Description Usage Arguments Details Value Author(s) References See Also Examples
Plot method for the gradientForest objects.
1 2 3 |
x |
an object of class |
plot.type |
specifies the type of plot defined for the object. Current choices for
gradientForest objects are |
par.args |
a list object with arguments to be passed on to be used by |
plot.args |
a list object with arguments to be passed on according
the |
... |
further arguments passed to or from other methods. When
|
The following are the default settings when par.args=NULL
for each plot type. See par
for the definition of each of the arguments.
Overall.Importance: list(mfrow = c(1, 2), mar = c(4, 6, 2, 1))
Split.Density: list(mar =c(4.5, 1.5, 0.5, 4.5), omi = c(0.1, 0.25, 0.1, 0.1))
.
Cumulative.Importance: list(mar=c(0.0,2.1,1.1,0), omi=c(0.75, 0.75, 0.1, 0.1))
.
See plot.args below for the definitions for show.species
and show.overall
.
Performance: list(mfrow=c(1,1))
The following are the default settings when plot.args=NULL
for each plot type.
Overall.Importance: list(cex.axis = 0.7, cex.names = cex.axis, las=1,horiz = TRUE)
Split.Density: list(leg.posn="topright",bin=FALSE,
nbin=101, leg.panel=1, barwidth=1,
cex.legend=0.8, line.ylab=1.5)
Cumulative.Importance: list(leg.posn="topleft",Legend=TRUE,
common.scale=FALSE, line.ylab=1.0,
cex.legend=0.75,
show.species=TRUE,show.overall=TRUE,leg.nspecies=10)
, where leg.nspecies
is set to the
number of species in the legend (in order of importance) for cumulative importance plot. If common.scale
is TRUE
the scale of the y axis is the same for all panels. If
show.species
is TRUE
the species cumulative curves are shown, and if show.overall
the overall cumulative curves are shown.
Performance: list(horizontal = FALSE,
show.names = FALSE,
las=2,
cex.axis = 0.7,
cex.labels=0.7,
line=2)
, where show.names
is
set to TRUE
or FALSE
to override the defaults on whether an x-axis label on the 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 split density plot shows the density of splits, the density of the data and the ratio of the densities for the the chosen physical variables.
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.
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, 2001.
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, URL http://CRAN.R-project.org/doc/Rnews/.
1 2 3 4 5 6 7 8 | data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
f1 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6], ntree=10)
plot(f1,plot.type="Overall.Importance")
plot(f1,plot.type="Split.Density")
plot(f1,plot.type="Cumulative.Importance")
plot(f1,plot.type="Performance")
|
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