Organize data for the multi-state occupancy model fit by occuMS

Organizes multi-state occupancy data (currently single-season only) along with covariates. This S4 class is required by the data argument of occuMS

unmarkedFrameOccuMS(y, siteCovs=NULL, obsCovs=NULL, 
                           numPrimary=1, yearlySiteCovs=NULL)

Arguments

y	An MxR matrix of multi-state occupancy data for a species, where M is the number of sites and R is the maximum number of observations per site (across all primary and secondary periods, if you have multi-season data). Values in `y` should be integers ranging from 0 (non-detection) to the number of total states - 1. For example, if you have 3 occupancy states, `y` should contain only values 0, 1, or 2.
siteCovs	A `data.frame` of covariates that vary at the site level. This should have M rows and one column per covariate
obsCovs	Either a named list of `data.frame`s of covariates that vary within sites, or a `data.frame` with MxR rows in the ordered by site-observation (if single-season) or site-primary period-observation (if multi-season).
numPrimary	Number of primary time periods (e.g. seasons) for the dynamic or multi-season version of the model. There should be an equal number of secondary periods in each primary period.
yearlySiteCovs	A data frame with one column per covariate that varies among sites and primary periods (e.g. years). It should have MxT rows where M is the number of sites and T the number of primary periods, ordered by site-primary period. These covariates only used for dynamic (multi-season) models.

Details

unmarkedFrameOccuMS is the S4 class that holds data to be passed to the occuMS model-fitting function.

Value

an object of class unmarkedFrameOccuMS

Author

Ken Kellner contact@kenkellner.com

Examples


# Fake data
#Parameters
N <- 100; J <- 3; S <- 3
psi <- c(0.5,0.3,0.2)
p11 <- 0.4; p12 <- 0.25; p22 <- 0.3

#Simulate state
z <- sample(0:2, N, replace=TRUE, prob=psi)

#Simulate detection
y <- matrix(0,nrow=N,ncol=J)
for (n in 1:N){
  probs <- switch(z[n]+1,
                  c(0,0,0),
                  c(1-p11,p11,0),
                  c(1-p12-p22,p12,p22))
  
  if(z[n]>0){
    y[n,] <- sample(0:2, J, replace=TRUE, probs)
  }
}

#Covariates
site_covs <- as.data.frame(matrix(rnorm(N*2),ncol=2)) # nrow = # of sites
obs_covs <- as.data.frame(matrix(rnorm(N*J*2),ncol=2)) # nrow = N*J

#Build unmarked frame
umf <- unmarkedFrameOccuMS(y=y,siteCovs=site_covs,obsCovs=obs_covs)

