Class "unmarkedFit"
unmarkedFit-class.RdContains fitted model information which can be manipulated or extracted using the methods described below.
Slots
fitType:Object of class
"character"call:Object of class
"call"formula:Object of class
"formula"data:Object of class
"unmarkedFrame"sitesRemoved:Object of class
"numeric"estimates:Object of class
"unmarkedEstimateList"AIC:Object of class
"numeric"opt:Object of class
"list"containing results fromoptimnegLogLike:Object of class
"numeric"nllFun:Object of class
"function"knownOcc:unmarkedFitOccu only: sites known to be occupied
K:unmarkedFitPCount only: upper bound used in integration
mixture:unmarkedFitPCount only: Mixing distribution
keyfun:unmarkedFitDS only: detection function used by distsamp
unitsOut:unmarkedFitDS only: density units
Methods
- [
signature(x = "unmarkedFit", i = "ANY", j = "ANY", drop = "ANY"): extract one of names(obj), eg 'state' or 'det'- backTransform
signature(obj = "unmarkedFit"): back-transform parameters to original scale when no covariate effects are modeled- coef
signature(object = "unmarkedFit"): returns parameter estimates. type can be one of names(obj), eg 'state' or 'det'. If altNames=TRUE estimate names are more specific.- confint
signature(object = "unmarkedFit"): Returns confidence intervals. Must specify type and method (either "normal" or "profile")- fitted
signature(object = "unmarkedFit"): returns expected values of Y- getData
signature(object = "unmarkedFit"): extracts data- getP
signature(object = "unmarkedFit"): calculates and extracts expected detection probabilities- getFP
signature(object = "unmarkedFit"): calculates and extracts expected false positive detection probabilities- getB
signature(object = "unmarkedFit"): calculates and extracts expected probabilities a true positive detection was classified as certain- hessian
signature(object = "unmarkedFit"): Returns hessian matrix- linearComb
signature(obj = "unmarkedFit", coefficients = "matrixOrVector"): Returns estimate and SE on original scale when covariates are present- mle
signature(object = "unmarkedFit"): Same as coef(fit)?- names
signature(x = "unmarkedFit"): Names of parameter levels- nllFun
signature(object = "unmarkedFit"): returns negative log-likelihood used to estimate parameters- parboot
signature(object = "unmarkedFit"): Parametric bootstrapping method to assess goodness-of-fit- plot
signature(x = "unmarkedFit", y = "missing"): Plots expected vs. observed values- predict
signature(object = "unmarkedFit"): Returns predictions and standard errors for original data or for covariates in a new data.frame- profile
signature(fitted = "unmarkedFit"): used by confint method='profile'- residuals
signature(object = "unmarkedFit"): returns residuals- sampleSize
signature(object = "unmarkedFit"): returns number of sites in sample- SE
signature(obj = "unmarkedFit"): returns standard errors- show
signature(object = "unmarkedFit"): concise results- summary
signature(object = "unmarkedFit"): results with more details- update
signature(object = "unmarkedFit"): refit model with changes to one or more arguments- vcov
signature(object = "unmarkedFit"): returns variance-covariance matrix- smoothed
signature(object="unmarkedFitColExt"): Returns the smoothed trajectory from a colonization-extinction model fit. Takes additional logical argument mean which specifies whether or not to return the average over sites.- projected
signature(object="unmarkedFitColExt"): Returns the projected trajectory from a colonization-extinction model fit. Takes additional logical argument mean which specifies whether or not to return the average over sites.- logLik
signature(object="unmarkedFit"): Returns the log-likelihood.- LRT
signature(m1="unmarkedFit", m2="unmarkedFit"): Returns the chi-squared statistic, degrees-of-freedom, and p-value from a Likelihood Ratio Test.
