rf.class.sensitivity: Random Forests class-level sensitivity analysis

Description

Performs a sensitivity analysis on a specified class in a random forests model

Usage

rf.class.sensitivity(x, xdata, d = "1", p = 0.05, nperm = 999,
  plot = TRUE, seed = NULL, ...)

Arguments

randomForest class object

xdata

Independent variables used in model

Which class to perturb

Proportion of class to be randomized

nperm

Number of permutations

plot

Plot results (TRUE/FALSE)

seed

Random seed value

...

Additional arguments passed to randomForest

Value

List object with following components: @return mean.error Mean of RMSE @return sd.error Standard deviation of RMSE @return rmse Root mean squared error (RMSE) for each perturbed probability @return probs data.frame with "true" estimate in first column and perturbed probabilities in subsequent columns.

References

Evans J.S., M.A. Murphy, Z.A. Holden, S.A. Cushman (2011). Modeling species distribution and change using Random Forests CH.8 in Predictive Modeling in Landscape Ecology eds Drew, CA, Huettmann F, Wiersma Y. Springer

Gardner, R.H., R.V. O'Neill, M.G. Turner, and V.H. Dale (1989). Quantifying scale-dependent effects of animal movements with simple percolation models. Landscape Ecology 3:217-227.

Examples

Run this code

# NOT RUN {
library(randomForest)	
data(iris)
  y <- as.factor(ifelse(iris$Species == "setosa" | 
                 iris$Species == "virginica", 1, 0) )
    xdata <- iris[,1:4] 

rf.mdl <- randomForest(xdata, y, ntree=501) 
  ua <- rf.class.sensitivity(rf.mdl, xdata=xdata, nperm=20, ntree=501, plot=TRUE)
      
# }