umf                     # look at data
#> Data frame representation of unmarkedFrame object.
#>     y.1 y.2 y.3          V1           V2        V1.1          V1.2         V1.3
#> 1     0   0   0 -0.64184095  0.548210901 -0.53520886 -0.8356606410 -0.758469900
#> 2     0   0   0 -0.97066324 -1.522230182 -1.32412990  0.7884674711 -1.376718465
#> 3     0   0   0 -1.29234619  1.028677258 -1.07039524 -0.9867409314  0.536586184
#> 4     0   0   0 -0.86270402  0.789193704 -0.44520658 -0.0035014370 -0.623814095
#> 5     0   0   0 -2.16186578  0.340937594  0.46084693 -0.5775201198  1.687608566
#> 6     0   0   0  1.36807697 -1.494366683  0.02566052 -0.5903071617  1.382021803
#> 7     0   0   2 -0.49683984 -0.225543593  0.03190134 -1.0025066940 -0.955665075
#> 8     1   1   0  0.15637447 -0.307737024  0.49056568 -1.1794173724  1.556754426
#> 9     0   0   0  0.04243094  0.177667082 -0.90426101 -1.5027029029 -1.564837363
#> 10    0   0   0  0.29982884 -2.225663422 -0.99417265  0.5575281557 -1.932779539
#> 11    0   0   0  0.02178191  0.974740952 -0.48807934 -0.0706973592 -1.419587253
#> 12    0   0   0  0.75375599 -1.567913894  0.18954846 -2.6224338033 -1.676943934
#> 13    0   0   0  1.06726362 -0.290134835  1.54275628  1.6812303146  1.043101669
#> 14    0   0   0 -0.66939632 -1.085662469  2.54097110 -0.5617795129  1.744045996
#> 15    0   0   1 -1.49849337 -0.487192711 -1.14145958  0.9093334431 -1.693295398
#> 16    0   0   0  0.24366876 -0.882772781  0.53830842 -2.1471398594 -1.654298198
#> 17    0   2   1 -0.11960214  0.186851868 -1.17214068 -0.7391005026  0.192501146
#> 18    1   0   0 -0.80760289 -0.163639051 -0.18973765  1.3336850772  1.487797494
#> 19    0   0   0  0.09131803 -0.088305581  0.86057330 -0.4099882319  0.009603735
#> 20    2   0   2  0.92576093  0.635917459 -0.14426542  1.0010663974 -0.074340286
#> 21    0   0   0  0.05379044  0.996137610 -0.75238923  0.5517417828 -2.178216721
#> 22    0   0   0 -1.22456890  1.086729800  0.09251114 -0.6870995782 -0.350172527
#> 23    0   0   2  0.31769023  0.476887615  0.59993325  0.2324404273  0.624939015
#> 24    0   0   0 -1.14544798 -0.246815426  0.02779428 -0.8387826308 -1.017810580
#> 25    0   0   1  0.27393584 -0.879350872 -0.04780211  2.0222448696 -1.697631133
#> 26    0   0   1  0.20349117 -1.174765141  0.16838284 -0.5222329406 -0.731790040
#> 27    2   2   0  1.51236233  1.313530633  0.55645452 -0.2405626819  1.365587671
#> 28    0   2   0 -0.19020727  0.101634956 -0.77874395 -1.7431565556 -1.211622486
#> 29    0   0   0  0.73398201  1.917755976 -1.22046577  0.7313058206  0.117969235
#> 30    0   1   0  0.88543869  0.772055258  0.60565918  0.2552602509  0.939343165
#> 31    0   0   0 -0.20871070  0.608179372  1.18999415  0.9520827795 -0.428301362
#> 32    0   0   0 -1.46930033 -0.149808640 -0.87302008  0.5398339243  0.925032621
#> 33    0   1   0 -0.03533960  0.902953785 -0.96825863  0.8269954342 -0.694504455
#> 34    0   0   0  0.29990145  1.006861854 -0.25812995 -0.5095114078 -0.409117306
#> 35    2   1   0  0.39163712 -2.104458877 -2.65533184  0.2518631290 -0.931551244
#> 36    1   0   0  0.17402993 -0.055362929 -0.62678646 -1.1019363818 -0.124774439
#> 37    0   0   0 -0.21195779 -0.142742628 -0.28941648  0.9445854587  0.710177379
#> 38    0   2   0  1.09060298  0.448182395  0.49978879 -2.0657522201  0.313389619
#> 39    0   2   1 -0.03023812  1.937838542 -1.93840623  2.3770803428  0.095257563
#> 40    0   0   0 -0.09080731  1.930482788 -1.22825986 -0.0004527973  0.181623331
