unmarkedModSel-class.RdThe unmarkedModSel class and associated methods
data.frame with formula, estimates, standard errors and model selection information. Converge is optim convergence code. CondNum is model condition number. n is the number of sites. delta is delta AIC. cumltvWt is cumulative AIC weight. Rsq is Nagelkerke's (1991) R-squared index, which is only returned when the nullmod argument is specified.
matrix referencing column names of estimates (row 1) and standard errors (row 2).
Print the AIC model selection table
Data frame of coefficients from all models in model selection table
Data frame of coefficient SEs from all models in model selection table
data(linetran)
(dbreaksLine <- c(0, 5, 10, 15, 20))
#> [1] 0 5 10 15 20
lengths <- linetran$Length * 1000
ltUMF <- with(linetran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
siteCovs = data.frame(Length, area, habitat), dist.breaks = dbreaksLine,
tlength = lengths, survey = "line", unitsIn = "m")
})
fm1 <- distsamp(~ 1 ~1, ltUMF)
fm2 <- distsamp(~ area ~1, ltUMF)
fm3 <- distsamp( ~ 1 ~area, ltUMF)
fl <- fitList(Null=fm1, A.=fm2, .A=fm3)
fl
#> An object of class "unmarkedFitList"
#> Slot "fits":
#> $Null
#>
#> Call:
#> distsamp(formula = ~1 ~ 1, data = ltUMF)
#>
#> Density (log-scale):
#> Estimate SE z P(>|z|)
#> -0.171 0.134 -1.28 0.201
#>
#> Detection (log-scale):
#> Estimate SE z P(>|z|)
#> 2.39 0.127 18.7 2.46e-78
#>
#> AIC: 164.7524
#> Number of sites: 12
#>
#> Survey design: line-transect
#> Detection function: halfnorm
#> UnitsIn: m
#> UnitsOut: ha
#>
#>
#> $A.
#>
#> Call:
#> distsamp(formula = ~area ~ 1, data = ltUMF)
#>
#> Density (log-scale):
#> Estimate SE z P(>|z|)
#> -0.168 0.134 -1.25 0.21
#>
#> Detection (log-scale):
#> Estimate SE z P(>|z|)
#> (Intercept) 3.00 0.5402 5.56 2.72e-08
#> area -0.12 0.0955 -1.26 2.07e-01
#>
#> AIC: 165.1845
#> Number of sites: 12
#>
#> Survey design: line-transect
#> Detection function: halfnorm
#> UnitsIn: m
#> UnitsOut: ha
#>
#>
#> $.A
#>
#> Call:
#> distsamp(formula = ~1 ~ area, data = ltUMF)
#>
#> Density (log-scale):
#> Estimate SE z P(>|z|)
#> (Intercept) 0.2364 0.5123 0.462 0.644
#> area -0.0801 0.0979 -0.817 0.414
#>
#> Detection (log-scale):
#> Estimate SE z P(>|z|)
#> 2.39 0.127 18.7 2.47e-78
#>
#> AIC: 166.0759
#> Number of sites: 12
#>
#> Survey design: line-transect
#> Detection function: halfnorm
#> UnitsIn: m
#> UnitsOut: ha
#>
#>
#>
ms <- modSel(fl, nullmod="Null")
ms
#> nPars AIC delta AICwt cumltvWt Rsq
#> Null 2 164.75 0.00 0.43 0.43 0.000
#> A. 3 165.18 0.43 0.35 0.78 0.122
#> .A 3 166.08 1.32 0.22 1.00 0.055
coef(ms) # Estimates only
#> lam(Int) lam(area) p(Int) p(area)
#> Null -0.1710554 NA 2.386380 NA
#> A. -0.1678270 NA 3.002507 -0.120364
#> .A 0.2364320 -0.08005895 2.386386 NA
SE(ms) # Standard errors only
#> SElam(Int) SElam(area) SEp(Int) SEp(area)
#> Null 0.1337819 NA 0.1273598 NA
#> A. 0.1340212 NA 0.5401575 0.09548038
#> .A 0.5122837 0.0979427 0.1273614 NA
(toExport <- as(ms, "data.frame")) # Everything
#> model formula lam(Int) SElam(Int) lam(area) SElam(area) p(Int)
#> 1 Null ~1 ~ 1 -0.1710554 0.1337819 NA NA 2.386380
#> 2 A. ~area ~ 1 -0.1678270 0.1340212 NA NA 3.002507
#> SEp(Int) p(area) SEp(area) Converge CondNum negLogLike nPars n
#> 1 0.1273598 NA NA 0 5.199344 80.37622 2 12
#> 2 0.5401575 -0.120364 0.09548038 0 1065.747363 79.59224 3 12
#> AIC delta AICwt Rsq cumltvWt
#> 1 164.7524 0.0000000 0.4307243 0.0000000 0.4307243
#> 2 165.1845 0.4320554 0.3470401 0.1224859 0.7777644
#> [ reached 'max' / getOption("max.print") -- omitted 1 rows ]