Statia Snake Analysis
D. S. Fernandez del Viso
20200622
Metadata [to complete]
Season: each year
Visits: frequency; details on observational design
Loading data
Data from Hanna.
Note: occupancy = occurrence.
library(readxl)
Also2018 <- read_excel("Alsophis 2018 occupancy data_covariates.xlsx")
head(Also2018)Also2019 <- read_excel("Alsophis 2019 occupancy data_covariates.xlsx")
head(Also2019)Cleaning the names of the columns
# install.packages("janitor")
library(janitor)##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testAlso2018 <- clean_names(Also2018)
head(Also2018)Also2019 <- clean_names(Also2019)
head(Also2019)Using occSS
occSS: allows for psi or p to be modelled as a logistic function of site covariates or survey covariates, as specified by model. It includes a built in .time covariate which can be used for modelling p with time as a fixed effect, and .Time for a linear or quadratic trend. A built-in .b covariate corresponds to a behavioural effect, where detection depends on whether the species was detected on the previous occasion or not.
link: function to link the response on a (0,1) scale to a linear predictor on (-Inf, Inf). The canonical link is the logistic (“logit”) function, which has some nice theoretical properties and can be interpreted as the log of the odds of success. Other links are available, notably the cumulative standard normal (“probit”) link, which allows for Gibbs sampling with truncated normal distributions. For that reason, several of the Bayesian estimation functions in wiqid use the probit link.
library(wiqid)## Loading required package: HDInterval## Loading required package: mcmcOutput#extraction of occupancy matrix DH
Also2018DH <- Also2018[,1:24]
Also2019DH <- Also2019[,1:22]
#with link
#2018 logit
occSS(Also2018DH, link=c("logit"))## Call: occSS0(y = y, n = n, ci = ci, link = link)
##
## Real values:
## est lowCI uppCI
## psiHat 0.72756 0.42788 0.90509
## pHat 0.05016 0.03332 0.07486
##
## AIC: 438.1761#2019 logit
occSS(Also2019DH, link=c("logit"))## Call: occSS0(y = y, n = n, ci = ci, link = link)
##
## Real values:
## est lowCI uppCI
## psiHat 0.64983 0.37361 0.8524
## pHat 0.07282 0.04566 0.1142
##
## AIC: 403.9795#2018 probit
occSS(Also2018DH, link=c("probit"))## Call: occSS0(y = y, n = n, ci = ci, link = link)
##
## Real values:
## est lowCI uppCI
## psiHat 0.72756 0.43872 0.91390
## pHat 0.05016 0.03289 0.07399
##
## AIC: 438.1761#2019 probit
occSS(Also2019DH, link=c("probit"))## Call: occSS0(y = y, n = n, ci = ci, link = link)
##
## Real values:
## est lowCI uppCI
## psiHat 0.64982 0.37736 0.8604
## pHat 0.07282 0.04486 0.1125
##
## AIC: 403.9795#A built-in .b covariate corresponds to a behavioural effect, where detection depends on whether the species was detected on the previous occasion or not.
#2018
occSS(Also2018DH, p ~ .b)## Call: occSS(DH = Also2018DH, model = p ~ .b)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.76782 0.39536 0.94359
## p:1,1 0.04584 0.02897 0.07182
## p:27,2 0.08696 0.03302 0.20985
##
## AIC: 438.9366#2019
occSS(Also2019DH, p ~ .b)## Call: occSS(DH = Also2019DH, model = p ~ .b)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.64803 0.35781 0.8588
## p:1,1 0.07313 0.04370 0.1199
## p:7,2 0.07143 0.02322 0.1993
##
## AIC: 405.9781occSS0:implements a simple model with one parameter for probability of occupancy and one for probability of detection, ie. a psi(.) p(.) model.
#2018
# with Also2018DH 24 visits
y <- rowSums(Also2018DH, na.rm=TRUE)
n <- rowSums(!is.na(Also2018DH))
# occSS0 is a faster simple form of occSS
occSS0(y, n)## Call: occSS0(y = y, n = n)
##
## Real values:
## est lowCI uppCI
## psiHat 0.72756 0.42788 0.90509
## pHat 0.05016 0.03332 0.07486
##
## AIC: 438.1761# with Also2019DH 22 visits
y <- rowSums(Also2019DH, na.rm=TRUE)
n <- rowSums(!is.na(Also2019DH))
# occSS0 is a faster simple form of occSS
occSS0(y, n)## Call: occSS0(y = y, n = n)
##
## Real values:
## est lowCI uppCI
## psiHat 0.64983 0.37361 0.8524
## pHat 0.07282 0.04566 0.1142
##
## AIC: 403.9795Other occSSs
occSStime: allows for time-varying covariates that are the same across all sites, eg, moon-phase. A categorical time variable .time and a time trend .Time are built-in. A plot of detection probability vs time is produced if plot=TRUE.