#> 41    0   0   0  0.45725551  0.637576384  1.05912965  1.1657936885 -3.825192770
#> 42    0   1   0  0.39260441  0.378542277  0.97341266 -1.8747091496  0.696300790
#> 43    1   1   1  1.75971039  2.644171544 -1.55170921 -0.2172027567  0.200334047
#> 44    0   0   1  1.29399569  0.725157301  2.24446004 -0.5981348356  2.376398852
#> 45    0   0   0  0.98258762 -1.309462096 -1.42285524 -0.4176629645 -0.671900133
#> 46    1   0   0  0.26220145 -0.868863220 -0.93930794  0.5003425311  0.495779431
#> 47    0   0   1 -0.63420164 -0.013568857 -2.29226575 -0.9625057645 -1.685839610
#> 48    0   2   0 -1.08804619  0.999472090  1.22092586  0.8589559642  0.495125069
#> 49    0   0   0 -0.93301612 -0.588890169  1.89631570  0.0458428395  0.323220836
#> 50    0   1   1 -0.86314585  0.543692463 -1.09725322  2.1192200553 -0.208625280
#> 51    0   0   0  0.50238220 -1.337667376 -0.58726938 -0.4842070623  2.229149501
#> 52    0   0   0 -0.56479505  0.293055307 -1.05489215  0.4654078806 -0.193418436
#> 53    0   0   0 -0.17649150 -0.270231836  0.96536418  0.8676121064 -1.075456399
#> 54    1   0   1  1.32140318  1.310052249 -2.39611235  1.8459489418  2.143414123
#> 55    0   0   0  0.27256808  1.693188759 -0.64959655  1.6275373724  0.626129077
#> 56    0   1   0  0.01105622  0.150314192 -0.23022334  0.7276509599 -0.003856744
#> 57    0   0   0 -0.78054627  0.314554325  0.68226024  0.2667503275  0.442835321
#> 58    1   0   1 -0.70110058 -0.051994432 -0.16009563  0.3027330830  1.431755443
#> 59    0   1   1 -0.75936487  0.956109028  0.76036431  0.4578848894  0.247813218
#> 60    0   0   0 -0.17025990  0.193907191 -0.01167935 -1.4804643446 -0.880207243
#> 61    0   0   0 -0.65628585 -0.548031010  0.99859327  0.2088545072 -0.508443215
#> 62    0   1   0  0.15754423  0.305963726  0.65339480 -1.0099323484  0.203324508
#> 63    0   0   0  1.09341476 -0.722769969 -0.83451137 -0.6252577722  0.511477082
#> 64    0   0   0  0.30104243 -0.950418512 -0.59934928 -0.0938764014  0.312626324
#> 65    0   0   0 -0.55848939  0.213063736 -1.12719048  2.3789054015  1.046135322
#> 66    0   0   0 -1.07537588  0.609497162  0.29781154 -1.7874894163  1.004488932
#> 67    0   0   0  0.20078737  0.442387967  1.24421188  0.5116851373  0.474771131
#> 68    0   0   0  0.08402028 -0.356939616 -0.51687077 -1.6597489739  0.224008679
#> 69    2   2   1 -0.92639870  1.751916726  1.80724876  0.1897257652 -1.162926270
#> 70    0   0   0  0.38670071 -0.848822909  3.01762144 -0.3857213667  0.562749831
#> 71    0   1   0  0.66041789 -0.036849050  0.88620092 -0.3564657983  0.249098500
#> 72    0   0   0 -0.27608803 -0.444763521 -0.36917207  0.0416310405  1.217376959
#> 73    0   1   0  0.31282214  0.478518115  0.95892354 -0.4360568297  1.439807918
#> 74    2   1   1 -0.68574952 -0.077862440  0.81655931 -1.7579772836 -0.048041491
#> 75    0   0   0  2.26820446 -0.003609624  1.49444163 -3.1455200356 -0.533122004
#> 76    1   1   1 -1.69216052  0.489831017  0.95207821  0.4692538208 -0.895449359
#> 77    0   0   0  0.58783751 -1.214862470  0.53504941 -0.7509258438 -1.047776900
#> 78    0   0   0  0.06640786 -0.197096846 -2.20051934  0.8508171172  0.241186508
#> 79    0   0   0 -1.40465612  1.708650899  0.26919676  1.6245366631 -1.177535778
#> 80    1   0   1 -0.34279754 -0.759601384 -0.54506222 -0.8097758037 -0.118515926
#> 81    0   0   1 -0.71325702  0.972369565  2.24367085  1.1053080544 -0.907419621
#> 82    0   0   0 -0.49621057 -1.031840383  0.61006329 -1.0834181706 -0.959203053
#> 83    0   1   0 -0.67162126  0.768586627  0.64339350  1.0595977905 -0.312166205
