Plot method for
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an object of class
specifies the type of plot defined for the object. Current choices for
arguments to be passed on to be used by
arguments to be passed on according
further arguments passed to or from other methods.
The following are the default settings for par.args for each plot type. See
par for the definition of each of the arguments.
list(mfrow = c(1, 2), mar = c(4, 6, 2, 1))
The following are the default settings for plot.args for each plot type.
list(cex.axis = 0.7, cex.names = cex.axis, horiz = TRUE, las = 1)
show.gf.names=TRUE, sort=TRUE), where:
weight is the type of weighting to perform across
gradientForest objects (see same argument in
use.diff=TRUE the differenced cumulative importances are plotted;
prednames is the names of the predictors for which plots are required;
gradientForest object weight per bin by colour saturation;
show.gf.names=FALSE do not show the individual
gradientForest object cumulative curves;
sort=TRUE, sort predictors by importance, otherwise use order in
weight has multiple elements, the given weightings are shown but not the individual
list(horizontal = FALSE, show.names = FALSE, cex.axis = 0.7, las = 2), where
show.names is set to
to override the defaults on whether an x-axis label on performance plot is printed for each group.
The overall importance plot shows a simple barplot of the ranked importances of the physical variables. The most reliable importances are the R^2 weighted importances.
The predictor ranges plot shows box plots of the observed predictors separately for each
The predictor density plot shows the density of the observed predictors with
gradientForest objects denoted
by colour; the combined density is also shown.
The cumulative importance plot is an integrated form of the split density plot. The cumulative importance is plotted separately for all species and averaged over all species. The cumulative importance can be interpreted as a mapping from an environmental gradient on to biological gradient
The performance plot shows the goodness of fit performance measures for all species for which the physical variables have some predictive power. For regression, the measure is out-of-bag R^2. For classification, the measure is out-of-bag error rate.
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/
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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") plot(f12,plot.type="Predictor.Density") plot(f12,plot.type="Cumulative.Importance") plot(f12,plot.type="Cumulative.Importance",plot.args=list(weight="uniform")) plot(f12,plot.type="Cumulative.Importance",plot.args=list(weight="species")) plot(f12,plot.type="Cumulative.Importance",plot.args=list(weight="rsq.total")) plot(f12,plot.type="Cumulative.Importance",plot.args=list(weight="rsq.mean"))
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