#time effect accross all sites
#2018
time <- occSStime(Also2018DH, p ~ .time)
time[["real"]]## est lowCI uppCI
## psi 7.184172e-01 NA NA
## p1 6.873750e-02 NA NA
## p2 1.223951e-01 NA NA
## p3 3.965567e-02 NA NA
## p4 8.037591e-02 NA NA
## p5 2.009536e-02 NA NA
## p6 4.071421e-02 NA NA
## p7 4.124665e-02 NA NA
## p8 4.124665e-02 NA NA
## p9 2.062295e-02 NA NA
## p10 3.131567e-10 NA NA
## p11 2.062295e-02 NA NA
## p12 2.062295e-02 NA NA
## p13 4.939186e-02 NA NA
## p14 4.939186e-02 NA NA
## p15 7.408685e-02 NA NA
## p16 7.408682e-02 NA NA
## p17 2.682846e-02 NA NA
## p18 2.682846e-02 NA NA
## p19 6.646634e-02 NA NA
## p20 2.217472e-09 NA NA
## p21 3.834787e-02 NA NA
## p22 1.150430e-01 NA NA
## p23 7.669844e-02 NA NA
## p24 1.533911e-01 NA NA# trend linear
Time <- occSStime(Also2018DH, p ~ .Time)
Time[["real"]]## est lowCI uppCI
## psi 0.72950574 0.42789513 0.90675796
## p1 0.04789495 0.02631721 0.08560896
## p2 0.04810682 0.02722857 0.08361792
## p3 0.04831958 0.02812650 0.08178997
## p4 0.04853323 0.02900042 0.08013630
## p5 0.04874778 0.02983790 0.07867007
## p6 0.04896323 0.03062464 0.07740640
## p7 0.04917958 0.03134482 0.07636208
## p8 0.04939683 0.03198171 0.07555493
## p9 0.04961500 0.03251864 0.07500280
## p10 0.04983408 0.03294046 0.07472221
## p11 0.05005408 0.03323502 0.07472675
## p12 0.05027499 0.03339469 0.07502565
## p13 0.05049683 0.03341726 0.07562282
## p14 0.05071960 0.03330616 0.07651667
## p15 0.05094330 0.03306979 0.07770076
## p16 0.05116793 0.03272032 0.07916500
## p17 0.05139349 0.03227216 0.08089717
## p18 0.05162000 0.03174057 0.08288432
## p19 0.05184745 0.03114051 0.08511389
## p20 0.05207585 0.03048595 0.08757443
## p21 0.05230520 0.02978939 0.09025605
## p22 0.05253550 0.02906172 0.09315054
## p23 0.05276676 0.02831226 0.09625134
## p24 0.05299898 0.02754885 0.09955348# trend quadratic
Time2 <- occSStime(Also2018DH, p ~ .Time + I(.Time^2))
Time2[["real"]]## est lowCI uppCI
## psi 0.73744098 0.42776332 0.91344182
## p1 0.08878875 0.04673287 0.16224989
## p2 0.07340268 0.04151664 0.12654493
## p3 0.06166663 0.03671059 0.10179512
## p4 0.05270372 0.03231623 0.08482568
## p5 0.04585858 0.02840138 0.07323721
## p6 0.04064704 0.02504968 0.06530568
## p7 0.03671455 0.02230801 0.05985527
## p8 0.03380425 0.02017098 0.05612423
## p9 0.03173322 0.01859798 0.05363850
## p10 0.03037567 0.01753780 0.05211252
## p11 0.02965128 0.01694653 0.05138291
## p12 0.02951798 0.01679709 0.05136940
## p13 0.02996807 0.01708360 0.05205536
## p14 0.03102758 0.01782292 0.05348258
## p15 0.03275859 0.01905435 0.05575893
## p16 0.03526488 0.02083680 0.05908064
## p17 0.03870141 0.02324094 0.06377508
## p18 0.04328862 0.02633318 0.07037230
## p19 0.04933280 0.03015178 0.07971291
## p20 0.05725409 0.03468649 0.09308861
## p21 0.06762354 0.03988518 0.11239443
## p22 0.08120969 0.04569835 0.14026039
## p23 0.09903206 0.05213289 0.18010538
## p24 0.12241159 0.05927587 0.23592900#2019
time <- occSStime(Also2019DH, p ~ .time)## Warning in occSStime(Also2019DH, p ~ .time): Convergence may not have been
## reached (code 4 )
time[["real"]]## est lowCI uppCI
## psi 6.267804e-01 NA NA
## p1 5.909162e-02 NA NA
## p2 4.431473e-02 NA NA
## p3 7.452641e-02 NA NA
## p4 6.052163e-02 NA NA
## p5 4.722960e-02 NA NA
## p6 1.162085e-01 NA NA
## p7 8.509604e-02 NA NA
## p8 4.651314e-02 NA NA
## p9 7.227290e-02 NA NA
## p10 1.725648e-01 NA NA
## p11 1.288880e-01 NA NA
## p12 5.330366e-02 NA NA
## p13 1.072302e-20 NA NA
## p14 2.402532e-18 NA NA
## p15 1.054292e-01 NA NA
## p16 2.277943e-01 NA NA
## p17 9.466957e-13 NA NA
## p18 5.513520e-10 NA NA
## p19 2.751518e-07 NA NA
## p20 1.000000e+00 NA NA
## p21 1.309008e-02 NA NA
## p22 1.309008e-02 NA NA# trend linear
Time <- occSStime(Also2019DH, p ~ .Time)
Time[["real"]]## est lowCI uppCI
## psi 0.65534698 0.38984420 0.84982389
## p1 0.05356992 0.02910943 0.09654064
## p2 0.05660363 0.03213874 0.09780986
## p3 0.05979829 0.03528334 0.09958798
## p4 0.06316119 0.03847322 0.10201052
## p5 0.06669978 0.04161779 0.10523816
## p6 0.07042172 0.04461380 0.10944858
## p7 0.07433481 0.04736054 0.11482063
## p8 0.07844699 0.04977854 0.12151479
## p9 0.08276631 0.05182346 0.12965816
## p10 0.08730093 0.05348888 0.13934016
## p11 0.09205906 0.05479825 0.15061853
## p12 0.09704893 0.05579237 0.16352961
## p13 0.10227881 0.05651796 0.17809685
## p14 0.10775689 0.05702030 0.19433501
## p15 0.11349128 0.05733956 0.21225024
## p16 0.11948997 0.05750965 0.23183746
## p17 0.12576074 0.05755842 0.25307627
## p18 0.13231115 0.05750842 0.27592656
## p19 0.13914844 0.05737782 0.30032434
## p20 0.14627949 0.05718121 0.32617844
## p21 0.15371073 0.05693036 0.35336829
## p22 0.16144810 0.05663479 0.38174330# trend quadratic
Time2 <- occSStime(Also2019DH, p ~ .Time + I(.Time^2))
Time2[["real"]]## est lowCI uppCI
## psi 0.65511215 0.38927921 0.8498618
## p1 0.05298617 0.02527060 0.1077390
## p2 0.05625356 0.03042855 0.1016975
## p3 0.05966720 0.03494048 0.1000779
## p4 0.06322920 0.03842132 0.1023501
## p5 0.06694135 0.04093635 0.1076125
## p6 0.07080508 0.04288636 0.1147202
## p7 0.07482145 0.04470374 0.1226254
## p8 0.07899112 0.04668216 0.1305979
## p9 0.08331432 0.04894201 0.1383158
## p10 0.08779086 0.05141936 0.1459318
## p11 0.09242008 0.05383256 0.1541607
## p12 0.09720085 0.05565852 0.1643531
## p13 0.10213156 0.05621837 0.1784518
## p14 0.10721012 0.05494090 0.1987489
## p15 0.11243392 0.05165613 0.2275625
## p16 0.11779988 0.04665785 0.2670326
## p17 0.12330438 0.04052607 0.3189572
## p18 0.12894334 0.03390750 0.3843690
## p19 0.13471218 0.02737190 0.4627296
## p20 0.14060582 0.02134617 0.5510146
## p21 0.14661876 0.01609818 0.6433831
## p22 0.15274500 0.01174963 0.7321681Question: From above results, can we establish how many visits are enough?
occSScovSite: allows for site covariates but not for occasion or survey covariates.
