MSJollySeber: Multistate Jolly Seber
In RMark: R Code for Mark Analysis
MSJollySeber R Documentation
Multistate Jolly Seber
Description
Multistate Jolly Seber
Author(s)
Jeff Laake (simulated data from Gary White)
Examples
# import the sample data
pathtodata=paste(path.package("RMark"),"extdata",sep="/")
msjs3=convert.inp(paste(pathtodata,"MSJS3state.inp",sep="/"))
# process with MSJollySeber model and strata labels
dp=process.data(msjs3,model="MSJollySeber",strata.labels=c("E","F","G"))
# create design data and use time pims for Psi and pent because global model used
# to simulate data only had time/strata effects
ddl=make.design.data(dp,parameters=list(Psi=list(pim.type="time"),pent=list(pim.type="time")))
# p for first occasion is confounded because pent since it is time dependent
# p for last occasion is confounded because survival is time dependent
# Thus times 1 and 2 are combined as 1 and times 3 and 4 are combined as time 2
ddl$p$time[ddl$p$time==2]=1
ddl$p$time[ddl$p$time==3]=2
ddl$p$time[ddl$p$time==4]=2
ddl$p=droplevels(ddl$p)
# run global model that was used to generate the data. These results match what was produced in
# MARK .dbf that Gary provided to me.
model=mark(dp,ddl=ddl,model.parameters=list(S=list(formula=~-1+stratum:time,link="sin"),
Psi=list(formula=~-1+stratum:tostratum:time),
pent=list(formula=~stratum*time),
p=list(formula=~-1+stratum:time,link="sin"),
pi=list(formula=~-1+stratum)),delete=TRUE)
# Examine derived abundance estimates
model$results$derived
#$`N*`
# estimate se lcl ucl
#1 1200 22.88454 1159.6 1249.699
#
#$`N_TSA(s*t)`
#estimate se lcl ucl
#1 180.00027 55.22876 126.98046 381.1247
#2 249.59448 73.28850 177.32650 511.2946
#3 377.20653 92.15176 276.37043 681.2139
#4 422.14994 105.74592 307.97920 774.6687
#5 119.99866 38.18683 84.16549 261.3716
#6 193.39459 57.72862 137.03323 401.0494
#7 296.19950 71.38362 217.52522 530.3700
#8 350.09106 86.97180 255.76981 639.0182
#9 60.00109 21.23089 41.40889 142.5095
#10 120.99888 38.29786 84.93901 262.4367
#11 194.19013 45.93703 143.06755 343.7371
#12 248.60043 61.37312 181.81724 451.9540
msjs3$grp=factor(as.numeric(runif(1686)<0.5))
dp=process.data(msjs3,model="MSJollySeber",strata.labels=c("E","F","G"),groups="grp")
# create design data and use time pims for Psi and pent because global model used
# to simulate data only had time/strata effects
ddl=make.design.data(dp,parameters=list(Psi=list(pim.type="time"),pent=list(pim.type="time")))
# p for first occasion is confounded because pent since it is time dependent
# p for last occasion is confounded because survival is time dependent
# Thus times 1 and 2 are combined as 1 and times 3 and 4 are combined as time 2
ddl$p$time[ddl$p$time==2]=1
ddl$p$time[ddl$p$time==3]=2
ddl$p$time[ddl$p$time==4]=2
ddl$p=droplevels(ddl$p)
model=mark(dp,ddl=ddl,model.parameters=list(S=list(formula=~-1+stratum:time,link="sin"),