Note
This is a superclass with child classes for each fit type
Examples
showClass("unmarkedFit")#> Class "unmarkedFit" [package "unmarked"] #> #> Slots: #> #> Name: fitType call formula #> Class: character call formula #> #> Name: data sitesRemoved estimates #> Class: unmarkedFrame numeric unmarkedEstimateList #> #> Name: AIC opt negLogLike #> Class: numeric list numeric #> #> Name: nllFun bootstrapSamples covMatBS #> Class: function optionalList optionalMatrix #> #> Name: TMB #> Class: optionalList #> #> Known Subclasses: #> Class "unmarkedFitDS", directly #> Class "unmarkedFitPCount", directly #> Class "unmarkedFitDailMadsen", directly #> Class "unmarkedFitOccu", directly #> Class "unmarkedFitOccuPEN", directly #> Class "unmarkedFitOccuPEN_CV", directly #> Class "unmarkedFitOccuFP", directly #> Class "unmarkedFitOccuMulti", directly #> Class "unmarkedFitOccuMS", directly #> Class "unmarkedFitOccuTTD", directly #> Class "unmarkedFitNmixTTD", directly #> Class "unmarkedFitMPois", directly #> Class "unmarkedFitOccuRN", directly #> Class "unmarkedFitMNmix", directly #> Class "unmarkedFitColExt", directly #> Class "unmarkedFitGMM", directly #> Class "unmarkedFitPCO", by class "unmarkedFitDailMadsen", distance 2 #> Class "unmarkedFitMMO", by class "unmarkedFitDailMadsen", distance 2 #> Class "unmarkedFitDSO", by class "unmarkedFitDailMadsen", distance 2 #> Class "unmarkedFitGDS", by class "unmarkedFitGMM", distance 2 #> Class "unmarkedFitGPC", by class "unmarkedFitGMM", distance 2 #> Class "unmarkedFitGDR", by class "unmarkedFitGDS", distance 3# Format removal data for multinomPois data(ovendata) ovenFrame <- unmarkedFrameMPois(y = ovendata.list$data, siteCovs = as.data.frame(scale(ovendata.list$covariates[,-1])), type = "removal") # Fit a couple of models (fm1 <- multinomPois(~ 1 ~ ufc + trba, ovenFrame))#> #> Call: #> multinomPois(formula = ~1 ~ ufc + trba, data = ovenFrame) #> #> Abundance: #> Estimate SE z P(>|z|) #> (Intercept) 0.102 0.119 0.864 0.388 #> ufc 0.100 0.126 0.794 0.427 #> trba -0.171 0.135 -1.262 0.207 #> #> Detection: #> Estimate SE z P(>|z|) #> 0.288 0.233 1.24 0.217 #> #> AIC: 326.1387#> #> Call: #> multinomPois(formula = ~1 ~ ufc + trba, data = ovenFrame) #> #> Abundance (log-scale): #> Estimate SE z P(>|z|) #> (Intercept) 0.102 0.119 0.864 0.388 #> ufc 0.100 0.126 0.794 0.427 #> trba -0.171 0.135 -1.262 0.207 #> #> Detection (logit-scale): #> Estimate SE z P(>|z|) #> 0.288 0.233 1.24 0.217 #> #> AIC: 326.1387 #> Number of sites: 70 #> optim convergence code: 0 #> optim iterations: 30 #> Bootstrap iterations: 0 #>#> [1] "state" "det"fm1['state']#> Abundance: #> Estimate SE z P(>|z|) #> (Intercept) 0.102 0.119 0.864 0.388 #> ufc 0.100 0.126 0.794 0.427 #> trba -0.171 0.135 -1.262 0.207fm1['det']#> Detection: #> Estimate SE z P(>|z|) #> 0.288 0.233 1.24 0.217#> lambda(Int) lambda(ufc) lambda(trba) #> 0.1023681 0.1002939 -0.1708609#> lambda(Int) lambda(ufc) lambda(trba) #> lambda(Int) 0.014052472 -0.001228179 0.002364962 #> lambda(ufc) -0.001228179 0.015941971 0.008057614 #> lambda(trba) 0.002364962 0.008057614 0.018318119#> Profiling parameter 1 of 3 ... done. #> Profiling parameter 2 of 3 ... done. #> Profiling parameter 3 of 3 ... done.#> 0.025 0.975 #> lambda(Int) -0.1390786 0.32676614 #> lambda(ufc) -0.1477724 0.34811770 #> lambda(trba) -0.4368444 0.09469605#> [,1] [,2] [,3] [,4] #> [1,] 0.8960021 0.3840242 0.16459179 0.07054362 #> [2,] 0.7870851 0.3373426 0.14458420 0.06196842 #> [3,] 0.8518174 0.3650868 0.15647527 0.06706490 #> [4,] 0.9330928 0.3999212 0.17140521 0.07346383 #> [5,] 0.6193105 0.2654349 0.11376473 0.04875927 #> [6,] 0.6709329 0.2875601 0.12324754 0.05282358 #> [7,] 0.4425821 0.1896896 0.08130047 0.03484517 #> [8,] 0.6406227 0.2745692 0.11767968 0.05043721 #> [9,] 0.5722107 0.2452480 0.10511269 0.04505103 #> [10,] 0.7758598 0.3325315 0.14252217 0.06108464 #> [11,] 0.6696637 0.2870162 0.12301439 0.05272365 #> [12,] 