18
## 461 19 2 0.91 5.77 18
## 462 20 2 0.12 3.82 18
## 463 21 2 0.50 6.73 18
## 464 22 2 0.94 1.67 18
## 465 23 2 0.81 1.19 18
## 466 24 2 0.70 6.35 18
## 467 25 2 0.69 7.47 18
## 468 26 2 0.49 6.23 18
## 469 1 3 0.64 7.03 19
## 470 2 3 0.93 3.49 19
## 471 3 3 0.03 6.35 19
## 472 4 3 0.67 2.39 19
## 473 5 3 0.02 4.86 19
## 474 6 3 0.45 3.12 19
## 475 7 3 0.34 3.55 19
## 476 8 3 0.89 7.27 19
## 477 9 3 0.61 7.24 19
## 478 10 3 0.14 1.35 19
## 479 11 3 0.30 2.64 19
## 480 12 3 0.34 6.52 19
## 481 13 3 0.55 5.65 19
## 482 14 3 1.01 3.98 19
## 483 15 3 0.58 2.64 19
## 484 16 3 0.27 3.33 19
## 485 17 3 0.77 3.88 19
## 486 18 3 0.38 1.17 19
## 487 19 3 0.37 5.92 19
## 488 20 3 0.39 1.94 19
## 489 21 3 0.21 7.65 19
## 490 22 3 0.44 7.57 19
## 491 23 3 0.09 1.08 19
## 492 24 3 0.75 2.03 19
## 493 25 3 0.84 7.74 19
## 494 26 3 0.73 2.96 19
## 495 1 4 0.92 1.22 20
## 496 2 4 0.34 3.36 20
## 497 3 4 0.26 1.46 20
## 498 4 4 0.37 0.81 20
## 499 5 4 0.78 7.44 20
## 500 6 4 0.99 2.14 20
## 501 7 4 0.49 7.30 20
## 502 8 4 0.52 3.98 20
## 503 9 4 0.68 6.56 20
## 504 10 4 0.01 1.75 20
## 505 11 4 0.46 2.43 20
## 506 12 4 0.72 5.27 20
## 507 13 4 0.32 6.32 20
## 508 14 4 0.60 1.14 20
## 509 15 4 0.93 2.25 20
## 510 16 4 0.57 6.83 20
## 511 17 4 1.01 7.26 20
## 512 18 4 0.99 3.69 20
## 513 19 4 0.23 6.24 20
## 514 20 4 0.23 2.52 20
## 515 21 4 0.06 2.76 20
## 516 22 4 0.34 6.58 20
## 517 23 4 0.05 2.77 20
## 518 24 4 0.80 2.80 20
## 519 25 4 0.60 5.86 20
## 520 26 4 0.55 4.29 20
## 521 1 5 1.00 6.73 21
## 522 2 5 0.20 2.64 21
## 523 3 5 0.99 4.71 21
## 524 4 5 0.40 5.06 21
## 525 5 5 0.47 5.63 21
## 526 6 5 0.85 0.91 21
## 527 7 5 0.21 1.35 21
## 528 8 5 0.67 6.47 21
## 529 9 5 0.06 3.12 21
## 530 10 5 0.48 2.14 21
## 531 11 5 0.15 6.06 21
## 532 12 5 0.91 4.69 21
## 533 13 5 0.67 3.27 21
## 534 14 5 0.87 7.35 21
## 535 15 5 0.50 4.62 21
## 536 16 5 0.00 3.96 21
## 537 17 5 0.88 6.06 21
## 538 18 5 0.90 6.80 21
## 539 19 5 0.83 2.12 21
## 540 20 5 0.75 7.36 21
## 541 21 5 0.27 3.32 21
## 542 22 5 0.35 1.75 21
## 543 23 5 0.64 4.36 21
## 544 24 5 0.30 6.18 21
## 545 25 5 0.36 4.85 21
## 546 26 5 0.91 3.45 21
## 547 1 6 0.10 4.77 22
## 548 2 6 0.67 3.21 22
## 549 3 6 