Psi=list(formula=~-1+stratum:tostratum:time),
pent=list(formula=~stratum*time),
p=list(formula=~-1+stratum:time,link="sin"),
pi=list(formula=~-1+stratum)),delete=TRUE)
# Note that I don't have groups separated out; probably need to add these but
# first 12 are for group 1 and second 12 are for group 2
model$results$derived
#$`N*`
#estimate se lcl ucl
#1 576.5519 10.99515 557.1415 600.4303
#2 623.4481 11.88948 602.4589 649.2687
#
#$`N_TSA(s*t)`
#estimate se lcl ucl
#1 86.48235 26.53543 59.74870 179.73562
#2 119.91930 35.21259 87.08794 250.88028
#3 181.23323 44.27568 158.25938 412.81875
#4 202.82678 50.80719 138.39582 351.52314
#5 57.65490 18.34742 40.00284 124.36399
#6 92.91895 27.73659 69.10044 202.16049
#7 142.31136 34.29734 108.00098 263.47936
#8 168.20397 41.78690 132.40312 333.04237
#9 28.82821 10.20072 22.06997 76.00783
#10 58.13528 18.40095 45.08338 139.96467
#11 93.30069 22.07105 72.55341 174.90726
#12 119.44262 29.48746 89.82760 223.31249
#13 93.51673 28.69379 67.33555 201.88406
#14 129.67341 38.07675 90.39324 261.12212
#15 195.97456 47.87702 131.43369 328.98380
#16 219.32450 54.93979 175.00065 444.87035
#17 62.34450 19.83979 44.17969 137.09149
#18 100.47688 29.99266 68.46238 201.34980
#19 153.88682 37.08705 110.11103 269.06238
#20 181.88551 45.18580 126.26801 317.46649
#21 31.17307 11.03043 19.84077 69.37125
#22 62.86395 19.89767 40.98810 128.26377
#23 100.88968 23.86628 71.50113 172.47586
#24 129.15797 31.88594 92.33709 229.95112
#
#$`N_TSA(t)`
#estimate se lcl ucl
#1 172.9655 17.57292 146.1440 216.6946
#2 270.9735 17.86549 242.2069 313.2976
#3 416.8453 21.54149 382.6336 468.5324
#4 490.4734 29.73746 440.1191 557.6749
#5 187.0343 19.00228 158.0680 234.3750
#6 293.0142 19.31866 260.1141 336.4346
#7 450.7511 23.29365 409.1688 500.7981
#8 530.3680 32.15627 476.2905 603.5088
#
#$`N(s*t)`
#estimate se lcl ucl
#1 81.67410 23.937921 58.04354 167.08272
#2 126.83858 37.175211 90.14070 259.47682
#3 259.54924 63.478782 190.12917 469.06140
#4 148.55131 36.331666 108.81919 268.46423
#5 56.08549 16.675093 39.76587 115.96055
#6 104.09903 30.950270 73.80855 215.23180
#7 157.41243 37.901060 115.62023 281.69787
#8 204.11663 49.146288 149.92471 365.27751
#9 34.90008 10.950259 24.53295 75.18089
#10 71.01092 22.280408 49.91700 152.96997
#11 109.13438 25.654572 80.49076 192.43787
#12 130.02727 30.565932 95.90006 229.27854
#13 98.32499 28.818144 69.87688 201.14587
#14 122.75425 35.978133 87.23809 251.12141
#15 117.65857 28.776129 86.18914 212.63438
#16 273.60001 66.915223 200.42187 494.45417
#17 63.91392 19.002608 45.31640 132.14636
#18 89.29676 26.549323 63.31340 184.62710
#19 138.78578 33.416218 101.93886 248.36450
#20 145.97290 35.146702 107.21784 261.22623
#21 25.10117 7.875752 17.64482 54.07233
#22 49.98828 15.684338 35.13917 107.68352
#23 85.05591 19.994369 62.73197 149.98004
#24 118.57332 27.873415 87.45234 209.08166
#
#$`N(t)`
