Description Usage Arguments Value Author(s) See Also Examples
View source: R/mds.plot.forestRK.R
Plots 2D MDS (Multi-Dimensional Scaling) ggplot
of the test observations
based on the provided forestRK
model, and each test observation is
colour coded by their predicted class types.
The plot also has legends that tells user which colour pertains to which predicted class type.
The existing R functions dist
and cmdscale
were used in this
function to compute the Multi-Dimensional Scales of the test data.
1 2 3 4 | mds.plot.forestRK(pred.forestRK.object = pred.forestRK(),
plot.title ="MDS Plot of Test Data Colour Coded by Forest RK Model Predictions",
xlab ="First Coordinate", ylab = "Second Coordinate",
colour.lab = "Predictions By The Random Forest RK Model")
|
pred.forestRK.object |
a |
plot.title |
an user specified title for the mds plot; the default is "MDS Plot of Test Data Colour Coded by Forest RK Model Predictions". |
xlab |
label for the x-axis of the plot; the default is "First Coordinate". |
ylab |
label for the y-axis of the plot; the default is "Second Coordinate". |
colour.lab |
label title for the legend that specifies categories for each colour; the default is "Predictions By The Random Forest RK Model". |
A multidimensional scaling ggplot (2D) of the test observations, colour coded by their predicted class types.
Hyunjin Cho, h56cho@uwaterloo.ca Rebecca Su, y57su@uwaterloo.ca
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## example: iris dataset
## load the forestRK package
library(forestRK)
x.train <- x.organizer(iris[,1:4], encoding = "num")[c(1:25,51:75,101:125),]
x.test <- x.organizer(iris[,1:4], encoding = "num")[c(26:50,76:100,126:150),]
y.train <- y.organizer(iris[c(1:25,51:75,101:125),5])$y.new
y.factor.levels <- y.organizer(iris[c(1:25,51:75,101:125),5])$y.factor.levels
# min.num.obs.end.node.tree is set to 5 by default;
# entropy is set to TRUE by default
# typically the nbags and samp.size has to be much larger than 30 and 50
pred.forest.rk <- pred.forestRK(x.test = x.test,
x.training = x.train, y.training = y.train,
nbags = 30, samp.size = 50,
y.factor.levels = y.factor.levels)
# generate a classical mds plot of test observations
# and colour code them by the predicted class
mds.plot.forestRK(pred.forest.rk)
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