0.81 1.32 22
## 550 4 6 0.87 5.05 22
## 551 5 6 0.43 4.13 22
## 552 6 6 0.50 1.07 22
## 553 7 6 0.73 1.03 22
## 554 8 6 0.68 4.25 22
## 555 9 6 0.74 1.88 22
## 556 10 6 0.22 1.92 22
## 557 11 6 0.05 1.86 22
## 558 12 6 0.19 6.91 22
## 559 13 6 0.25 2.61 22
## 560 14 6 0.69 6.68 22
## 561 15 6 0.94 7.31 22
## 562 16 6 0.60 5.22 22
## 563 17 6 0.36 7.68 22
## 564 18 6 0.14 5.18 22
## 565 19 6 0.79 7.07 22
## 566 20 6 0.27 7.75 22
## 567 21 6 0.93 4.60 22
## 568 22 6 0.33 4.60 22
## 569 23 6 0.49 2.37 22
## 570 24 6 0.61 5.27 22
## 571 25 6 0.52 2.75 22
## 572 26 6 0.50 2.04 22
## 573 1 7 0.27 1.87 23
## 574 2 7 0.77 0.86 23
## 575 3 7 0.47 1.01 23
## 576 4 7 0.30 5.63 23
## 577 5 7 0.45 7.58 23
## 578 6 7 0.64 2.70 23
## 579 7 7 0.95 0.84 23
## 580 8 7 0.83 2.27 23
## 581 9 7 0.20 2.76 23
## 582 10 7 0.35 2.70 23
## 583 11 7 0.81 6.64 23
## 584 12 7 0.18 5.61 23
## 585 13 7 0.35 2.84 23
## 586 14 7 0.39 1.40 23
## 587 15 7 0.23 1.93 23
## 588 16 7 0.48 5.67 23
## 589 17 7 0.38 4.61 23
## 590 18 7 0.39 7.15 23
## 591 19 7 0.14 4.91 23
## 592 20 7 0.08 2.26 23
## 593 21 7 0.88 5.98 23
## 594 22 7 0.71 1.73 23
## 595 23 7 0.22 0.80 23
## 596 24 7 0.32 3.17 23
## 597 25 7 0.02 5.44 23
## 598 26 7 0.16 1.14 23
## 599 1 8 NA NA 24
## 600 2 8 NA NA 24
## 601 3 8 NA NA 24
## 602 4 8 NA NA 24
## 603 5 8 NA NA 24
## 604 6 8 NA NA 24
## 605 7 8 NA NA 24
## 606 8 8 NA NA 24
## 607 9 8 NA NA 24
## 608 10 8 NA NA 24
## 609 11 8 NA NA 24
## 610 12 8 NA NA 24
## 611 13 8 NA NA 24
## 612 14 8 NA NA 24
## 613 15 8 NA NA 24
## 614 16 8 NA NA 24
## 615 17 8 NA NA 24
## 616 18 8 NA NA 24
## 617 19 8 NA NA 24
## 618 20 8 NA NA 24
## 619 21 8 NA NA 24
## 620 22 8 NA NA 24
## 621 23 8 NA NA 24
## 622 24 8 NA NA 24
## 623 25 8 NA NA 24
## 624 26 8 NA NA 24
##
## $nsurveyseason
## [1] 3 3 3 3 3 3 3 3
##
## $title
## [1] "sunfish 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"
##
## $surveynames
## [1] "1-1" "1-2" "1-3" "2-1" "2-2" "2-3" "3-1" "3-2" "3-3" "4-1" "4-2"
## [12] "4-3" "5-1" "5-2" "5-3" "6-1" "6-2" "6-3" "7-1" "7-2" "7-3" "8-1"
## [23] "8-2" "8-3"
##
## $paoname
## [1] "pres.pao"
##
## $frq
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## attr(,"class")
