spind (version 2.1.3)

mmiWMRR: Multi-model inference for wavelet multiresolution regression

Description

mmiWMRR is a multimodel inference approach evaluating the relative importance of predictors used in scaleWMRR.

Usage

mmiWMRR(object, data, scale, detail = TRUE, trace = FALSE)

Arguments

object

A model of class WRM.

data

Data frame.

scale

0 or higher integers possible (limit depends on sample size). scale=1 is equivalent to WRM with level=1.

detail

Remove smooth wavelets? If TRUE, only detail components are analyzed. If set to FALSE, smooth and detail components are analyzed. Default is TRUE.

trace

Logical value indicating whether to print results to console.

Value

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

Details

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.

References

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.

See Also

aic.calc, rvi.plot, MuMIn, WRM

Examples

# 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)

# }