| rvi.plot | R Documentation | 
Creates model selection tables, calculates and plots relative variable importance based on the scale level of a given model.
rvi.plot(
  formula,
  family,
  data,
  coord,
  maxlevel,
  detail = TRUE,
  wavelet = "haar",
  wtrafo = "dwt",
  n.eff = NULL,
  trace = FALSE,
  customize_plot = NULL
)
| 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
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)
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