#2018
y <- rowSums(Also2018DH, na.rm=TRUE)
n <- rowSums(!is.na(Also2018DH))
# model habitat
occSScovSite(y, n, model = psi ~ habitat, data = Also2018)## Call: occSScovSite(y = y, n = n, model = psi ~ habitat, data = Also2018)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.71460 0.41421 0.89864
## psi:4 0.68001 0.37316 0.88353
## psi:16 0.85043 0.23215 0.99073
## psi:21 0.74685 0.41320 0.92515
## psi:34 0.80375 0.33548 0.97078
## psi:47 0.64332 0.29646 0.88532
## psi:56 0.77659 0.38214 0.95131
## psi:75 0.82835 0.28336 0.98330
## p:1 0.05006 0.03343 0.07433
##
## AIC: 439.6284# model elevation
occSScovSite(y, n, model = psi ~ elevation, data = Also2018)## Call: occSScovSite(y = y, n = n, model = psi ~ elevation, data = Also2018)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.04796 0.001877 0.57437
## psi:2 0.05231 0.002262 0.57344
## psi:3 0.05870 0.002896 0.57245
## psi:4 0.06582 0.003704 0.57178
## psi:5 0.08484 0.006397 0.57171
## psi:6 0.11790 0.012998 0.57567
## psi:7 0.13119 0.016352 0.57835
## psi:8 0.13830 0.018311 0.58001
## psi:9 0.14957 0.021650 0.58294
## psi:10 0.16158 0.025523 0.58644
## psi:11 0.18331 0.033323 0.59376
## psi:12 0.22801 0.052288 0.61256
## psi:14 0.24451 0.060143 0.62075
## psi:15 0.30509 0.091619 0.65648
## psi:16 0.38042 0.132377 0.71189
## psi:17 0.48479 0.181698 0.79950
## psi:18 0.53049 0.198884 0.83719
## psi:19 0.70139 0.239795 0.94592
## psi:20 0.70774 0.240700 0.94872
## psi:21 0.74987 0.245732 0.96502
## psi:23 0.77736 0.248110 0.97365
## psi:24 0.78260 0.248480 0.97512
## psi:26 0.78774 0.248819 0.97651
## psi:27 0.81212 0.250059 0.98247
## psi:28 0.82568 0.250478 0.98532
## psi:29 0.83428 0.250636 0.98698
## psi:31 0.84254 0.250704 0.98845
## psi:32 0.86173 0.250518 0.99147
## psi:33 0.87892 0.249883 0.99372
## psi:34 0.88212 0.249709 0.99409
## psi:35 0.90781 0.247528 0.99662
## psi:36 0.91980 0.245911 0.99753
## psi:37 0.93420 0.243256 0.99841
## psi:38 0.93785 0.242425 0.99860
## psi:39 0.95342 0.237875 0.99926
## psi:40 0.95732 0.236400 0.99939
## psi:41 0.96091 0.234882 0.99949
## psi:42 0.96204 0.234367 0.99952
## psi:43 0.96818 0.231195 0.99968
## psi:44 0.97490 0.226777 0.99981
## psi:45 0.97635 0.225645 0.99983
## psi:46 0.97901 0.223352 0.99987
## psi:47 0.98022 0.222193 0.99988
## psi:48 0.98245 0.219852 0.99991
## psi:49 0.98777 0.212689 0.99996
## psi:50 0.98848 0.211479 0.99996
## psi:51 0.98979 0.209052 0.99997
## psi:52 0.99067 0.207225 0.99998
## psi:54 0.99290 0.201721 0.99999
## psi:55 0.99351 0.199883 0.99999
## psi:56 0.99576 0.191314 1.00000
## psi:57 0.99601 0.190094 1.00000
## psi:59 0.99613 0.189485 1.00000
## psi:60 0.99636 0.188267 1.00000
## psi:61 0.99705 0.184022 1.00000
## psi:62 0.99723 0.182815 1.00000
## psi:63 0.99731 0.182212 1.00000
## psi:64 0.99824 0.173852 1.00000
## psi:65 0.99835 0.172671 1.00000
## psi:66 0.99878 0.166823 1.00000
## psi:67 0.99885 0.165666 1.00000
## psi:68 0.99920 0.158812 1.00000
## psi:69 0.99925 0.157686 1.00000
## psi:70 0.99938 0.154335 1.00000
## psi:71 0.99954 0.148848 1.00000
## psi:72 0.99980 0.134680 1.00000
## psi:74 0.99987 0.127233 1.00000
## psi:75 0.99990 0.122914 1.00000
## psi:76 0.99993 0.116873 1.00000
## psi:77 0.99999 0.096192 1.00000
## psi:78 0.99999 0.084406 1.00000
## psi:79 0.99999 0.083713 1.00000
## psi:80 1.00000 0.078341 1.00000
## psi:81 0.85806 0.250593 0.99093
## p:1 0.04889 0.036218 0.06568
##
## AIC: 427.0842res1 <- occSScovSite(y, n, model = psi ~ elevation, data = Also2018)
# plot psi vs elevation
psi <- as.data.frame(res1[["real"]])
psi <- psi[1:81,1]
plot(Also2018$elevation,psi)
#2019
y <- rowSums(Also2019DH, na.rm=TRUE)
n <- rowSums(!is.na(Also2019DH))
# model habitat
occSScovSite(y, n, model = psi ~ habitat, data = Also2019)## Call: occSScovSite(y = y, n = n, model = psi ~ habitat, data = Also2019)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.5597 0.27822 0.8074