#estimate se lcl ucl
#1 172.6597 12.84355 151.7126 202.7258
#2 301.9485 20.11213 268.5098 348.1893
#3 526.0961 35.72379 466.8913 608.4782
#4 482.6952 31.12914 430.6676 553.9137
#5 187.3401 16.99719 160.7135 228.6425
#6 262.0393 19.37969 230.3986 307.3613
#7 341.5003 20.10434 307.4281 386.8953
#8 538.1462 38.90093 474.3711 628.7710
#
# For better labels and interpretation see MARK output
#Total Abundance Estimates
#Grp. N*-hat Standard Error Lower Upper
#---- -------------- -------------- -------------- --------------
#1 576.55192 10.995146 557.14154 600.43028
#2 623.44807 11.889480 602.45888 649.26868
#
#Abundance Estimates
#Grp. Str. Occ. Abundance Standard Error Lower Upper
#---- ---- ---- -------------- -------------- -------------- --------------
#1 E 1 86.482346 26.535427 59.748702 179.73562
#1 E 2 119.91930 35.212594 87.087938 250.88028
#1 E 3 181.23323 44.275677 158.25938 412.81875
#1 E 4 202.82678 50.807187 138.39582 351.52314
#1 F 1 57.654903 18.347425 40.002836 124.36399
#1 F 2 92.918950 27.736591 69.100442 202.16049
#1 F 3 142.31136 34.297342 108.00098 263.47936
#1 F 4 168.20397 41.786899 132.40312 333.04237
#1 G 1 28.828208 10.200716 22.069972 76.007829
#1 G 2 58.135284 18.400953 45.083379 139.96467
#1 G 3 93.300695 22.071048 72.553406 174.90726
#1 G 4 119.44262 29.487455 89.827598 223.31249
#2 E 1 93.516733 28.693792 67.335545 201.88406
#2 E 2 129.67341 38.076751 90.393237 261.12212
#2 E 3 195.97456 47.877016 131.43369 328.98380
#2 E 4 219.32450 54.939793 175.00065 444.87035
#2 F 1 62.344496 19.839786 44.179691 137.09149
#2 F 2 100.47688 29.992658 68.462381 201.34980
#2 F 3 153.88682 37.087053 110.11103 269.06238
#2 F 4 181.88551 45.185803 126.26801 317.46649
#2 G 1 31.173066 11.030432 19.840768 69.371247
#2 G 2 62.863950 19.897668 40.988101 128.26377
#2 G 3 100.88968 23.866285 71.501133 172.47586
#2 G 4 129.15797 31.885935 92.337089 229.95112
#
#Summed Abundance Estimates
#Grp. Occ. Abundance Standard Error Lower Upper
#---- ---- -------------- -------------- -------------- --------------
#1 1 172.96546 17.572916 146.14398 216.69465
#1 2 270.97353 17.865494 242.20692 313.29762
#1 3 416.84528 21.541486 382.63361 468.53243
#1 4 490.47337 29.737455 440.11912 557.67492
#2 1 187.03430 19.002280 158.06800 234.37501
#2 2 293.01425 19.318655 260.11406 336.43459
#2 3 450.75106 23.293649 409.16876 500.79814
#2 4 530.36798 32.156270 476.29049 603.50885
#
#Abundance Estimates without TSA
#Grp. Str. Occ. Abundance Standard Error Lower Upper
#---- ---- ---- -------------- -------------- -------------- --------------
#1 E 1 81.674097 23.937921 58.043545 167.08272
#1 E 2 126.83858 37.175211 90.140704 259.47682
#1 E 3 259.54924 63.478782 190.12917 469.06140
#1 E 4 148.55131 36.331666 108.81919 268.46423
#1 F 1 56.085487 16.675093 39.765869 115.96055
#1 F 2 104.09903 30.950270 73.808547 215.23180
#1 F 3 157.41243 37.901060 115.62023 281.69787