## [1] "pao"
# Define the models.
# model.type do.1 is dynamic occupancy first parameterization
# do.4 is dynamic occupancy 4th parameterization (random occupancy)
# Random occupancy are fit using type="do.4" in the call.
# Parameters are psi, p with gamma=1-epsilon enforced internally
model.list.csv <- textConnection("
p, psi, gamma, epsilon, model.type
~SEASON, ~LN, ~maxQ, ~minQ, do.1
~SEASON, ~LN, ~minQ, ~minQ, do.1
~SEASON, ~LN, ~minQ, ~maxQ, do.1
~SEASON, ~1, ~SEASON, ~SEASON, do.1")
model.list <- read.csv(model.list.csv, header=TRUE, as.is=TRUE, strip.white=TRUE)
model.list
## p psi gamma epsilon model.type
## 1 ~SEASON ~LN ~maxQ ~minQ do.1
## 2 ~SEASON ~LN ~minQ ~minQ do.1
## 3 ~SEASON ~LN ~minQ ~maxQ do.1
## 4 ~SEASON ~1 ~SEASON ~SEASON do.1
# fit the model
model.fits <- plyr::alply(model.list, 1, function(x,detect.pao){
cat("\n\n***** Starting ", unlist(x), "\n")
if(x$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$p ))),
data=detect.pao,type="do.4")
}
fit <- RPresence.add.derived(fit)
fit
},detect.pao=sunfish.pao)
##
##
## ***** Starting ~SEASON ~LN ~maxQ ~minQ do.1
## PRESENCE Version 2.12.21.
## Loading required package: plyr
##
##
## ***** Starting ~SEASON ~LN ~minQ ~minQ do.1
## PRESENCE Version 2.12.21.
##
##
## ***** Starting ~SEASON ~LN ~minQ ~maxQ do.1
## PRESENCE Version 2.12.21.
##
##
## ***** Starting ~SEASON ~1 ~SEASON ~SEASON do.1
## PRESENCE Version 2.12.21.
# Look at output from a specified model
model.number <- 1
names(model.fits[[model.number]])
## [1] "modname" "model" "dmat" "data" "outfile"
## [6] "neg2loglike" "aic" "npar" "beta" "real"
## [11] "derived" "gof" "warnings" "version"
names(model.fits[[model.number]]$real)
## [1] "psi" "gamma" "epsilon" "p" "theta" "th0pi"
model.fits[[model.number]]$beta
## $psi
## psi.coeff
## 1 -0.755011
## 2 0.690331
##
## $psi.VC
## A1 A2
## A1 0.836775 -0.311879
## A2 -0.311879 0.159827
##
## $gamma
## gamma.coeff
## 1 -5.562369
## 2 2.441616
##
## $gamma.VC
## B1 B2
## B1 4.979296 -2.077430
## B2 -2.077430 0.925414
##
## $epsilon
## epsilon.coeff
## 1 2.019153
## 2 -8.485075
##
## $epsilon.VC
## C1 C2
## C1 0.319066 -0.860020
## C2 -0.860020 3.021943
##
## $p
## p.coeff
## 1 1.099655
## 2 0.561162
## 3 1.099465
## 4 0.277320
## 5 0.258332
## 6 0.179798
## 7 0.237975
## 8 0.316904
##
## $p.VC
## D1 D2 D3 D4 D5 D6 D7
## D1 0.109131 -0.109130 -0.109119 -0.109146 -0.109177 -0.109124 -0.108700
## D2 -0.109130 0.229378 0.109140 0.109148 0.109189 0.109122 0.108691
## D3 -0.109119 0.109140 0.339079 0.109144 0.109252 0.109105 0.109409
## D4 -0.109146 0.109148 0.109144 0.214892 0.109196 0.109141 0.108702
## D5 -0.109177 0.109189 0.109252 0.109196 0.218199 0.109173 0.108928
## D6 -0.109124 0.109122 0.109105 0.109141 0.109173 0.208204 0.108679
## D7 -0.108700 0.108691 0.109409 0.108702 0.108928 0.108679 0.228684
## D8 -0.109124 0.109125 0.109146 0.109146 0.109184 0.109121 0.108796
## D8
## D1 -0.109124
## D2 0.109125
## D3 0.109146
## D4 0.109146
## D5 0.109184
## D6 0.109121
## D7 0.108796
## D8 0.224353
##
## $VC
## A1 A2 B1 B2 C1 C2 D1
## A1 0.836775 -0.311879 0.001206 -0.000048 0.000800 -0.001309 -0.003740