## psi:2 0.7404 0.28402 0.9535
## psi:6 0.6997 0.31386 0.9223
## psi:33 0.4591 0.14153 0.8137
## psi:40 0.6557 0.33019 0.8803
## psi:64 0.5095 0.21066 0.8017
## psi:67 0.7773 0.24875 0.9735
## psi:74 0.6087 0.32116 0.8365
## p:1 0.0711 0.04279 0.1159
##
## AIC: 404.991# model elevation
occSScovSite(y, n, model = psi ~ elevation, data = Also2019)## Call: occSScovSite(y = y, n = n, model = psi ~ elevation, data = Also2019)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.68615 0.32065 0.9101
## psi:3 0.68563 0.32218 0.9092
## psi:4 0.68528 0.32318 0.9085
## psi:5 0.68511 0.32369 0.9082
## psi:7 0.68476 0.32469 0.9075
## psi:8 0.68442 0.32568 0.9069
## psi:9 0.68355 0.32815 0.9052
## psi:10 0.68320 0.32912 0.9046
## psi:11 0.68181 0.33296 0.9019
## psi:13 0.68164 0.33344 0.9016
## psi:14 0.68042 0.33670 0.8993
## psi:15 0.67955 0.33898 0.8976
## psi:17 0.67902 0.34032 0.8966
## psi:18 0.67780 0.34339 0.8943
## psi:19 0.67762 0.34383 0.8940
## psi:20 0.67622 0.34720 0.8913
## psi:21 0.67552 0.34883 0.8900
## psi:23 0.67516 0.34963 0.8893
## psi:25 0.67499 0.35003 0.8890
## psi:27 0.67446 0.35121 0.8880
## psi:28 0.67393 0.35237 0.8870
## psi:29 0.67234 0.35571 0.8841
## psi:30 0.67146 0.35748 0.8825
## psi:32 0.67093 0.35850 0.8815
## psi:33 0.67057 0.35917 0.8809
## psi:34 0.66862 0.36266 0.8774
## psi:35 0.66844 0.36296 0.8771
## psi:36 0.66381 0.36962 0.8693
## psi:37 0.66327 0.37024 0.8684
## psi:38 0.66165 0.37191 0.8659
## psi:39 0.66129 0.37224 0.8654
## psi:40 0.66112 0.37240 0.8651
## psi:41 0.66076 0.37271 0.8646
## psi:42 0.66058 0.37286 0.8643
## psi:43 0.65986 0.37341 0.8633
## psi:44 0.65968 0.37354 0.8630
## psi:45 0.65896 0.37402 0.8620
## psi:46 0.65715 0.37493 0.8596
## psi:47 0.65679 0.37506 0.8592
## psi:48 0.65462 0.37552 0.8566
## psi:49 0.65443 0.37553 0.8564
## psi:50 0.65316 0.37549 0.8550
## psi:51 0.65171 0.37517 0.8536
## psi:52 0.65025 0.37458 0.8523
## psi:53 0.64861 0.37358 0.8510
## psi:54 0.64842 0.37345 0.8509
## psi:55 0.64788 0.37302 0.8505
## psi:56 0.64714 0.37239 0.8500
## psi:57 0.64678 0.37205 0.8498
## psi:58 0.64659 0.37187 0.8497
## psi:60 0.64568 0.37092 0.8492
## psi:61 0.64549 0.37071 0.8491
## psi:62 0.64513 0.37029 0.8489
## psi:63 0.64255 0.36685 0.8480
## psi:64 0.64145 0.36512 0.8477
## psi:66 0.64126 0.36481 0.8476
## psi:68 0.64108 0.36451 0.8476
## psi:70 0.63923 0.36120 0.8474
## psi:73 0.63645 0.35547 0.8475
## psi:74 0.63533 0.35293 0.8477
## psi:76 0.63496 0.35206 0.8478
## psi:77 0.63403 0.34980 0.8480
## psi:78 0.63347 0.34840 0.8482
## psi:80 0.63292 0.34698 0.8484
## psi:81 0.63161 0.34352 0.8489
## psi:82 0.63012 0.33937 0.8496
## psi:84 0.62937 0.33722 0.8500
## psi:86 0.62899 0.33613 0.8502
## psi:87 0.62824 0.33391 0.8507
## psi:89 0.62749 0.33164 0.8512
## psi:90 0.62637 0.32815 0.8519
## psi:91 0.62505 0.32395 0.8529
## psi:92 0.62468 0.32273 0.8532
## psi:93 0.62449 0.32212 0.8534
## psi:95 0.62430 0.32150 0.8535
## psi:97 0.62015 0.30738 0.8573
## psi:98 0.61713 0.29653 0.8604
## psi:99 0.61561 0.29096 0.8621
## psi:100 0.61352 0.28317 0.8645
## psi:101 0.61181 0.27672 0.8665
## psi:102 0.61085 0.27310 0.8677
## psi:104 0.60070 0.23427 0.8809
## psi:105 0.59588 0.21614 0.8875
## psi:106 0.59414 0.20971 0.8898
## psi:107 0.59066 0.19708 0.8945
## psi:108 0.59007 0.19501 0.8953
## p:1 0.07291 0.04582 0.1141
##
## AIC: 405.8647res2 <- occSScovSite(y, n, model = psi ~ elevation, data = Also2019)
# plot psi vs elevation
psi <- as.data.frame(res2[["real"]])
psi <- psi[1:108,1]
plot(Also2019$elevation,psi)
Royle-Nichols functions
These functions implement the Royle-Nichols method (Royle & Nichols 2003) for estimation of site occupancy allowing for abundance-induced heterogeneity in detection probability. Probability of detection is modelled as a function of the number of animals available for detection, n, and the probability of detection of an individual animal, r. Probability of occupancy is derived as the probability that n > 0.
Function occSSrn allows for site-specific covariates to be included in the model. occSSrnSite and occSSrn0 are fast alternatives that do not require a full detection history matrix.
