Description Usage Arguments Value Author(s) References Examples
Creates an object of class combinedGradientForest
that represents the synthesis of
two or more gradientForest
objects.
1 | combinedGradientForest(..., nbin = 101, method=2, standardize=c("before","after")[1])
|
... |
any number of |
nbin |
number of bins for common predictor grid. Default set to 101. |
method |
|
standardize |
Should standardization by density occur before or after normalization to R^2?
Takes values |
call |
the matched call |
X |
combined data frame of predictor variables with first column denoting
the name of the source |
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 |
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 |
N. Ellis, CSIRO, Cleveland, Australia. <Nick.Ellis@csiro.au>. S.J. Smith, DFO, Dartmouth, NS, Canada. <Stephen.Smith@dfo-mpo.gc.ca>
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
1 2 3 4 5 6 7 | 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")
|
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