## A2 -0.311879 0.159827 -0.007349 0.001855 -0.000639 0.000475 -0.003855
## B1 0.001206 -0.007349 4.979296 -2.077430 -0.098418 0.467864 0.013475
## B2 -0.000048 0.001855 -2.077430 0.925414 0.037353 -0.176917 -0.003661
## C1 0.000800 -0.000639 -0.098418 0.037353 0.319066 -0.860020 0.000467
## C2 -0.001309 0.000475 0.467864 -0.176917 -0.860020 3.021943 0.000375
## D1 -0.003740 -0.003855 0.013475 -0.003661 0.000467 0.000375 0.109131
## D2 0.003726 0.003861 -0.014764 0.003922 -0.000628 0.000753 -0.109130
## D3 0.003740 0.003847 -0.009301 0.002092 -0.005287 0.023282 -0.109119
## D4 0.003739 0.003862 -0.013689 0.003221 0.000371 -0.001140 -0.109146
## D5 0.003728 0.003884 -0.010384 0.000706 -0.002018 0.007979 -0.109177
## D6 0.003744 0.003850 -0.013470 0.003752 0.000559 -0.002573 -0.109124
## D7 0.003794 0.003612 0.092900 -0.036377 -0.015652 0.073580 -0.108700
## D8 0.003745 0.003848 -0.013023 0.003496 0.000305 0.000633 -0.109124
## D2 D3 D4 D5 D6 D7 D8
## A1 0.003726 0.003740 0.003739 0.003728 0.003744 0.003794 0.003745
## A2 0.003861 0.003847 0.003862 0.003884 0.003850 0.003612 0.003848
## B1 -0.014764 -0.009301 -0.013689 -0.010384 -0.013470 0.092900 -0.013023
## B2 0.003922 0.002092 0.003221 0.000706 0.003752 -0.036377 0.003496
## C1 -0.000628 -0.005287 0.000371 -0.002018 0.000559 -0.015652 0.000305
## C2 0.000753 0.023282 -0.001140 0.007979 -0.002573 0.073580 0.000633
## D1 -0.109130 -0.109119 -0.109146 -0.109177 -0.109124 -0.108700 -0.109124
## D2 0.229378 0.109140 0.109148 0.109189 0.109122 0.108691 0.109125
## D3 0.109140 0.339079 0.109144 0.109252 0.109105 0.109409 0.109146
## D4 0.109148 0.109144 0.214892 0.109196 0.109141 0.108702 0.109146
## D5 0.109189 0.109252 0.109196 0.218199 0.109173 0.108928 0.109184
## D6 0.109122 0.109105 0.109141 0.109173 0.208204 0.108679 0.109121
## D7 0.108691 0.109409 0.108702 0.108928 0.108679 0.228684 0.108796
## D8 0.109125 0.109146 0.109146 0.109184 0.109121 0.108796 0.224353
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 0.9778537 0.01436967 0.9232369 0.9938688
## unit2_2 0.8671848 0.04645862 0.7475705 0.9350444
## unit3_2 0.5937346 0.11349299 0.3675475 0.7861048
## unit4_2 0.7730427 0.05490708 0.6484410 0.8628246
## unit5_2 0.5827042 0.08017766 0.4225343 0.7271375
## unit6_2 0.9485505 0.02655958 0.8638533 0.9816751
## unit7_2 0.8708179 0.04553819 0.7530241 0.9371218
## unit8_2 0.9723082 0.01702207 0.9104743 0.9918182
## unit9_2 0.5822173 0.13217193 0.3245098 0.8016895
## unit10_2 0.9731035 0.01724051 0.9086650 0.9924569
model.fits[[model.number]]$real$gamma[1:5,]
## est se lower_0.95 upper_0.95
## gamma1_unit1 0.9996924 0.0010046539 0.8433980 0.9999995
## gamma1_unit2 0.9999757 0.0001033555 0.9075889 1.0000000
## gamma1_unit3 0.9025941 0.0939694135 0.5328320 0.9868909
## gamma1_unit4 0.9999574 0.0001718848 0.8959136 1.0000000
## gamma1_unit5 0.9999530 0.0001877284 0.8937504 1.0000000
model.fits[[model.number]]$real$epsilon[1:5,]