# 2018
#basic model
occSSrn(Also2018DH)## Call: occSSrn0(y = y, n = n, ci = ci, link = link)
##
## Real values:
## est lowCI uppCI
## psiHat 0.83740 0.482172 0.99335
## lambdaHat 1.81643 0.658112 5.01348
## rHat 0.02048 0.007266 0.05636
##
## AIC: 435.0417# 2019
#basic model
occSSrn(Also2019DH)## Call: occSSrn0(y = y, n = n, ci = ci, link = link)
##
## Real values:
## est lowCI uppCI
## psiHat 0.79876 0.320573 0.9987
## lambdaHat 1.60326 0.386505 6.6504
## rHat 0.03012 0.006932 0.1214
##
## AIC: 405.8#lambda - elevation
#2018
occSSrn(Also2018DH, model = lambda ~ elevation, data = Also2018)## Call: occSSrnSite(y = y, n = n, model = model, data = data, ci = ci,
## link = link)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.99251 9.765e-02 1.000e+00
## psi:2 0.99306 9.921e-02 1.000e+00
## psi:3 0.99374 1.013e-01 1.000e+00
## psi:4 0.99436 1.035e-01 1.000e+00
## psi:5 0.99559 1.085e-01 1.000e+00
## psi:6 0.99687 1.155e-01 1.000e+00
## psi:7 0.99722 1.180e-01 1.000e+00
## psi:8 0.99739 1.192e-01 1.000e+00
## psi:9 0.99762 1.211e-01 1.000e+00
## psi:10 0.99783 1.230e-01 1.000e+00
## psi:11 0.99815 1.262e-01 1.000e+00
## psi:12 0.99863 1.322e-01 1.000e+00
## psi:14 0.99876 1.342e-01 1.000e+00
## psi:15 0.99913 1.413e-01 1.000e+00
## psi:16 0.99942 1.494e-01 1.000e+00
## psi:17 0.99967 1.603e-01 1.000e+00
## psi:18 0.99974 1.652e-01 1.000e+00
## psi:19 0.99991 1.860e-01 1.000e+00
## psi:20 0.99992 1.869e-01 1.000e+00
## psi:21 0.99994 1.934e-01 1.000e+00
## psi:23 0.99995 1.982e-01 1.000e+00
## psi:24 0.99996 1.991e-01 1.000e+00
## psi:26 0.99996 2.001e-01 1.000e+00
## psi:27 0.99997 2.050e-01 1.000e+00
## psi:28 0.99997 2.080e-01 1.000e+00
## psi:29 0.99998 2.100e-01 1.000e+00
## psi:31 0.99998 2.120e-01 1.000e+00
## psi:32 0.99998 2.171e-01 1.000e+00
## psi:33 0.99999 2.224e-01 1.000e+00
## psi:34 0.99999 2.234e-01 1.000e+00
## psi:35 0.99999 2.332e-01 1.000e+00
## psi:36 0.99999 2.387e-01 1.000e+00
## psi:37 1.00000 2.466e-01 1.000e+00
## psi:38 1.00000 2.489e-01 1.000e+00
## psi:39 1.00000 2.607e-01 1.000e+00
## psi:40 1.00000 2.643e-01 1.000e+00
## psi:41 1.00000 2.680e-01 1.000e+00
## psi:42 1.00000 2.692e-01 1.000e+00
## psi:43 1.00000 2.767e-01 1.000e+00
## psi:44 1.00000 2.868e-01 1.000e+00
## psi:45 1.00000 2.894e-01 1.000e+00
## psi:46 1.00000 2.946e-01 1.000e+00
## psi:47 1.00000 2.973e-01 1.000e+00
## psi:48 1.00000 3.026e-01 1.000e+00
## psi:49 1.00000 3.190e-01 1.000e+00
## psi:50 1.00000 3.218e-01 1.000e+00
## psi:51 1.00000 3.274e-01 1.000e+00
## psi:52 1.00000 3.317e-01 1.000e+00
## psi:54 1.00000 3.447e-01 1.000e+00
## psi:55 1.00000 3.491e-01 1.000e+00
## psi:56 1.00000 3.703e-01 1.000e+00
## psi:57 1.00000 3.734e-01 1.000e+00
## psi:59 1.00000 3.749e-01 1.000e+00
## psi:60 1.00000 3.780e-01 1.000e+00
## psi:61 1.00000 3.890e-01 1.000e+00
## psi:62 1.00000 3.922e-01 1.000e+00
## psi:63 1.00000 3.938e-01 1.000e+00
## psi:64 1.00000 4.165e-01 1.000e+00
## psi:65 1.00000 4.198e-01 1.000e+00
## psi:66 1.00000 4.366e-01 1.000e+00
## psi:67 1.00000 4.400e-01 1.000e+00
## psi:68 1.00000 4.606e-01 1.000e+00
## psi:69 1.00000 4.641e-01 1.000e+00
## psi:70 1.00000 4.746e-01 1.000e+00
## psi:71 1.00000 4.923e-01 1.000e+00
## psi:72 1.00000 5.415e-01 1.000e+00
## psi:74 1.00000 5.694e-01 1.000e+00
## psi:75 1.00000 5.863e-01 1.000e+00
## psi:76 1.00000 6.107e-01 1.000e+00
## psi:77 1.00000 7.022e-01 1.000e+00
## psi:78 1.00000 7.593e-01 1.000e+00
## psi:79 1.00000 7.627e-01 1.000e+00
## psi:80 1.00000 7.896e-01 1.000e+00
## psi:81 0.99998 2.161e-01 1.000e+00
## lambda:1 4.89463 1.028e-01 2.332e+02
## lambda:2 4.97051 1.045e-01 2.365e+02
## lambda:3 5.07351 1.068e-01 2.409e+02
## lambda:4 5.17865 1.092e-01 2.455e+02
## lambda:5 5.42325 1.149e-01 2.561e+02
## lambda:6 5.76745 1.228e-01 2.710e+02
## lambda:7 5.88697 1.255e-01 2.761e+02
## lambda:8 5.94766 1.269e-01 2.787e+02
## lambda:9 6.03986 1.290e-01 2.827e+02
## lambda:10 6.13350 1.312e-01 2.867e+02
## lambda:11 6.29279 1.349e-01 2.936e+02
## lambda:12 6.59001 1.418e-01 3.064e+02
## lambda:14 6.69217 1.441e-01 3.108e+02
## lambda:15 7.04429 1.523e-01 3.259e+02
## lambda:16 7.45306 1.618e-01 3.434e+02
## lambda:17 8.00779 1.747e-01 3.671e+02
## lambda:18 8.25800 1.805e-01 3.778e+02
## lambda:19 9.33948 2.058e-01 4.239e+02
## lambda:20 9.38750 2.069e-01 4.259e+02
## lambda:21 9.73059 2.149e-01 4.405e+02
## lambda:23 9.98330 2.208e-01 4.513e+02
## lambda:24 10.03462 2.221e-01 4.535e+02
## lambda:26 10.08621 2.233e-01 4.557e+02
## lambda:27 10.34816 2.294e-01 4.668e+02