#1 F 4 204.11663 49.146288 149.92471 365.27751
#1 G 1 34.900079 10.950259 24.532949 75.180890
#1 G 2 71.010918 22.280408 49.917000 152.96997
#1 G 3 109.13438 25.654572 80.490758 192.43787
#1 G 4 130.02727 30.565932 95.900063 229.27854
#2 E 1 98.324993 28.818144 69.876881 201.14587
#2 E 2 122.75425 35.978133 87.238086 251.12141
#2 E 3 117.65857 28.776129 86.189140 212.63438
#2 E 4 273.60001 66.915223 200.42187 494.45417
#2 F 1 63.913917 19.002608 45.316401 132.14636
#2 F 2 89.296755 26.549323 63.313404 184.62710
#2 F 3 138.78578 33.416218 101.93886 248.36450
#2 F 4 145.97290 35.146702 107.21784 261.22623
#2 G 1 25.101172 7.8757515 17.644825 54.072326
#2 G 2 49.988280 15.684338 35.139173 107.68352
#2 G 3 85.055913 19.994369 62.731974 149.98004
#2 G 4 118.57332 27.873415 87.452338 209.08166
#
#Summed Abundance Estimates without TSA
#Grp. Occ. Abundance Standard Error Lower Upper
#---- ---- -------------- -------------- -------------- --------------
#1 1 172.65966 12.843549 151.71260 202.72578
#1 2 301.94852 20.112126 268.50983 348.18933
#1 3 526.09605 35.723790 466.89129 608.47820
#1 4 482.69521 31.129138 430.66757 553.91371
#2 1 187.34008 16.997192 160.71347 228.64245
#2 2 262.03929 19.379689 230.39863 307.36128
#2 3 341.50026 20.104344 307.42812 386.89528
#2 4 538.14624 38.900929 474.37108 628.77101
#
#Expected Ingress, Egress, and Residence Times 1&2
#Grp. Estimate Standard Error Lower Upper
#---- -------------- -------------- -------------- --------------
#1 2.4000144 0.0650048 2.2726051 2.5274237
#1 3.7437021 0.0583736 3.6292898 3.8581145
#1 2.3436877 0.0811437 2.1846461 2.5027293
#1 3.4389990 0.1604949 3.1244290 3.7535689
#2 2.4000144 0.0650048 2.2726051 2.5274237
#2 3.7437021 0.0583736 3.6292898 3.8581145
#2 2.3436877 0.0811437 2.1846461 2.5027293
#2 3.4389990 0.1604949 3.1244290 3.7535689
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| MSJollySeber | R Documentation |
Multistate Jolly Seber
Description
Multistate Jolly Seber
Author(s)
Jeff Laake (simulated data from Gary White)
Examples
# import the sample data
pathtodata=paste(path.package("RMark"),"extdata",sep="/")
msjs3=convert.inp(paste(pathtodata,"MSJS3state.inp",sep="/"))
# process with MSJollySeber model and strata labels
dp=process.data(msjs3,model="MSJollySeber",strata.labels=c("E","F","G"))
# create design data and use time pims for Psi and pent because global model used
# to simulate data only had time/strata effects
ddl=make.design.data(dp,parameters=list(Psi=list(pim.type="time"),pent=list(pim.type="time")))
# p for first occasion is confounded because pent since it is time dependent
# p for last occasion is confounded because survival is time dependent
# Thus times 1 and 2 are combined as 1 and times 3 and 4 are combined as time 2
ddl$p$time[ddl$p$time==2]=1
ddl$p$time[ddl$p$time==3]=2
ddl$p$time[ddl$p$time==4]=2
ddl$p=droplevels(ddl$p)
