rfUtilities (version 1.0-2)

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

x

randomForest class object

xdata

Independent variables used in model

d

Which class to perturb

p

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: mean.error Mean of RMSE sd.error Standard deviation of RMSE rmse Root mean squared error (RMSE) for each perturbed probability 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

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)