## lambda:28 10.50858 2.332e-01 4.736e+02
## lambda:29 10.61691 2.357e-01 4.782e+02
## lambda:31 10.72636 2.383e-01 4.829e+02
## lambda:32 11.00493 2.448e-01 4.947e+02
## lambda:33 11.29074 2.515e-01 5.068e+02
## lambda:34 11.34878 2.529e-01 5.093e+02
## lambda:35 11.88481 2.655e-01 5.321e+02
## lambda:36 12.19347 2.727e-01 5.452e+02
## lambda:37 12.63911 2.832e-01 5.641e+02
## lambda:38 12.76940 2.863e-01 5.696e+02
## lambda:39 13.44128 3.021e-01 5.981e+02
## lambda:40 13.64965 3.070e-01 6.069e+02
## lambda:41 13.86126 3.120e-01 6.159e+02
## lambda:42 13.93252 3.136e-01 6.189e+02
## lambda:43 14.36785 3.239e-01 6.374e+02
## lambda:44 14.96952 3.380e-01 6.629e+02
## lambda:45 15.12383 3.417e-01 6.695e+02
## lambda:46 15.43725 3.490e-01 6.828e+02
## lambda:47 15.59638 3.528e-01 6.895e+02
## lambda:48 15.91959 3.604e-01 7.032e+02
## lambda:49 16.92997 3.842e-01 7.461e+02
## lambda:50 17.10449 3.883e-01 7.535e+02
## lambda:51 17.45895 3.966e-01 7.685e+02
## lambda:52 17.72961 4.030e-01 7.800e+02
## lambda:54 18.56702 4.227e-01 8.156e+02
## lambda:55 18.85486 4.295e-01 8.278e+02
## lambda:56 20.25823 4.625e-01 8.874e+02
## lambda:57 20.46706 4.674e-01 8.963e+02
## lambda:59 20.57228 4.699e-01 9.007e+02
## lambda:60 20.78435 4.748e-01 9.098e+02
## lambda:61 21.54396 4.927e-01 9.421e+02
## lambda:62 21.76605 4.979e-01 9.515e+02
## lambda:63 21.87795 5.005e-01 9.563e+02
## lambda:64 23.50632 5.387e-01 1.026e+03
## lambda:65 23.74864 5.444e-01 1.036e+03
## lambda:66 24.99820 5.737e-01 1.089e+03
## lambda:67 25.25590 5.798e-01 1.100e+03
## lambda:68 26.85882 6.173e-01 1.169e+03
## lambda:69 27.13570 6.238e-01 1.181e+03
## lambda:70 27.98356 6.436e-01 1.217e+03
## lambda:71 29.45596 6.780e-01 1.280e+03
## lambda:72 33.83003 7.798e-01 1.468e+03
## lambda:74 36.53487 8.426e-01 1.584e+03
## lambda:75 38.26050 8.825e-01 1.659e+03
## lambda:76 40.89797 9.435e-01 1.773e+03
## lambda:77 52.58057 1.211e+00 2.282e+03
## lambda:78 61.95688 1.424e+00 2.696e+03
## lambda:79 62.59557 1.438e+00 2.724e+03
## lambda:80 67.94785 1.559e+00 2.962e+03
## lambda:81 10.94864 2.435e-01 4.923e+02
## r:1 0.00201 4.515e-05 8.245e-02
##
## AIC: 417.319#2019
occSSrn(Also2019DH, model = lambda ~ elevation, data = Also2019)## Call: occSSrnSite(y = y, n = n, model = model, data = data, ci = ci,
## link = link)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.82458 0.306352 0.9997
## psi:3 0.82427 0.306762 0.9997
## psi:4 0.82407 0.307030 0.9997
## psi:5 0.82396 0.307164 0.9997
## psi:7 0.82376 0.307428 0.9997
## psi:8 0.82355 0.307689 0.9997
## psi:9 0.82304 0.308328 0.9997
## psi:10 0.82283 0.308578 0.9997
## psi:11 0.82201 0.309544 0.9997
## psi:13 0.82190 0.309661 0.9997
## psi:14 0.82118 0.310458 0.9997
## psi:15 0.82066 0.311002 0.9996
## psi:17 0.82035 0.311318 0.9996
## psi:18 0.81962 0.312025 0.9996
## psi:19 0.81952 0.312122 0.9996
## psi:20 0.81869 0.312871 0.9996
## psi:21 0.81827 0.313224 0.9996
## psi:23 0.81806 0.313395 0.9996
## psi:25 0.81796 0.313480 0.9996
## psi:27 0.81765 0.313727 0.9995
## psi:28 0.81733 0.313966 0.9995
## psi:29 0.81639 0.314633 0.9995
## psi:30 0.81587 0.314972 0.9995
## psi:32 0.81556 0.315164 0.9995
## psi:33 0.81535 0.315287 0.9995
## psi:34 0.81419 0.315897 0.9994
## psi:35 0.81409 0.315947 0.9994
## psi:36 0.81135 0.316898 0.9993
## psi:37 0.81103 0.316965 0.9993
## psi:38 0.81007 0.317112 0.9993
## psi:39 0.80986 0.317134 0.9993
## psi:40 0.80976 0.317143 0.9993
## psi:41 0.80954 0.317158 0.9993
## psi:42 0.80944 0.317165 0.9993
## psi:43 0.80901 0.317180 0.9992
## psi:44 0.80891 0.317181 0.9992
## psi:45 0.80848 0.317176 0.9992
## psi:46 0.80742 0.317092 0.9992
## psi:47 0.80720 0.317063 0.9992
## psi:48 0.80592 0.316803 0.9991
## psi:49 0.80581 0.316774 0.9991
## psi:50 0.80507 0.316547 0.9991
## psi:51 0.80421 0.316226 0.9991
## psi:52 0.80335 0.315839 0.9991
## psi:53 0.80238 0.315324 0.9990
## psi:54 0.80227 0.315262 0.9990
## psi:55 0.80195 0.315069 0.9990
## psi:56 0.80152 0.314797 0.9990
## psi:57 0.80130 0.314656 0.9990
## psi:58 0.80119 0.314583 0.9990
## psi:60 0.80065 0.314205 0.9990
## psi:61 0.80055 0.314126 0.9990
## psi:62 0.80033 0.313966 0.9990
## psi:63 0.79881 0.312729 0.9989
## psi:64 0.79816 0.312139 0.9989
## psi:66 0.79805 0.312037 0.9989
## psi:68 0.79795 0.311934 0.9989
## psi:70 0.79686 0.310849 0.9989
## psi:73 0.79522 0.309036 0.9989
## psi:74 0.79457 0.308249 0.9989
## psi:76 0.79435 0.307979 0.9989
## psi:77 0.79380 0.307287 0.9989
## psi:78 0.79347 0.306861 0.9989
## psi:80 0.79315 0.306426 