## est se lower_0.95 upper_0.95
## epsilon1_unit1 0.04082642 0.02458983 0.01227937 0.1271933
## epsilon1_unit2 0.14195691 0.04826181 0.07071810 0.2645308
## epsilon1_unit3 0.73124414 0.07766568 0.55635472 0.8551412
## epsilon1_unit4 0.33268391 0.06204251 0.22377341 0.4629850
## epsilon1_unit5 0.53803550 0.07327866 0.39522496 0.6748655
# 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 0.5389772 0.1342479 0.2884973 0.7712081
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 0.9778537 0.014369667 0.9232369 0.9938688
## unit1_3 0.3550013 0.078926573 0.2187927 0.5196055
## unit1_4 0.9886538 0.008641238 0.9506227 0.9974708
## unit1_5 0.9864276 0.011338135 0.9325292 0.9973903
## unit1_6 0.9984677 0.001930336 0.9821298 0.9998706
## unit1_7 0.2379044 0.076861914 0.1197183 0.4174365
## unit1_8 0.3404969 0.106954572 0.1687395 0.5676884
# 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 0.5389772 0.134247934 0.2884973 0.7712081
## unit1_2 0.9778537 0.014369667 0.9232369 0.9938688
## unit1_3 0.3550013 0.078926573 0.2187927 0.5196055
## unit1_4 0.9886538 0.008641238 0.9506227 0.9974708
## unit1_5 0.9864276 0.011338135 0.9325292 0.9973903
## unit1_6 0.9984677 0.001930336 0.9821298 0.9998706
## unit1_7 0.2379044 0.076861914 0.1197183 0.4174365
## unit1_8 0.3404969 0.106954572 0.1687395 0.5676884
# 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 0.040826416 0.024589827 0.0122793693 0.12719330
## epsilon2_unit1 0.659606468 0.078172814 0.4947859833 0.79313693
## epsilon3_unit1 0.031944327 0.020882859 0.0087072317 0.11029474
## epsilon4_unit1 0.003057438 0.003467866 0.0003296986 0.02772700
## epsilon5_unit1 0.001553140 0.001974345 0.0001282636 0.01851384
## epsilon6_unit1 0.763261673 0.076080552 0.5855032396 0.88036429
## epsilon7_unit1 0.432461017 0.067195860 0.3082285900 0.56581081
# 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 0.99969236 1.004654e-03 0.843398000 0.9999995
## gamma2_unit1 0.99999761 1.232883e-05 0.944055014 1.0000000
## gamma3_unit1 0.99999085 4.241293e-05 0.925052754 1.0000000
## gamma4_unit1 0.07020155 7.407161e-02 0.008100074 0.4110937
## gamma5_unit1 0.99998097 8.278001e-05 0.912278219 1.0000000
## gamma6_unit1 0.99772646 5.655703e-03 0.768060532 0.9999828
## gamma7_unit1 0.26962085 1.314819e-01 0.090696244 0.5773902
# 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 1.8142764 NA NA NA
## unit1_2 0.3630414 NA NA NA
## unit1_3 2.7849298 NA NA NA
## unit1_4 0.9977482 NA NA NA
## unit1_5 1.0122057 NA NA NA
## unit1_6 0.2382695 NA NA NA
## unit1_7 1.4312341 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 3.776799e+01 NA NA NA
## unit1_2 1.246520e-02 NA NA NA
## unit1_3 1.583155e+02 NA NA NA
## unit1_4 8.340915e-01 NA NA NA
## unit1_5 8.965542e+00 NA NA NA
## unit1_6 4.790801e-04 NA NA NA
## unit1_7 1.653877e+00 NA NA NA
# collect models and make AIC table
aic.table <- RPresence::createAicTable(model.fits)
aic.table$table
## Model AIC neg2ll npar
## 1 psi(LN)p(SEASON)gamma(maxQ)epsilon(minQ) 611.0108 583.0108 14
## 2 psi(LN)p(SEASON)gamma(minQ)epsilon(minQ) 637.8562 609.8562 14
## 3 psi(LN)p(SEASON)gamma(minQ)epsilon(maxQ) 693.8409 665.8409 14
## 4 psi()p(SEASON)gamma(SEASON)epsilon(SEASON) 713.5897 667.5897 23
## warn.conv warn.VC DAIC modlike wgt
## 1 0 0 0.0000 1 1
## 2 0 0 26.8454 0 0
## 3 0 0 82.8301 0 0
## 4 0 0 102.5789 0 0