# run global model that was used to generate the data. These results match what was produced in
# MARK .dbf that Gary provided to me.
model=mark(dp,ddl=ddl,model.parameters=list(S=list(formula=~-1+stratum:time,link="sin"),
Psi=list(formula=~-1+stratum:tostratum:time),
pent=list(formula=~stratum*time),
p=list(formula=~-1+stratum:time,link="sin"),
pi=list(formula=~-1+stratum)),delete=TRUE)
# Examine derived abundance estimates
model$results$derived
#$`N*`
# estimate se lcl ucl
#1 1200 22.88454 1159.6 1249.699
#
#$`N_TSA(s*t)`
#estimate se lcl ucl
#1 180.00027 55.22876 126.98046 381.1247
#2 249.59448 73.28850 177.32650 511.2946
#3 377.20653 92.15176 276.37043 681.2139
#4 422.14994 105.74592 307.97920 774.6687
#5 119.99866 38.18683 84.16549 261.3716
#6 193.39459 57.72862 137.03323 401.0494
#7 296.19950 71.38362 217.52522 530.3700
#8 350.09106 86.97180 255.76981 639.0182
#9 60.00109 21.23089 41.40889 142.5095
#10 120.99888 38.29786 84.93901 262.4367
#11 194.19013 45.93703 143.06755 343.7371
#12 248.60043 61.37312 181.81724 451.9540
msjs3$grp=factor(as.numeric(runif(1686)<0.5))
dp=process.data(msjs3,model="MSJollySeber",strata.labels=c("E","F","G"),groups="grp")
# create design data and use time pims for Psi and pent because global model used
# to simulate data only had time/strata effects
ddl=make.design.data(dp,parameters=list(Psi=list(pim.type="time"),pent=list(pim.type="time")))
# p for first occasion is confounded because pent since it is time dependent
# p for last occasion is confounded because survival is time dependent
# Thus times 1 and 2 are combined as 1 and times 3 and 4 are combined as time 2
ddl$p$time[ddl$p$time==2]=1
ddl$p$time[ddl$p$time==3]=2
ddl$p$time[ddl$p$time==4]=2
ddl$p=droplevels(ddl$p)
model=mark(dp,ddl=ddl,model.parameters=list(S=list(formula=~-1+stratum:time,link="sin"),
Psi=list(formula=~-1+stratum:tostratum:time),
pent=list(formula=~stratum*time),
p=list(formula=~-1+stratum:time,link="sin"),
pi=list(formula=~-1+stratum)),delete=TRUE)
# Note that I don't have groups separated out; probably need to add these but
# first 12 are for group 1 and second 12 are for group 2
model$results$derived
#$`N*`
#estimate se lcl ucl
#1 576.5519 10.99515 557.1415 600.4303
#2 623.4481 11.88948 602.4589 649.2687
#
#$`N_TSA(s*t)`
#estimate se lcl ucl
#1 86.48235 26.53543 59.74870 179.73562
#2 119.91930 35.21259 87.08794 250.88028
#3 181.23323 44.27568 158.25938 412.81875
#4 202.82678 50.80719 138.39582 351.52314
#5 57.65490 18.34742 40.00284 124.36399
#6 92.91895 27.73659 69.10044 202.16049
#7 142.31136 34.29734 108.00098 263.47936
#8 168.20397 41.78690 132.40312 333.04237
#9 28.82821 10.20072 22.06997 76.00783
#10 58.13528 18.40095 45.08338 139.96467
#11 93.30069 22.07105 72.55341 174.90726
#12 119.44262 29.48746 89.82760 223.31249
#13 93.51673 28.69379 67.33555 201.88406
#14 129.67341 38.07675 90.39324 261.12212
#15 195.97456 47.87702 131.43369 328.98380
#16 219.32450 54.93979 175.00065 444.87035
#17 62.34450 19.83979 44.17969 137.09149
#18 100.47688 29.99266 68.46238 201.34980
#19 153.88682 37.08705 110.11103 269.06238
#20 181.88551 45.18580 126.26801 317.46649
#21 31.17307 11.03043 19.84077 69.37125
