Description Usage Arguments Details Value Author(s) References See Also Examples
Draws a table depicting the inputs selected by a number of
sisal
runs, one row for each run.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## S3 method for class 'sisal'
plotSelected(x, useAllNames = TRUE,
pickIntPart = FALSE, intTransform = function(x) x,
formatCArgs = list(), xLabels = 1, yLabels = NULL,
L.f.color = "black", L.v.color = "grey50",
other.color = "white", naFill = other.color,
naStripes = L.v.color, selectedLabels = TRUE,
otherLabels = FALSE,
labelPar = gpar(fontface = 1, fontsize = 20, cex = 0.35),
nestedPar = gpar(fontface = 3),
ranking = c("pairwise", "nested"), tableArgs = list(),
...)
## S3 method for class 'list'
plotSelected(x, ...)

x 
an object of class 
useAllNames 
a 
pickIntPart 
a 
intTransform 
a 
formatCArgs 
a named 
xLabels 
a 
yLabels 
a 
L.f.color 
fill color for table cells representing an input variable in the L.f set. 
L.v.color 
fill color for table cells representing an input variable in the L.v set. 
other.color 
fill color for table cells representing an input variable outside both L.f and L.v. 
naFill 
background color for table cells representing a missing input variable. 
naStripes 
stripe color for table cells representing a missing input variable. 
selectedLabels 
a 
otherLabels 
a 
labelPar 
graphical parameters for labels of table cells. 
nestedPar 
graphical parameters for labels on rows that
represent input selection runs where the best nodes of each size are
all nested. See ‘Details’. Only used if

ranking 
which input ranking method(s) to use. A

tableArgs 
a named 
... 
In the 
Currently the "sisal"
and "list"
methods are the only
methods for the generic function plotSelected
defined by the
sisal package.
Mathematical annotation can be used in text. See plotmath. If
the same input is in both the L.f and the L.v sets,
L.f.color
and L.v.color
are mixed in
alternating stripes. See col2rgb
for a description of
possible color values.
The importance rank of input variables is determined using one or both
of the following two methods (see ranking
):
This method requires that all the nodes with the smallest
validation error among the nodes with the same number of input
variables are nested. Let's imagine a path through the
incrementally smaller best nodes (not necessarily a path in the
search graph) where the edges are labeled with the ID of
the input removed in order to create the smaller model. In this
ranking method, the remaining input variable gets rank 1.
Traversing the path in the reverse direction and printing the edge
labels produces the rest of the input variables from smaller rank
to larger. If hbranches = 1
in sisal
, the models are
always nested and the method agrees with "pairwise"
.
This is Copeland's pairwise aggregation method. It can be used in
all cases, unlike "nested"
. The score of an input
variable is the number of pairwise victories minus the number of
pairwise defeats when compared with other inputs. The inputs are
ranked by their score. The method may result in ties. Tied nodes
are ranked according to ties.method = "min"
in
rank
.
The pairwise comparisons are performed in the following way: In
sisal
, at each stage of the search, input variables are
ordered and inputs are removed starting from one or more (when
hbranches > 1
) of the worst ones according to that
order. A record, let's say C[A, B]
, is
kept of each pair of inputs (A, B) in order to keep
track of how many times A was better than B. Let
L be the set of inputs to remove at the current stage of the
search in one of the branches and M the set of remaining
inputs. Then, C[A, B]
is incremented by
one for all A in M and B in L, but also
for all A in L and B in L such that
A is better than B according to the order used for
picking the inputs to remove. A gets a pairwise victory
over B if
C[A, B] > C[B, A]
.
For information on setting graphical parameters
(labelPar
, nestedPar
), see
gpar
.
The function is usually called for the side effect (a plot is drawn),
but it also returns a grob
representation of the plot.
Mikko Korpela
Pomerol, J.C. and BarbaRomero, S. (2000) Multicriterion decision in management: principles and practice. Springer. p. 122. ISBN: 0792377567.
sisal
, sisalTable
,
plotmath, gpar
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  library(grDevices)
library(grid)
toy1.2 < list(testSisal(Mtimes=10, stepsAhead=1, dataset="tsToy"),
testSisal(Mtimes=10, stepsAhead=2, dataset="tsToy"))
## Resizing enabled:
##  mathematical expressions in titles
##  extracting the integer part of input variable names
grid.newpage()
plotSelected(toy1.2, yLabels = c("+1", "+2"),
main = "Toy time series",
xlab = expression(paste("input variables ",
italic(y[t+l]))),
ylab = expression(paste("output ", italic(y[t+k]))),
pickIntPart = TRUE, intTransform = function(x) x)
## Fixed size plot:
##  some graphical parameters adjusted
##  cex in labelPar adjusts the space around the text in table cells
##  new device the same size as the plot
grb < plotSelected(toy1.2, resizeText = FALSE, resizeTable = FALSE,
axesPar = gpar(fontsize = 11, col = "red"),
labelPar = gpar(fontsize = 14/0.25, cex = 0.25),
fg = "wheat", outerRect = FALSE,
linePar = gpar(lty = "dashed"),
xAxisRot = 45, just = c("left", "top"),
tableArgs = list(x = 0, y = 1), draw = FALSE)
devWidth < convertWidth(grobWidth(grb), unitTo = "inches",
valueOnly = TRUE)
devHeight < convertHeight(grobHeight(grb), unitTo = "inches",
valueOnly = TRUE)
dev.new(width = devWidth, height = devHeight, units = "in", res = 72)
grid.draw(grb)
if (interactive()) {
dev.set(dev.prev())
} else {
dev.off()
}

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