#> 84    0   0   0 -0.33154946  0.301285060 -1.88179488  0.5517466442  0.379658026
#> 85    0   1   1  0.17127612  0.424560581  0.74674973 -1.5516728818  1.160177176
#> 86    0   0   0 -0.97939232  0.006571057  0.27871367 -1.6594292656  0.004756581
#> 87    0   1   0  0.32099407  0.741297749  0.11815242  0.9854456858 -1.628949195
#> 88    0   0   0 -0.91187115  1.559007487  0.93992530  0.0127289781  0.315581938
#> 89    0   0   0  1.32086543 -0.417168752 -0.76462294 -0.3671731885 -0.251556827
#> 90    1   1   0 -0.14469855 -0.420322963 -0.68119202 -0.1705038590 -0.229977245
#> 91    0   0   0 -0.97100451  0.697185864 -0.57972851  0.1317782552  1.538578812
#> 92    0   1   0 -0.26407080 -0.210961609 -0.15863134 -1.7907943777 -0.466653036
#> 93    1   1   0  0.73831045  1.541085498  0.87534492 -1.3446055726 -0.936891947
#> 94    0   1   0  0.33237462  0.284831960 -1.78732832  0.9216300728 -0.320494146
#> 95    0   0   0 -0.34408614  0.321961376  1.39813717  1.4417777265  0.204873505
#> 96    0   0   0  1.70559275 -1.068526069 -0.25417885 -0.1732909467  0.518905826
#> 97    0   0   0  0.30669904  1.425715862  1.05153012 -0.0886198239  1.561617050
#> 98    0   0   0 -0.23380619  1.199570200  0.96689305 -0.0500577177 -1.572254628
#> 99    1   1   0 -0.57237583 -1.678036407 -0.32245560 -1.4407089241  0.625852956
#> 100   1   0   1  1.18807891  1.828806163  0.59798851  0.2818128897  0.306771392
#>            V2.1         V2.2        V2.3
#> 1    0.01872454 -0.361380290 -0.87784876
#> 2   -2.29833054  0.023484659  0.05277509
#> 3    3.85175797  1.669081736  0.63151187
#> 4   -0.81596448 -1.294877516  0.70004573
#> 5   -0.99496665  0.852350396  0.33354522
#> 6   -0.67883947 -0.413895834  0.18060951
#> 7   -0.53403857  0.055683399 -0.49847062
#> 8    1.47066088  0.225038972 -0.28253360
#> 9   -0.34441780 -0.926788852  1.48766407
#> 10  -0.29712930  0.195877108  0.38630524
#> 11  -0.55027985 -0.146678677  2.41717451
#> 12  -0.48172262 -0.418759641  1.27879340
#> 13  -1.34322581  0.077453309  1.01627541
#> 14   0.47144438  0.845016206 -0.63787277
#> 15   0.90078177  0.965314556 -2.06431505
#> 16   0.66839191 -0.346110954 -0.35951650
#> 17   0.09703120 -0.244525361  0.51228285
#> 18   1.62537932  0.349395823 -1.40294529
#> 19   0.90395120  0.181019728  2.58787592
#> 20  -0.94622107  0.097786086  0.52062112
#> 21   0.59254958 -0.499619063 -0.82302738
#> 22   1.27514364 -1.064299146  0.10758017
#> 23   0.53348710  0.710190859  2.14668958
#> 24  -1.90963308 -0.340402608 -2.06637154
#> 25  -1.00429449  0.564560691 -1.94080737
#> 26   0.78895333 -1.130472653  0.57338131
#> 27  -0.93493189 -0.410000659  0.15691106
#> 28  -0.57763324  0.536004940  0.56858611
#> 29  -0.23214033 -1.969235728  0.21876232
#> 30  -1.70185098  1.127069316  0.27846103
#> 31   0.83054149  0.525377968 -0.00591447
#> 32  -0.20404631 -0.522807749 -0.59610094
#> 33  -0.75476367  1.409275722 -0.63872599
#> 34   0.15994780  0.442129726 -2.24486993
#> 35  -0.82191117 -0.996499987  0.29172317
#> 36  -0.23718814 -0.246866937  0.31770395
#> 37  -0.40705793 -0.408328005  0.64708099
#> 38  -1.59314960  0.393719551 -0.90107686
#> 39  -1.13162369  0.005020132  1.13980577
#> 40   0.70534851 -1.008312237  1.20737148
#> 41  -0.10849829  0.545097257  1.37848568
#> 42  -0.16367690 -1.005130049  0.64358722
#> 43  -0.38280020 -1.984354597  0.39247562
#> 44   0.11903629 -0.740477010  0.75935675
#> 45  -1.42959983 -0.843607892  0.30994852
#> 46  -1.04208054 -0.188198590 -1.57826034