#22 62.86395 19.89767 40.98810 128.26377
#23 100.88968 23.86628 71.50113 172.47586
#24 129.15797 31.88594 92.33709 229.95112
#
#$`N_TSA(t)`
#estimate se lcl ucl
#1 172.9655 17.57292 146.1440 216.6946
#2 270.9735 17.86549 242.2069 313.2976
#3 416.8453 21.54149 382.6336 468.5324
#4 490.4734 29.73746 440.1191 557.6749
#5 187.0343 19.00228 158.0680 234.3750
#6 293.0142 19.31866 260.1141 336.4346
#7 450.7511 23.29365 409.1688 500.7981
#8 530.3680 32.15627 476.2905 603.5088
#
#$`N(s*t)`
#estimate se lcl ucl
#1 81.67410 23.937921 58.04354 167.08272
#2 126.83858 37.175211 90.14070 259.47682
#3 259.54924 63.478782 190.12917 469.06140
#4 148.55131 36.331666 108.81919 268.46423
#5 56.08549 16.675093 39.76587 115.96055
#6 104.09903 30.950270 73.80855 215.23180
#7 157.41243 37.901060 115.62023 281.69787
#8 204.11663 49.146288 149.92471 365.27751
#9 34.90008 10.950259 24.53295 75.18089
#10 71.01092 22.280408 49.91700 152.96997
#11 109.13438 25.654572 80.49076 192.43787
#12 130.02727 30.565932 95.90006 229.27854
#13 98.32499 28.818144 69.87688 201.14587
#14 122.75425 35.978133 87.23809 251.12141
#15 117.65857 28.776129 86.18914 212.63438
#16 273.60001 66.915223 200.42187 494.45417
#17 63.91392 19.002608 45.31640 132.14636
#18 89.29676 26.549323 63.31340 184.62710
#19 138.78578 33.416218 101.93886 248.36450
#20 145.97290 35.146702 107.21784 261.22623
#21 25.10117 7.875752 17.64482 54.07233
#22 49.98828 15.684338 35.13917 107.68352
#23 85.05591 19.994369 62.73197 149.98004
#24 118.57332 27.873415 87.45234 209.08166
#
#$`N(t)`
#estimate se lcl ucl
#1 172.6597 12.84355 151.7126 202.7258
#2 301.9485 20.11213 268.5098 348.1893
#3 526.0961 35.72379 466.8913 608.4782
#4 482.6952 31.12914 430.6676 553.9137
#5 187.3401 16.99719 160.7135 228.6425
#6 262.0393 19.37969 230.3986 307.3613
#7 341.5003 20.10434 307.4281 386.8953
#8 538.1462 38.90093 474.3711 628.7710
#
# For better labels and interpretation see MARK output
#Total Abundance Estimates
#Grp. N*-hat Standard Error Lower Upper
#---- -------------- -------------- -------------- --------------
#1 576.55192 10.995146 557.14154 600.43028
#2 623.44807 11.889480 602.45888 649.26868
#
#Abundance Estimates
#Grp. Str. Occ. Abundance Standard Error Lower Upper
#---- ---- ---- -------------- -------------- -------------- --------------
#1 E 1 86.482346 26.535427 59.748702 179.73562
#1 E 2 119.91930 35.212594 87.087938 250.88028
#1 E 3 181.23323 44.275677 158.25938 412.81875
#1 E 4 202.82678 50.807187 138.39582 351.52314
#1 F 1 57.654903 18.347425 40.002836 124.36399
#1 F 2 92.918950 27.736591 69.100442 202.16049
#1 F 3 142.31136 34.297342 108.00098 263.47936
#1 F 4 168.20397 41.786899 132.40312 333.04237
#1 G 1 28.828208 10.200716 22.069972 76.007829
#1 G 2 58.135284 18.400953 45.083379 139.96467
#1 G 3 93.300695 22.071048 72.553406 174.90726
#1 G 4 119.44262 29.487455 89.827598 223.31249
#2 E 1 93.516733 28.693792 67.335545 201.88406
#2 E 2 129.67341 38.076751 90.393237 261.12212
#2 E 3 195.97456 47.877016 131.43369 328.98380
#2 E 4 219.32450 54.939793 175.00065 444.87035
#2 F 1 62.344496 19.839786 44.179691 137.09149
#2 F 2 100.47688 29.992658 68.462381 201.34980
