combinedGradientForest: Combine gradientForest objects

In gradientForest: Random Forest functions for the Census of Marine Life synthesis project.

Combine gradientForest objects

Description

Creates an object of class combinedGradientForest that represents the synthesis of two or more gradientForest objects.

Usage

combinedGradientForest(..., nbin = 101, method=2, standardize=c("before","after")[1])

Arguments

...	any number of gradientForest objects
nbin	number of bins for common predictor grid. Default set to 101.
method	method=1, calls combine.cumulative.importance.method1, method=2 calls combine.cumulative.importance.
standardize	Should standardization by density occur before or after normalization to R^2? Takes values "before" or "after", or abbreviations thereof. Default "before".

Value

call	the matched call
X	combined data frame of predictor variables with first column denoting the name of the source gradientForest object
dens	list of lists of Gaussian kernel density estimates for each physical variable and source.
rsq	a named vector of species R^2 for those species for which the physical variables have some predictive power. See gradientForest for details.
imp.rsq	a matrix of importance values for predictor and species. The columns sum to species R^2.
nspec	a named vector of number of species for which the physical variables have some predictive power.
CU	list of lists of cumulative importance for each predictor and source. Also holds the combined cumulative importance and the gridded density per predictor.
gf.names	list of gradientForest objects having information on each predictor.

Author(s)

N. Ellis, CSIRO, Cleveland, Australia. <[e-mail]>. S.J. Smith, DFO, Dartmouth, NS, Canada. <[e-mail]>

References

Breiman, L. (2001) Random Forests, Machine Learning, 45(1), 5–32.

Ellis, N., Smith, S.J., and Pitcher, C.R. (2012) Gradient Forests: calculating importance gradients on physical predictors. Ecology, 93, 156–168.

Liaw, A. and Wiener, M. (2002) Classification and regression by randomforest. R News, 2(3), 18–22. http://CRAN.R-project.org/doc/Rnews/

Strobl, C. Boulesteix, A.-L., Kneib, T., Augustin, T. and Zeilis, A. (2008) Conditional variable importance for random forests. BMC Bioinformatics, 9, 307–317. Open Access: http://www.biomedcentral.com/1471-2105/9/307

Examples

data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
f1 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6], ntree=10)
f2 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6+6], ntree=10)
f12 <- combinedGradientForest(west=f1,east=f2)
plot(f12,plot.type="Predictor.Ranges")

gradientForest documentation built on Aug. 24, 2023, 3:03 p.m.