rfUtilities (version 1.0-2)

rf.modelSel: Random Forest Model Selection

Description

Implements Murphy et al., (2010) Random Forests model selection approach.

Usage

rf.modelSel(xdata, ydata, imp.scale = "mir", r = c(0.25, 0.5, 0.75),
  final.model = FALSE, plot.imp = TRUE, seed = NULL, parsimony = NULL,
  ...)

Arguments

xdata

X Data for model

ydata

Y Data for model

imp.scale

Type of scaling for importance values (mir or se), default is mir

r

Vector of importance percentiles to test i.e., c(0.1, 0.2, 0.5, 0.7, 0.9)

final.model

Run final model with selected variables (TRUE/FALSE)

plot.imp

Plot variable importance (TRUE/FALSE)

seed

Sets random seed in the R global environment. This is highly suggested.

parsimony

Threshold for competing model (0-1)

...

Arguments to pass to randomForest (e.g., ntree=1000, replace=TRUE, proximity=TRUE)

Value

• A list class object with the following components: rf.final - Final selected model, if final=TRUE(randomForest model object) selvars - final selected variables (vector) test - Validation parameters used on model selection (data.frame) importance - Importance values for selected model (data.frame) parameters - Variables used in each tested model (list)

References

Evans, J.S. and S.A. Cushman (2009) Gradient Modeling of Conifer Species Using Random Forest. Landscape Ecology 5:673-683. 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

# Classification on iris data
require(randomForest)
data(iris)
  iris$Species <- as.factor(iris$Species)
( rf.class <- rf.modelSel(iris[,1:4], iris[,"Species"], seed=1234, imp.scale="mir") )
( rf.class <- rf.modelSel(iris[,1:4], iris[,"Species"], seed=1234, imp.scale="mir",
                          parsimony=0.03) )
 vars <- rf.class$selvars
 ( rf.fit <- randomForest(x=iris[,vars], y=iris[,"Species"]) )

# Regression on airquality data
data(airquality)
  airquality <- na.omit(airquality)
( rf.regress <- rf.modelSel(airquality[,2:6], airquality[,1], imp.scale="se") )
( rf.regress <- rf.modelSel(airquality[,2:6], airquality[,1], imp.scale="se", parsimony=0.03) )

# To use parameters from competing model
vars <- rf.regress$parameters[[3]]

# To use parameters from selected model
vars <- rf.regress$selvars

( rf.fit <- randomForest(x=airquality[,vars], y=airquality[,1]) )