Description Usage Arguments Details Value Author(s) References See Also Examples
mmiWMRR is a multimodel inference approach evaluating the relative
importance of predictors used in scaleWMRR
.
1 
object 
A model of class 
data 
Data frame. 
scale 
0 or higher integers possible (limit depends on sample size).

detail 
Remove smooth wavelets? If 
trace 
Logical value indicating whether to print results to console. 
It performs automatically
generated model selection and creates a model
selection table according to the approach of multimodel inference
(Burnham & Anderson, 2002). The analysis is carried out for scalespecific
regressions (i.e. where scaleWMRR
can be used). AIC is
used to obtain model
selection weights and to rank the models.
Furthermore, this function requires that all predictor variables
be continuous.
mmiWMRR
returns a list containing the following elements
result
A matrix containing slopes, degrees of freedom, likelihood, AIC, delta, and weight values for the set of candidate models. The models are ranked by AIC.
level
An integer corresponding to scale
Gudrun Carl
Burnham, K.P. & Anderson, D.R. (2002) Model selection and multimodel inference. Springer, New York.
Carl G, Doktor D, Schweiger O, Kuehn I (2016) Assessing relative variable importance across different spatial scales: a twodimensional wavelet analysis. Journal of Biogeography 43: 25022512.
aic.calc
, rvi.plot
,
MuMIn, WRM
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  data(carlinadata)
coords < carlinadata[ ,4:5]
## Not run:
wrm < WRM(carlina.horrida ~ aridity + land.use,
family = "poisson",
data = carlinadata,
coord = coords,
level = 1,
wavelet = "d4")
mmi < mmiWMRR(wrm,
data = carlinadata,
scale = 3,
detail = TRUE,
trace = FALSE)
## End(Not run)

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