Description Usage Arguments Details Author(s) Examples
Method to plot an object of forestFloor-class. Plot partial feature contributions of the most important variables. Colour gradients can be applied two show possible interactions.
1 2 3 4 5 | ggPlotForestFloor(ff,
plot_seq=NULL,
col=NULL,
orderByImportance = TRUE
)
|
ff |
foretFloor-object, also abbrivated ff..
Computed topology of randomForest-model, the output from the forestFloor function |
plot_seq |
a numeric vector describing which variables and in what sequence to plot, ordered by importance as default, order_by_importance = F then by feature/coloumn order of training data. |
orderByImportance |
TRUE / FALSE should plotting and plot_seq be ordered after importance. Most important feature plot first(TRUE) |
col |
color of points. Should either be one single colour or a vector of lenght N.samples with colour of each individual sample. Such vector is normally created with eg. ColVec=fcol(ff) |
Beta ggplot2 will gradually be implemented here. This function relies on ggplot2 and extraGrid
Soren Havelund Welling
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Not run:
##testing ggplot functionality
rm(list=ls())
library(forestFloor)
library(randomForest)
#simulate data
obs=1000
vars = 18
X = data.frame(replicate(vars,rnorm(obs)))
Y = with(X, X1^2 + sin(X2*pi) + 2 * X3 * X4 + 1 * rnorm(obs))
#grow a forest, remeber to include inbag
rfo=randomForest(X,Y,keep.inbag = TRUE,sampsize=250,ntree=50)
#compute topology
ff = forestFloor(rfo,X)
ggPlotForestFloor(ff,1:9)
plot(ff,1:9,col=fcol(ff))
## End(Not run)
|
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