#2 F 3 153.88682 37.087053 110.11103 269.06238
#2 F 4 181.88551 45.185803 126.26801 317.46649
#2 G 1 31.173066 11.030432 19.840768 69.371247
#2 G 2 62.863950 19.897668 40.988101 128.26377
#2 G 3 100.88968 23.866285 71.501133 172.47586
#2 G 4 129.15797 31.885935 92.337089 229.95112
#
#Summed Abundance Estimates
#Grp. Occ. Abundance Standard Error Lower Upper
#---- ---- -------------- -------------- -------------- --------------
#1 1 172.96546 17.572916 146.14398 216.69465
#1 2 270.97353 17.865494 242.20692 313.29762
#1 3 416.84528 21.541486 382.63361 468.53243
#1 4 490.47337 29.737455 440.11912 557.67492
#2 1 187.03430 19.002280 158.06800 234.37501
#2 2 293.01425 19.318655 260.11406 336.43459
#2 3 450.75106 23.293649 409.16876 500.79814
#2 4 530.36798 32.156270 476.29049 603.50885
#
#Abundance Estimates without TSA
#Grp. Str. Occ. Abundance Standard Error Lower Upper
#---- ---- ---- -------------- -------------- -------------- --------------
#1 E 1 81.674097 23.937921 58.043545 167.08272
#1 E 2 126.83858 37.175211 90.140704 259.47682
#1 E 3 259.54924 63.478782 190.12917 469.06140
#1 E 4 148.55131 36.331666 108.81919 268.46423
#1 F 1 56.085487 16.675093 39.765869 115.96055
#1 F 2 104.09903 30.950270 73.808547 215.23180
#1 F 3 157.41243 37.901060 115.62023 281.69787
#1 F 4 204.11663 49.146288 149.92471 365.27751
#1 G 1 34.900079 10.950259 24.532949 75.180890
#1 G 2 71.010918 22.280408 49.917000 152.96997
#1 G 3 109.13438 25.654572 80.490758 192.43787
#1 G 4 130.02727 30.565932 95.900063 229.27854
#2 E 1 98.324993 28.818144 69.876881 201.14587
#2 E 2 122.75425 35.978133 87.238086 251.12141
#2 E 3 117.65857 28.776129 86.189140 212.63438
#2 E 4 273.60001 66.915223 200.42187 494.45417
#2 F 1 63.913917 19.002608 45.316401 132.14636
#2 F 2 89.296755 26.549323 63.313404 184.62710
#2 F 3 138.78578 33.416218 101.93886 248.36450
#2 F 4 145.97290 35.146702 107.21784 261.22623
#2 G 1 25.101172 7.8757515 17.644825 54.072326
#2 G 2 49.988280 15.684338 35.139173 107.68352
#2 G 3 85.055913 19.994369 62.731974 149.98004
#2 G 4 118.57332 27.873415 87.452338 209.08166
#
#Summed Abundance Estimates without TSA
#Grp. Occ. Abundance Standard Error Lower Upper
#---- ---- -------------- -------------- -------------- --------------
#1 1 172.65966 12.843549 151.71260 202.72578
#1 2 301.94852 20.112126 268.50983 348.18933
#1 3 526.09605 35.723790 466.89129 608.47820
#1 4 482.69521 31.129138 430.66757 553.91371
#2 1 187.34008 16.997192 160.71347 228.64245
#2 2 262.03929 19.379689 230.39863 307.36128
#2 3 341.50026 20.104344 307.42812 386.89528
#2 4 538.14624 38.900929 474.37108 628.77101
#
#Expected Ingress, Egress, and Residence Times 1&2
#Grp. Estimate Standard Error Lower Upper
#---- -------------- -------------- -------------- --------------
#1 2.4000144 0.0650048 2.2726051 2.5274237
#1 3.7437021 0.0583736 3.6292898 3.8581145
#1 2.3436877 0.0811437 2.1846461 2.5027293
#1 3.4389990 0.1604949 3.1244290 3.7535689
#2 2.4000144 0.0650048 2.2726051 2.5274237
#2 3.7437021 0.0583736 3.6292898 3.8581145
#2 2.3436877 0.0811437 2.1846461 2.5027293
#2 3.4389990 0.1604949 3.1244290 3.7535689
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