0.7332182 0.3142555 0.13468909 0.05772740 #> [13,] 0.5964939 0.2556558 0.10957341 0.04696288 #> [14,] 0.5788819 0.2481073 0.10633816 0.04557626 #> [15,] 0.4480118 0.1920167 0.08229787 0.03527266 #> [16,] 0.5547235 0.2377531 0.10190036 0.04367423 #> [17,] 0.5216096 0.2235605 0.09581748 0.04106713 #> [18,] 1.0757606 0.4610683 0.19761268 0.08469629 #> [19,] 0.5526803 0.2368774 0.10152504 0.04351337 #> [20,] 0.4572096 0.1959589 0.08398748 0.03599682 #> [21,] 0.4353994 0.1866111 0.07998104 0.03427967 #> [22,] 0.4794734 0.2055011 0.08807723 0.03774968 #> [23,] 0.4893682 0.2097419 0.08989486 0.03852871 #> [24,] 0.5725686 0.2454015 0.10517844 0.04507921 #> [25,] 1.0031664 0.4299546 0.18427743 0.07898084 #> [26,] 0.5177313 0.2218983 0.09510505 0.04076178 #> [27,] 0.7363423 0.3155944 0.13526297 0.05797336 #> [28,] 0.7049276 0.3021302 0.12949221 0.05550003 #> [29,] 0.5390061 0.2310166 0.09901314 0.04243678 #> [30,] 0.5812395 0.2491178 0.10677125 0.04576189 #> [31,] 0.6342956 0.2718575 0.11651743 0.04993907 #> [32,] 0.4892256 0.2096808 0.08986867 0.03851748 #> [33,] 0.4624241 0.1981938 0.08494535 0.03640736 #> [34,] 0.4049857 0.1735759 0.07439417 0.03188515 #> [35,] 0.4680972 0.2006252 0.08598747 0.03685401 #> [36,] 0.7909652 0.3390057 0.14529696 0.06227391 #> [37,] 0.8078930 0.3462609 0.14840652 0.06360666 #> [38,] 0.5747929 0.2463548 0.10558703 0.04525433 #> [39,] 0.5099109 0.2185465 0.09366847 0.04014607 #> [40,] 0.5960968 0.2554856 0.10950047 0.04693162 #> [41,] 0.7888067 0.3380805 0.14490045 0.06210397 #> [42,] 0.8171921 0.3502464 0.15011473 0.06433879 #> [43,] 0.7124434 0.3053514 0.13087284 0.05609176 #> [44,] 0.8708712 0.3732532 0.15997535 0.06856503 #> [45,] 0.8224575 0.3525032 0.15108197 0.06475335 #> [46,] 0.7623319 0.3267335 0.14003715 0.06001957 #> [47,] 0.6738618 0.2888155 0.12378557 0.05305418 #> [48,] 0.6154523 0.2637813 0.11305598 0.04845550 #> [49,] 0.8719385 0.3737106 0.16017141 0.06864906 #> [50,] 0.8787262 0.3766198 0.16141829 0.06918347 #> [51,] 0.7032088 0.3013935 0.12917648 0.05536471 #> [52,] 0.6650580 0.2850422 0.12216834 0.05236104 #> [53,] 0.8037815 0.3444987 0.14765127 0.06328296 #> [54,] 0.7548815 0.3235403 0.13866854 0.05943298 #> [55,] 0.7062050 0.3026777 0.12972687 0.05560061 #> [56,] 0.5395671 0.2312571 0.09911620 0.04248095 #> [57,] 0.6750032 0.2893046 0.12399523 0.05314404 #> [58,] 0.7114952 0.3049450 0.13069865 0.05601711 #> [59,] 0.5724614 0.2453555 0.10515875 0.04507077 #> [60,] 0.6729650 0.2884311 0.12362082 0.05298357 #> [61,] 0.7051330 0.3022182 0.12952996 0.05551621 #> [62,] 0.8043147 0.3447272 0.14774921 0.06332494 #> [63,] 0.5752835 0.2465650 0.10567715 0.04529296 #> [64,] 0.5306117 0.2274188 0.09747113 0.04177588 #> [65,] 0.5472712 0.2345590 0.10053140 0.04308750 #> [66,] 0.4957018 0.2124565 0.09105832 0.03902736 #> [67,] 0.6007372 0.2574744 0.11035289 0.04729697 #> [68,] 0.4665963 0.1999820 0.08571178 0.03673585 #> [69,] 0.3338288 0.1430782 0.06132294 0.02628286 #> [70,] 0.6806401 0.2917206 0.12503070 0.05358784# Original data getData(fm1)#> Data frame representation of unmarkedFrame object. #> y.1 y.2 y.3 y.4 ufc trba #> 1 0 0 0 0 0.84177975 -1.53958767 #> 2 1 0 0 0 0.03022367 -1.25741503 #> 3 0 0 0 0 2.10015927 -0.50495466 #> 4 0 0 0 0 1.08597395 -1.63364522 #> 5 0 0 0 0 1.00492226 0.71779345 #> 6 0 0 0 0 0.84177975 0.15344817 #> 7 0 0 0 0 -0.90290994 1.56431137 #> 8 0 0 0 0 -0.74080655 -0.50495466 #> 9 2 0 0 0 -0.58493791 0.24750572 #> 10 1 0 0 0 1.32912904 -0.41089711 #> 11 0 0 0 0 -0.61922901 -0.69306975 #> 12 0 0 0 0 1.08597395 -0.22278202 #> 13 2 0 1 0 -0.01030218 0.34156326 #> 14 1 0 0 0 -1.43078508 -0.31683956 #> 15 1 0 0 0 -0.78133240 1.56431137 #> 16 0 0 0 0 -0.25345726 0.62373590 #> 17 0 1 0 1 -1.18763000 0.43562081 #> 18 0 1 0 0 2.34435347 -1.72770276 #> 19 0 0 0 0 0.03022367 0.81185099 #> 20 0 0 0 0 -0.57870316 1.56431137 #> 21 1 0 0 0 -1.06605246 1.56431137 #> 22 1 1 0 0 -1.06605246 0.99996609 #> 23 1 0 0 0 -0.86238410 0.99996609 #> 24 1 0 0 0 -0.57870316 0.24750572 #> 25 0 1 0 0 1.16702565 -2.00987540 #> 26 1 0 0 0 -0.78133240 0.71779345 #> 27 1 1 0 1 1.28860319 -0.12872447 #> 28 0 0 0 0 -1.22919497 -1.35147258 #> 29 0 0 0 0 -0.70028071 0.52967835 #> 30 0 0 0 0 -1.39025924 -0.31683956 #> 31 0 0 0 0 0.92283144 0.52967835 #> 32 0 0 0 0 -1.02552661 0.90590854 #> 33 0 0 0 0 -1.10657831 1.18808118 #> 34 0 0 0 0 -1.14710415 1.94054155 #> 35 0 0 0 0 -0.98500076 1.18808118 #> 36 2 0 1 0 0.72020220 -0.88118484 #> 37 1 0 0 0 1.57228412 -0.50495466 #> 38 0 1 0 0 -0.70028071 0.15344817 #> 39 1 1 0 0 -0.74080655 0.83066250 #> 40 1 0 0 0 -0.49765147 0.05939062 #> 41 2 1 0 0 1.65437494 -0.31683956 #> 42 0 1 0 0 1.04544811 -0.88118484 #> 43 1 2 0 0 0.63915051 -0.31683956 #> 44 0 0 0 1 1.16702565 -1.18216899 #> 45 1 0 1 0 -0.17240556 -1.63364522 #> 46 3 0 0 0 0.19232706 -0.97524239 #> 47 1 0 0 0 1.04544811 0.24750572 #> 48 2 0 1 0 -0.65975486 -0.22278202 #> 49 0 0 0 2 1.53175828 -0.97524239 #> 50 1 1 0 0 1.28860319 -1.16335748 #> 51 3 0 0 0 -0.13187972 -0.69306975 #> 52 0 0 0 0 0.27337876 -0.12872447 #> 53 2 0 0 0 1.32912904 -0.61782371 #> 54 1 0 0 0 1.85700418 0.05939062 #> 55 1 0 0 0 0.07074952 -0.59901220 #> 56 1 0 0 0 0.11127537 0.99996609 #> 57 1 0 0 0 -0.70028071 -0.78712730 #> 58 1 1 0 0 -0.33554808 -0.88118484 #> 59 1 1 0 0 -0.74080655 0.15344817 #> 60 2 0 0 1 0.07074952 -0.31683956 #> 61 0 1 0 0 -1.06605246 -1.25741503 #> 62 2 0 0 0 1.20755150 -0.69306975 #> 63 1 0 1 1 -0.05082802 0.52967835 #> 64 2 1 0 0 -0.37607393 0.81185099 #> 65 0 0 0 0 -1.34973339 0.05939062 #> 66 1 0 0 0 -0.25345726 1.28213873 #> 67 0 0 0 0 -0.74080655 -0.12872447 #> 68 0 0 0 0 -0.37607393 1.56431137 #> 69 0 0 0 0 -1.47131093 2.88111702 #> 70 0 0 0 0 -0.45712562 -0.69306975#> [,1] [,2] [,3] [,4] #> [1,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [2,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [3,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [4,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [5,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [6,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [7,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [8,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [9,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [10,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [11,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [12,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [13,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [14,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [15,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [16,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [17,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [18,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [19,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [20,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [21,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [22,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [23,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [24,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [25,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [26,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [27,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [28,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [29,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [30,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [31,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [32,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [33,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [34,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [35,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [36,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [37,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [38,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [39,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [40,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [41,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [42,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [43,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [44,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [45,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [46,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [47,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [48,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [49,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [50,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [51,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [52,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [53,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [54,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [55,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [56,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [57,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [58,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [59,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [60,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [61,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [62,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [63,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [64,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [65,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [66,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [67,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [68,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [69,] 0.5714025 0.2449017 0.1049642 0.0449874 #> [70,] 0.5714025 0.2449017 0.1049642 0.0449874# log-likelihood logLik(fm1)#> [1] -159.0694# Back-transform detection probability to original scale # backTransform only works on models with no covariates or # in conjunction with linearComb (next example) backTransform(fm1, type ='det')#> Backtransformed linear combination(s) of Detection estimate(s) #> #> Estimate SE LinComb (Intercept) #> 0.571 0.057 0.288 1 #> #> Transformation: logistic# Predicted abundance at specified covariate values (lc <- linearComb(fm1, c(Int = 1, ufc = 0, trba = 0), type='state'))#> Linear combination(s) of Abundance estimate(s) #> #> Estimate SE (Intercept) ufc trba #> 0.102 0.119 1 0 0 #>#> Backtransformed linear combination(s) of Abundance estimate(s) #> #> Estimate SE LinComb (Intercept) ufc trba #> 1.11 0.131 0.102 1 0 0 #> #> Transformation: exp#> #> Call: parboot(object = fm1) #> #> Parametric Bootstrap Statistics: #> t0 mean(t0 - t_B) StdDev(t0 - t_B) Pr(t_B > t0) #> SSE 72.2 -6.09 11.9 0.636 #> #> t_B quantiles: #> 0% 2.5% 25% 50% 75% 97.5% 100% #> SSE 56 57 72 83 85 92 93 #> #> t0 = Original statistic computed from data #> t_B = Vector of bootstrap samples #># Predict abundance at specified covariate values. newdat <- data.frame(ufc = 0, trba = seq(-1, 1, length=10)) predict(fm1, type='state', newdata=newdat)#> Predicted SE lower upper #> 1 1.3142011 0.2184923 0.9487362 1.820448 #> 2 1.2652375 0.1853259 0.9494939 1.685978 #> 3 1.2180982 0.1591867 0.9428516 1.573698 #> 4 1.1727152 0.1412680 0.9260934 1.485013 #> 5 1.1290230 0.1324045 0.8971796 1.420778 #> 6 1.0869586 0.1322528 0.8563382 1.379688 #> 7 1.0464615 0.1390828 0.8064770 1.357858 #> 8 1.0074732 0.1505623 0.7516666 1.350336 #> 9 0.9699374 0.1646399 0.6954379 1.352786 #> 10 0.9338002 0.1798639 0.6401766 1.362097# Number of sites in the sample sampleSize(fm1)#> [1] 70#> #> Call: #> multinomPois(formula = ~1 ~ 1, data = ovenFrame) #> #> Abundance: #> Estimate SE z P(>|z|) #> 0.13 0.115 1.12 0.262 #> #> Detection: #> Estimate SE z P(>|z|) #> 0.288 0.233 1.24 0.217 #> #> AIC: 326.2803# Likelihood ratio test LRT(fm1, fmNull)#> Chisq DF Pr(>Chisq) #> 1 4.141604 2 0.1260846