0.9989
## psi:81 0.79238 0.305377 0.9989
## psi:82 0.79150 0.304123 0.9989
## psi:84 0.79106 0.303473 0.9989
## psi:86 0.79084 0.303143 0.9989
## psi:87 0.79040 0.302472 0.9989
## psi:89 0.78996 0.301786 0.9989
## psi:90 0.78930 0.300730 0.9989
## psi:91 0.78853 0.299459 0.9989
## psi:92 0.78831 0.299088 0.9989
## psi:93 0.78819 0.298901 0.9989
## psi:95 0.78808 0.298713 0.9989
## psi:97 0.78565 0.294369 0.9989
## psi:98 0.78387 0.290963 0.9989
## psi:99 0.78298 0.289185 0.9989
## psi:100 0.78175 0.286663 0.9989
## psi:101 0.78075 0.284535 0.9990
## psi:102 0.78019 0.283328 0.9990
## psi:104 0.77423 0.269565 0.9991
## psi:105 0.77140 0.262536 0.9992
## psi:106 0.77038 0.259935 0.9992
## psi:107 0.76833 0.254633 0.9993
## psi:108 0.76799 0.253737 0.9993
## lambda:1 1.74057 0.365791 8.2823
## lambda:3 1.73882 0.366381 8.2523
## lambda:4 1.73765 0.366769 8.2325
## lambda:5 1.73707 0.366962 8.2226
## lambda:7 1.73590 0.367343 8.2031
## lambda:8 1.73473 0.367720 8.1837
## lambda:9 1.73182 0.368643 8.1358
## lambda:10 1.73066 0.369004 8.1169
## lambda:11 1.72601 0.370403 8.0429
## lambda:13 1.72543 0.370573 8.0338
## lambda:14 1.72137 0.371728 7.9712
## lambda:15 1.71848 0.372517 7.9277
## lambda:17 1.71675 0.372975 7.9020
## lambda:18 1.71272 0.374003 7.8433
## lambda:19 1.71214 0.374144 7.8350
## lambda:20 1.70754 0.375233 7.7704
## lambda:21 1.70525 0.375747 7.7389
## lambda:23 1.70410 0.375997 7.7234
## lambda:25 1.70353 0.376119 7.7157
## lambda:27 1.70181 0.376479 7.6928
## lambda:28 1.70010 0.376828 7.6702
## lambda:29 1.69496 0.377801 7.6043
## lambda:30 1.69212 0.378295 7.5688
## lambda:32 1.69041 0.378576 7.5480
## lambda:33 1.68928 0.378756 7.5343
## lambda:34 1.68304 0.379647 7.4612
## lambda:35 1.68248 0.379719 7.4548
## lambda:36 1.66784 0.381111 7.2989
## lambda:37 1.66616 0.381209 7.2823
## lambda:38 1.66112 0.381424 7.2343
## lambda:39 1.66001 0.381456 7.2240
## lambda:40 1.65945 0.381470 7.2189
## lambda:41 1.65834 0.381493 7.2087
## lambda:42 1.65778 0.381502 7.2037
## lambda:43 1.65555 0.381524 7.1839
## lambda:44 1.65499 0.381526 7.1791
## lambda:45 1.65277 0.381518 7.1600
## lambda:46 1.64722 0.381395 7.1143
## lambda:47 1.64612 0.381353 7.1055
## lambda:48 1.63949 0.380972 7.0555
## lambda:49 1.63894 0.380930 7.0515
## lambda:50 1.63509 0.380598 7.0245
## lambda:51 1.63070 0.380128 6.9955
## lambda:52 1.62632 0.379562 6.9683
## lambda:53 1.62141 0.378810 6.9401
## lambda:54 1.62086 0.378719 6.9371
## lambda:55 1.61923 0.378437 6.9282
## lambda:56 1.61705 0.378041 6.9169
## lambda:57 1.61597 0.377834 6.9114
## lambda:58 1.61542 0.377728 6.9087
## lambda:60 1.61271 0.377177 6.8955
## lambda:61 1.61217 0.377062 6.8930
## lambda:62 1.61109 0.376828 6.8880
## lambda:63 1.60352 0.375027 6.8563
## lambda:64 1.60029 0.374168 6.8443
## lambda:66 1.59975 0.374020 6.8425
## lambda:68 1.59922 0.373870 6.8406
## lambda:70 1.59385 0.372295 6.8235
## lambda:73 1.58584 0.369668 6.8031
## lambda:74 1.58264 0.368530 6.7966
## lambda:76 1.58158 0.368139 6.7947
## lambda:77 1.57892 0.367140 6.7903
## lambda:78 1.57733 0.366524 6.7880
## lambda:80 1.57574 0.365897 6.7860
## lambda:81 1.57204 0.364386 6.7821
## lambda:82 1.56782 0.362582 6.7793
## lambda:84 1.56571 0.361649 6.7785
## lambda:86 1.56466 0.361175 6.7783
## lambda:87 1.56256 0.360212 6.7782
## lambda:89 1.56046 0.359229 6.7785
## lambda:90 1.55731 0.357719 6.7797
## lambda:91 1.55365 0.355902 6.7823
## lambda:92 1.55261 0.355372 6.7833
## lambda:93 1.55209 0.355106 6.7838
## lambda:95 1.55157 0.354838 6.7844
## lambda:97 1.54014 0.348663 6.8032
## lambda:98 1.53187 0.343847 6.8247
## lambda:99 1.52776 0.341344 6.8378
## lambda:100 1.52212 0.337802 6.8586
## lambda:101 1.51753 0.334823 6.8779
## lambda:102 1.51498 0.333138 6.8895
## lambda:104 1.48823 0.314115 7.0510
## lambda:105 1.47578 0.304538 7.1515
## lambda:106 1.47132 0.301017 7.1915
## lambda:107 1.46244 0.293879 7.2776
## lambda:108 1.46097 0.292678 7.2928
## r:1 0.02964 0.006592 0.1233
##
## AIC: 407.7252#lambda ~ habitat
#2018
occSSrn(Also2018DH, model = lambda ~ habitat, data = Also2018)## Call: occSSrnSite(y = y, n = n, model = model, data = data, ci = ci,
