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mmiWMRR is a multimodel inference approach evaluating the relative
importance of predictors used in scaleWMRR
.
mmiWMRR(object, data, scale, detail = TRUE, trace = FALSE)
A model of class WRM
.
Data frame.
0 or higher integers possible (limit depends on sample size).
scale
=1 is equivalent to WRM
with level
=1.
Remove smooth wavelets? If TRUE
, only detail
components are analyzed. If set to FALSE
, smooth and detail
components are analyzed. Default is TRUE
.
Logical value indicating whether to print results to console.
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
It performs automatically
generated model selection and creates a model
selection table according to the approach of multi-model inference
(Burnham & Anderson, 2002). The analysis is carried out for scale-specific
regressions (i.e. where scaleWMRR
can be used). AIC is
used to obtain model
selection weights and to rank the models.
Futhermore, this function requires that all predictor variables
be continuous.
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 two-dimensional wavelet analysis. Journal of Biogeography 43: 2502-2512.
# NOT RUN {
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)
# }
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