if (FALSE) {
## From vignette(randomForestRegression, package="ggRandomForests")
data(Boston, package="MASS")
rfsrc_boston <- randomForestSRC::rfsrc(medv~., Boston)
varsel_boston <- var.select(rfsrc_boston)
rm_pts <- quantile_pts(rfsrc_boston$xvar$rm,
groups = 9,
intervals = TRUE)
partial_boston_surf <- lapply(rm_pts, function(ct) {
rfsrc_boston$xvar$rm <- ct
randomForestSRC::plot.variable(
rfsrc_boston,
xvar.names = "lstat",
time = 1,
npts = 10,
show.plots = FALSE,
partial = TRUE
)
})
# Instead of groups, we want the raw rm point values,
# To make the dimensions match, we need to repeat the values
# for each of the 50 points in the lstat direction
rm.tmp <- do.call(c,lapply(rm_pts,
function(grp) {rep(grp,
length(partial_boston_surf))}))
# Convert the list of plot.variable output to
partial_surf <- do.call(rbind,lapply(partial_boston_surf, gg_partial))
# attach the data to the gg_partial_coplot
partial_surf$rm <- rm.tmp
# Transform the gg_partial_coplot object into a list of three named matrices
# for surface plotting with plot3D::surf3D
srf <- surface_matrix(partial_surf, c("lstat", "rm", "yhat"))
}
if (FALSE) {
# surf3D is in the plot3D package.
library(plot3D)
# Generate the figure.
surf3D(x=srf$x, y=srf$y, z=srf$z, col=topo.colors(10),
colkey=FALSE, border = "black", bty="b2",
shade = 0.5, expand = 0.5,
lighting = TRUE, lphi = -50,
xlab="Lower Status", ylab="Average Rooms", zlab="Median Value"
)
}
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