## link = link)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.88625 0.42186 0.9998
## psi:4 0.85511 0.38760 0.9995
## psi:16 0.98018 0.55518 1.0000
## psi:21 0.91336 0.45398 0.9999
## psi:34 0.95481 0.51025 1.0000
## psi:47 0.82036 0.35187 0.9989
## psi:56 0.93622 0.48352 1.0000
## psi:75 0.96934 0.53411 1.0000
## lambda:1 2.17374 0.54793 8.6236
## lambda:4 1.93181 0.49038 7.6103
## lambda:16 3.92122 0.81008 18.9807
## lambda:21 2.44597 0.60509 9.8873
## lambda:34 3.09696 0.71387 13.4355
## lambda:47 1.71681 0.43367 6.7966
## lambda:56 2.75228 0.66072 11.4648
## lambda:75 3.48480 0.76381 15.8991
## r:1 0.01518 0.00365 0.0609
##
## AIC: 434.0306#2019
occSSrn(Also2019DH, model = lambda ~ habitat, data = Also2019)## Call: occSSrnSite(y = y, n = n, model = model, data = data, ci = ci,
## link = link)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.75619 0.267771 0.9983
## psi:2 0.83372 0.336065 0.9996
## psi:6 0.81541 0.323612 0.9993
## psi:33 0.71401 0.221515 0.9981
## psi:40 0.79633 0.307754 0.9990
## psi:64 0.73530 0.245032 0.9981
## psi:67 0.85118 0.344893 0.9998
## psi:74 0.77656 0.288930 0.9986
## lambda:1 1.41135 0.311662 6.3912
## lambda:2 1.79408 0.409571 7.8588
## lambda:6 1.68963 0.390989 7.3016
## lambda:33 1.25179 0.250405 6.2577
## lambda:40 1.59125 0.367814 6.8841
## lambda:64 1.32917 0.281081 6.2854
## lambda:67 1.90500 0.422956 8.5801
## lambda:74 1.49860 0.340985 6.5862
## r:1 0.02944 0.006507 0.1232
##
## AIC: 407.1395##Graphs
#2018
lamelev18 <- occSSrn(Also2018DH, model = lambda ~ elevation, data = Also2018)
lamelev18 <- as.data.frame(lamelev18[["real"]])
#psi vs elevation
plot(Also2018$elevation,lamelev18[1:81,1])
#lambda vs elevation
plot(Also2018$elevation,lamelev18[82:162,1])
###
#2019
lamelev19 <- occSSrn(Also2019DH, model = lambda ~ elevation, data = Also2019)
lamelev19 <- as.data.frame(lamelev19[["real"]])
#psi vs elevation
plot(Also2019$elevation,lamelev19[1:108,1])
#lambda vs elevation
plot(Also2019$elevation,lamelev19[109:216,1])
++++++++++++++++++++++++++++ EXAMPLE MS - ============================
#2018 alone
occMS(Also2018DH, 24, data=Also2018)## Call: occMS0(DH = DH, occsPerSeason = occsPerSeason, ci = ci, verify = FALSE)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi1 0.72756 NA NA
## gamma 0.50000 NA NA
## p 0.05016 NA NA
##
## AIC: NA##GrandSkins data set
data(GrandSkinks)
head(GrandSkinks)data(weta)
head(weta)#testing
data(GrandSkinks)
DH <- GrandSkinks[, 1:15]
occMS0(DH, 3)## Call: occMS0(DH = DH, occsPerSeason = 3)
##
## Real values:
## est lowCI uppCI
## psi1 0.39450 0.33475 0.45759
## gamma 0.06829 0.04839 0.09554
## epsilon 0.10001 0.06948 0.14192
## p 0.68915 0.64222 0.73250
##
## AIC: 1775.015occMStime(DH, 3, model=list(gamma ~ .interval, epsilon~1, p~.season))## Call: occMStime(DH = DH, occsPerSeason = 3, model = list(gamma ~ .interval,
## epsilon ~ 1, p ~ .season))
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi1 0.38496 0.319995 0.4543
## gam1 0.11860 0.061710 0.2159
## gam2 0.01527 0.001163 0.1711
## gam3 0.07222 0.037156 0.1357
## gam4 0.09667 0.052560 0.1711
## eps1 0.09865 0.067173 0.1426
## p1 0.69273 0.569878 0.7932
## p2 0.65359 0.563057 0.7342
## p3 0.69616 0.611982 0.7690
## p4 0.82857 0.722633 0.8997
## p5 0.61062 0.507160 0.7050
##
## AIC: 1771.799occMScovSite(DH, 3,
model=list(psi1~habitat, gamma ~ .interval, epsilon~habitat, p~.season),
data=GrandSkinks)## Call: occMScovSite(DH = DH, occsPerSeason = 3, model = list(psi1 ~
## habitat, gamma ~ .interval, epsilon ~ habitat, p ~ .season),
## data = GrandSkinks)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.22640 0.153977 0.3200
## psi:138 0.52535 0.428414 0.6204
## gamma:1,1 0.09092 0.037891 0.2025
## gamma:1,2 0.01995 0.002735 0.1313
## gamma:1,3 0.07338 0.037610 0.1383
## gamma:1,4 0.09892 0.054518 0.1729
## epsilon:1,1 0.17427 0.104875 0.2754
## epsilon:138,1 0.07686 0.046198 0.1252
## p:1,1 0.64860 0.515433 0.7621
## p:1,2 0.65864 0.568371 0.7387
## p:1,3 0.69303 0.608569 0.7663
## p:1,4 0.82998 0.729596 0.8983
## p:1,5 0.60978 0.508240 0.7026
##
## AIC: 1745.792occMS(DH, 3,
model=list(psi1~habitat, gamma ~ .interval, epsilon~habitat, p~.season),
data=GrandSkinks)## Call: occMS(DH = DH, occsPerSeason = 3, model = list(psi1 ~ habitat,
## gamma ~ .interval, epsilon ~ habitat, p ~ .season), data = GrandSkinks)
##
## Real values (duplicates omitted):
## est lowCI uppCI
## psi:1 0.22640 0.153977 0.3200
## psi:138 0.52535 0.428414 0.6204
## gamma:1,1 0.09092 0.037891 0.2025
## gamma:1,2 0.01995 0.002735 0.1313
## gamma:1,3 0.07338 0.037610 0.1383
## gamma:1,4 0.09892 0.054518 0.1729
## epsilon:1,1 0.17427 0.104875 0.2754
## epsilon:138,1 0.07686 0.046198 0.1252
## p:1,1 0.64860 0.515433 0.7621
## p:1,4 0.65864 0.568371 0.7387
## p:1,7 0.69303 0.608569 0.7663
## p:1,10 0.82998 0.729596 0.8983
## p:1,13 0.60978 0.508240 0.7026
##
## AIC: 1745.792