Package ‘MuMIn’

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34 nested Arguments x indices rank a "model.selection" object (result of dredge or model.sel). if omitted or "none" then the function checks if, for each model, there are any higher ranked models nested within it. If "numeric" or "rownames", indices or names of all nested models are returned. See “Value”. the name of the column with the ranking values (defaults to the one before “delta”). Only used if indices is "none". Details In model comparison, a model is said to be “nested” within another model if it contains a subset of parameters of the latter model, but does not include other parameters (e.g. model ‘A+B’ is nested within ‘A+B+C’ but not ‘A+C+D’). This function can be useful in a model selection approach suggested by Richards (2008), in which more complex variants of any model with a lower IC value are excluded from the candidate set. Value A vector of length equal to the number of models (table rows). If indices = "none" (the default), it is a vector of logical values where i-th element is TRUE if any model(s) higher up in the table are nested within it (i.e. if simpler models have lower IC pointed by rank). For indices other than "none", the function returns a list of vectors of numeric indices or names of models nested within each i-th model. Note This function determines nesting based only on fixed model terms, within groups of models sharing the same ‘varying’ parameters (see dredge and example in Beetle). Author(s) Kamil Bartoń References Richards, S. A., Whittingham, M. J., Stephens, P. A (2011). Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework. Behavioral Ecology and Sociobiology, 65: 77-89 Richards, S. A (2008) Dealing with overdispersed count data in applied ecology. Journal of Applied Ecology 45: 218–227 See Also dredge, model.sel
par.avg 35 Examples fm
Page 1 and 2: Type Package Title Multi-Model Infe
Page 3 and 4: MuMIn-package 3 model.avg calculate
Page 5 and 6: arm.glm 5 See Also Akaike’s An In
Page 7 and 8: Beetle 7 Beetle Flour beetle mortal
Page 9 and 10: Cement 9 print(tab, digits = 3, na.
Page 11 and 12: dredge 11 varying extra x Details a
Page 13 and 14: dredge 13 Value Note ‘*nvar*‘ n
Page 15 and 16: exprApply 15 # Add other informatio
Page 17 and 18: Formula manipulation 17 # submodels
Page 19 and 20: GPA 19 See Also dredge and pdredge,
Page 21 and 22: Information criteria 21 importance(
Page 23 and 24: merge.model.selection 23 Arguments
Page 25 and 26: model.avg 25 full, adjust.se logica
Page 27 and 28: model.avg 27 coef, vcov, confint an
Page 29 and 30: model.sel 29 # (in this case it doe
Page 31 and 32: model.selection.object 31 model.sel
Page 33: nested 33 Note • spml* (if fitted
Page 37 and 38: pdredge 37 Arguments Details Value
Page 39 and 40: plot.model.selection 39 modSel(fitL
Page 41 and 42: predict.averaging 41 Details predic
Page 43 and 44: QAIC 43 Description Calculate a mod
Page 45 and 46: QIC 45 Arguments Value Note object
Page 47 and 48: .squaredLR 47 Author(s) This implem
Page 49 and 50: std.coef 49 See Also summary.lm, r.
Page 51 and 52: stdize 51 stdize Standardize data D
Page 53 and 54: stdize 53 Value stdize returns a ve
Page 55 and 56: subset.model.selection 55 ## S3 met
Page 57 and 58: updateable 57 x an object from whic
Page 59 and 60: Weights 59 ### ## Not run: # "lmeki
Page 61 and 62: Index ∗Topic datasets Beetle, 7 C
Page 63: INDEX 63 negbin, 32 nested, 33 null

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