Description Usage Arguments Details Value Examples
Creates model selection tables, calculates and plots relative variable importance based on the scale level of a given model.
1 2 3 4 5 6 7 8 9 10 11 12 13 |
formula |
A model formula |
family |
|
data |
A data frame or set of vectors of equal length. |
coord |
X,Y coordinates for each observation. Coordinates should be consecutive integers. |
maxlevel |
An integer for maximum scale level |
detail |
Remove smooth wavelets? If |
wavelet |
Type of wavelet: |
wtrafo |
Type of wavelet transform: |
n.eff |
A numeric value of effective sample size |
trace |
Should R print progress updates to the console? Default is FALSE |
customize_plot |
Additional plotting parameters passed to |
Calculates the relative importance of each variable
using multi-model inference methods in a wavelet multi-resolution regression
framework implemented in mmiWMRR
. The scale level dependent
results are then graphically displayed.
A list containing
1. A matrix containing the relative importance of each variable in the regression at each value of the scale level.
2. A ggplot
object containing a plot of the relative
variable importance
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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)
# Plot scale-dependent relative variable importance
rvi <- rvi.plot(carlina.horrida ~ aridity + land.use,
family = "poisson",
data = carlinadata,
coord = coords,
maxlevel = 4,
detail = TRUE,
wavelet = "d4")
rvi$plot
rvi$rvi
## End(Not run)
|
level=1 level=2 level=3 level=4
aridity 0.005 1 1 1
land.use 1.000 1 1 0
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