#> 47   1.19011275  1.780184056  1.05504463
#> 48   1.44375919 -0.591646556  1.41419218
#> 49   0.44684374 -1.208871860 -0.32359956
#> 50   1.64859021  0.370831067  0.08121916
#> 51   1.06781026 -1.314178005  0.97500703
#> 52  -0.86060781 -1.784806845  0.22950633
#> 53   1.26488854 -0.714100676 -1.50231027
#> 54   0.62684078 -0.878572637 -0.54793907
#> 55   0.61023176  0.680038329 -0.31756582
#> 56   1.25261483 -0.399390650  0.81276665
#> 57   0.08039585  1.217744742  0.95327491
#> 58   0.14405341 -0.283343985  1.04550271
#> 59  -0.20961705  0.597283229 -1.58156859
#> 60   0.39150405 -0.896101716  0.80919741
#> 61   1.69891935 -1.545991479  1.02255790
#> 62  -0.25003924 -1.296601763  0.13510179
#> 63   0.62185780 -1.013122753 -0.16318239
#> 64   0.96663711  0.347485262  0.16637783
#> 65  -1.24781057 -0.984813152  0.44122657
#> 66  -0.62228934 -2.301111730  0.53537822
#> 67   1.95390197  1.231877823 -0.40318214
#> 68  -1.23378764 -0.318970999  0.42872279
#> 69  -0.08936380  0.132809154  0.85955078
#> 70  -0.83654386  0.287022002  0.51664757
#> 71   1.64870228 -2.926628588 -0.40102396
#> 72  -2.73351268 -0.455470500  0.93584973
#> 73  -0.34082486 -0.301172526  0.74484201
#> 74  -0.70321279  1.216742433  1.31937278
#> 75   0.13882873  0.436317475  2.01285428
#> 76   0.62610236 -0.108914265  0.50250170
#> 77   0.49527911 -1.635742414  0.66399159
#> 78   1.14779735  1.187860233  0.12209850
#> 79   0.20057410 -0.078304150  0.14152753
#> 80   0.63271939 -1.357479143  1.94575546
#> 81   0.36379338  0.285271573  0.77725703
#> 82  -1.60712721 -0.186522495  0.65791120
#> 83  -0.09324682 -0.116875447 -0.64878105
#> 84   0.76789613 -0.499934447 -0.05042814
#> 85   0.04443762 -2.184613772  0.20089494
#> 86  -0.16732086 -0.230566566 -1.17426116
#> 87  -0.59608537 -2.089287265 -0.13282695
#> 88  -1.25415197 -0.897870703  1.54493127
#> 89   0.85052225 -1.227272191 -0.17049409
#> 90   0.86430544  0.097125288 -1.43133063
#> 91   0.14545999 -0.633855638 -1.02372154
#> 92  -1.56706913  0.631062904  0.53948272
#> 93   0.21151700 -0.915273429  0.26329392
#> 94   0.34802268  0.064803477  0.65275912
#> 95  -1.09692603 -1.183711173  0.38742178
#> 96  -0.13273848  0.996608053 -2.06514236
#> 97   0.82878384 -0.685227838 -0.54703787
#> 98   0.52179939 -2.207718952 -0.11589740
#> 99  -0.65199788 -0.209630127  0.05690934
#> 100  0.04170706 -0.538136645 -1.61479492
summary(umf)            # summarize      
#> unmarkedFrame Object
#> 
#> 100 sites
#> Maximum number of observations per site: 3 
#> Mean number of observations per site: 3 
#> Number of primary survey periods: 1 
#> Number of secondary survey periods: 3 
#> Sites with at least one detection: 45 
#> 
#> Tabulation of y observations:
#>   0   1   2 
#> 231  54  15 
#> 
#> Site-level covariates:
#>        V1                  V2         
#>  Min.   :-2.161866   Min.   :-2.2257  
#>  1st Qu.:-0.669953   1st Qu.:-0.4180  
#>  Median :-0.009591   Median : 0.2035  
#>  Mean   :-0.046846   Mean   : 0.1657  
#>  3rd Qu.: 0.345956   3rd Qu.: 0.7695  
#>  Max.   : 2.268204   Max.   : 2.6442  
#> 
#> Observation-level covariates:
#>        V1                  V2          
#>  Min.   :-3.825193   Min.   :-2.92663  
#>  1st Qu.:-0.815960   1st Qu.:-0.65871  
#>  Median :-0.001977   Median : 0.04861  
#>  Mean   :-0.047920   Mean   :-0.04404  
#>  3rd Qu.: 0.685770   3rd Qu.: 0.62629  
#>  Max.   : 3.017621   Max.   : 3.85176  
plot(umf)               # visualize
umf@numStates           # check number of occupancy states detected
#> [1] 3