SFCB-class {SISIR}

Methods for SFCB objects

Description

Print, plot, manipulate or compute quality for outputs of the sfcb function (SFCB object)

Usage

## 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)

Arguments

object

a SFCB object

...

not used

x

a SFCB object

plot.type

type of the plot. Default to "dendrogram" (see Details)

sel.type

when plot.type == "selection", criterion on which to base the selection. Default to "importance"

threshold

numeric value. If not NULL, selection of variables to compute qualities is based on a threshold of importance values extract_at

shape.imp

when plot.type == "importance", type of plot to represent the importance. Default to "boxplot"

quality.crit

character vector (length 1 or 2) indicating one or two quality criteria to display. The values have to be taken in {"mse", "time", "Precision", "Recall", "ARI", "NMI"}. If "time" is chosen, it can not be associated with any other criterion

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

Details

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.

Author(s)

Remi Servien, [e-mail]
Nathalie Vialaneix, [e-mail]

References

Servien, R. and Vialaneix, N. (2023) A random forest approach for interval selection in functional regression. Preprint.

See Also

sfcb

Examples

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)