rfUtilities (version 1.0-2)

rf.significance: Random Forest model significance test

Description

Performs significance test for classification and regression Random Forests models.

Usage

rf.significance(x, xdata, q = 0.99, p = 0.05, nperm = 999, plot = TRUE,
  ...)

Arguments

x

randomForest class object

xdata

Independent variables (x) used in model

q

Quantile threshold to test classification models

p

p-value to test for significance in regression models

nperm

Number of permutations

plot

Plot results (TRUE/FALSE). Dotted line represents p-value/test quantile

...

Additional Random Forests arguments

Value

• A list class object with the following components: For Regression problems: RandRsquare Vector of random R-square values Rsquare The R-square of the "true" model Accept Is the model significant at specified p-value (TRUE/FALSE) TestQuantile Quantile threshold used in significance plot pValueThreshold Specified p-value pValue p-values of randomizations nPerm Number of permutations For Classification problems: RandOOB Vector of random out-of-bag (OOB) values RandMaxError Maximum error of randomizations test.OOB Error if the "true" model Accept Is the model significant at specified p-value (TRUE/FALSE) TestQuantile Quantile threshold used in significance plot pValueThreshold Specified p-value pValue p-values of randomizations nPerm Number of permutations

References

Murphy M.A., J.S. Evans, and A.S. Storfer (2010) Quantify Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91:252-261 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

Examples

# Regression
require(randomForest)
  set.seed(1234)
    data(airquality)
      airquality <- na.omit(airquality)
 ( rf.mdl <- randomForest(x=airquality[,2:6], y=airquality[,1]) )
   ( rf.test <- rf.significance(rf.mdl, airquality[,2:6], nperm=99, ntree=501) )

# Classification
require(randomForest)
  set.seed(1234)
    data(iris)
      iris$Species <- as.factor(iris$Species)
 ( rf.mdl <- randomForest(iris[,1:4], iris[,"Species"], ntree=501) )
   ( rf.perm <- rf.significance(rf.mdl, iris[,1:4], nperm=99, ntree=501) )