R: Model selection results from an unmarkedFitList

modSel {unmarked} | R Documentation

## Model selection results from an unmarkedFitList

### Description

Model selection results from an unmarkedFitList

### Arguments

`object` | an object of class "unmarkedFitList" created by the function `fitList`.
`nullmod` | optional character naming which model in the `fitList` contains results from the null model. Only used in calculation of Nagelkerke's R-squared index.

### Value

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) R-squared 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).

### Note

Two requirements exist to conduct AIC-based model-selection and model-averaging 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.

### Author(s)

Richard Chandler rbchan@uga.edu

### References

Nagelkerke, N.J.D. (2004) A Note on a General Definition of the Coefficient of Determination. Biometrika 78, pp. 691-692.

### Examples

    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

[Package unmarked version 1.0.0 Index]