Description Arguments Value Note Author(s) References Examples
Model selection results from an unmarkedFitList
object 
an object of class "unmarkedFitList" created by the function

nullmod 
optional character naming which model in the

A S4 object with the following slots
Full 
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) Rsquared index, which is only returned when the nullmod argument is specified. 
Names 
matrix referencing column names of estimates (row 1) and standard errors (row 2). 
Two requirements exist to conduct AICbased modelselection and modelaveraging in unmarked. First, the data objects (ie, unmarkedFrames) must be identical among fitted models. Second, the response matrix must be identical among fitted models after missing values have been removed. This means that if a response value was removed in one model due to missingness, it needs to be removed from all models.
Richard Chandler rbchan@uga.edu
Nagelkerke, N.J.D. (2004) A Note on a General Definition of the Coefficient of Determination. Biometrika 78, pp. 691692.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  data(linetran)
(dbreaksLine < c(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
ms < modSel(fl, nullmod="Null")
ms
coef(ms) # Estimates only
SE(ms) # Standard errors only
(toExport < as(ms, "data.frame")) # Everything

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.