SFCB-class | R Documentation |
Print, plot, manipulate or compute quality for outputs of the
sfcb
function (SFCB
object)
## S3 method for class 'SFCB'
summary(object, ...)
## S3 method for class 'SFCB'
print(x, ...)
## S3 method for class 'SFCB'
plot(
x,
...,
plot.type = c("dendrogram", "selection", "importance", "quality"),
sel.type = c("importance", "selection"),
threshold = "none",
shape.imp = c("boxplot", "histogram"),
quality.crit = "mse"
)
extract_at(object, at)
quality(object, ground_truth, threshold = NULL)
object |
a |
... |
not used |
x |
a |
plot.type |
type of the plot. Default to |
sel.type |
when |
threshold |
numeric value. If not |
shape.imp |
when |
quality.crit |
character vector (length 1 or 2) indicating one or two
quality criteria to display. The values have to be taken in { |
at |
numeric vector. Set of the number of intervals to extract for |
ground_truth |
numeric vector of ground truth. Target variables to compute qualities correspond to non-zero entries of this vector |
The plot
functions can be used in four different ways to
extract information from the SFCB
object:
plot.type == "dendrogram"
displays the dendrogram obtained at
the clustering step of the method. Depending on the cases, the dendrogram
comes with additional information on clusters, variable selections and/or
importance values;
plot.type == "selection"
displays either the evolution of the
importance for the simulation with the best (smallest) MSE for each time
step in the range of the functional predictor or the evolution of the
selected intervals along the whole range of the functional prediction also
for the best MSE;
plot.type == "importance"
displays a summary of the importance
values over the whole range of the functional predictor and for the
different experiments. This summary can take the form of a boxplot or of
an histogram;
plot.type == "quality"
displays one or two quality distribution
with respect to the different experiments and different number of intervals.
Remi Servien, remi.servien@inrae.fr
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr
Servien, R. and Vialaneix, N. (2023) A random forest approach for interval selection in functional regression. Preprint.
sfcb
data(truffles)
out1 <- sfcb(rainfall, truffles, group.method = "adjclust",
summary.method = "pls", selection.method = "relief")
summary(out1)
## Not run:
plot(out1)
plot(out1, plot.type = "selection")
plot(out1, plot.type = "importance")
## End(Not run)
out2 <- sfcb(rainfall, truffles, group.method = "adjclust",
summary.method = "basics", selection.method = "none",
range.at = c(5, 7))
out3 <- extract_at(out2, at = 6)
summary(out3)
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