The function aims to provide a similar look-and-feel to the
built-in plot.default
and curve
function.
# S4 method for FuzzyNumber,missing
plot(x, y, from=NULL, to=NULL, n=101, at.alpha=NULL,
draw.membership.function=TRUE, draw.alphacuts=!draw.membership.function,
xlab=NULL, ylab=NULL, xlim=NULL, ylim=NULL,
type="l", col=1, lty=1, pch=1, lwd=1,
shadowdensity=15, shadowangle=45, shadowcol=col, shadowborder=NULL,
add=FALSE, ...)# S4 method for TrapezoidalFuzzyNumber,missing
plot(x, y, from=NULL, to=NULL,
draw.membership.function=TRUE, draw.alphacuts=!draw.membership.function,
xlab=NULL, ylab=NULL, xlim=NULL, ylim=NULL,
type="l", col=1, lty=1, pch=1, lwd=1, add=FALSE, ...)
# S4 method for PiecewiseLinearFuzzyNumber,missing
plot(x, y, from=NULL, to=NULL,
draw.membership.function=TRUE, draw.alphacuts=!draw.membership.function,
xlab=NULL, ylab=NULL, xlim=NULL, ylim=NULL,
type="l", col=1, lty=1, pch=1, lwd=1, add=FALSE, ...)
# S4 method for DiscontinuousFuzzyNumber,missing
plot(x, y, from=NULL, to=NULL,
n=101, draw.membership.function=TRUE, draw.alphacuts=!draw.membership.function,
xlab=NULL, ylab=NULL, xlim=NULL, ylim=NULL,
type="l", col=1, lty=1, pch=1, lwd=1,
add=FALSE, ...)
a fuzzy number
not used
numeric;
numeric;
numeric; number of points to probe
numeric vector; give exact alpha-cuts at which linear interpolation should be done
logical; you want membership function (TRUE
) or alpha-cuts plot (FALSE
)?
logical; defaults !draw.membership.function
character; x-axis label
character; y-axis label
numeric;
numeric;
character; defaults "l"
; plot type, e.g.~"l"
for lines, "p"
for points, or "b"
for both
see plot.default
see plot.default
see plot.default
see plot.default
numeric; for shadowed sets;
numeric; for shadowed sets;
color specification, see plot.default
; for shadowed sets;
numeric; for shadowed sets;
logical; add another FuzzyNumber to existing plot?
further arguments passed to plot.default
Returns nothing really interesting.
Note that if from > a1
then it is set to a1
.
Other FuzzyNumber-method:
Arithmetic
,
Extract
,
FuzzyNumber-class
,
FuzzyNumber
,
alphaInterval()
,
alphacut()
,
ambiguity()
,
as.FuzzyNumber()
,
as.PiecewiseLinearFuzzyNumber()
,
as.PowerFuzzyNumber()
,
as.TrapezoidalFuzzyNumber()
,
as.character()
,
core()
,
distance()
,
evaluate()
,
expectedInterval()
,
expectedValue()
,
integrateAlpha()
,
piecewiseLinearApproximation()
,
show()
,
supp()
,
trapezoidalApproximation()
,
value()
,
weightedExpectedValue()
,
width()
Other PiecewiseLinearFuzzyNumber-method:
Arithmetic
,
Extract
,
PiecewiseLinearFuzzyNumber-class
,
PiecewiseLinearFuzzyNumber
,
^,PiecewiseLinearFuzzyNumber,numeric-method
,
alphaInterval()
,
arctan2()
,
as.PiecewiseLinearFuzzyNumber()
,
as.PowerFuzzyNumber()
,
as.TrapezoidalFuzzyNumber()
,
as.character()
,
expectedInterval()
,
fapply()
,
maximum()
,
minimum()
,
necessityExceedance()
,
necessityStrictExceedance()
,
necessityStrictUndervaluation()
,
necessityUndervaluation()
,
possibilityExceedance()
,
possibilityStrictExceedance()
,
possibilityStrictUndervaluation()
,
possibilityUndervaluation()
Other TrapezoidalFuzzyNumber-method:
Arithmetic
,
TrapezoidalFuzzyNumber-class
,
TrapezoidalFuzzyNumber
,
TriangularFuzzyNumber()
,
alphaInterval()
,
as.PiecewiseLinearFuzzyNumber()
,
as.PowerFuzzyNumber()
,
as.TrapezoidalFuzzyNumber()
,
expectedInterval()
Other DiscontinuousFuzzyNumber-method:
DiscontinuousFuzzyNumber-class
,
DiscontinuousFuzzyNumber
,
Extract
,
distance()
,
integrateAlpha()
# NOT RUN {
plot(FuzzyNumber(0,1,2,3), col="gray")
plot(FuzzyNumber(0,1,2,3, left=function(x) x^2, right=function(x) 1-x^3), add=TRUE)
plot(FuzzyNumber(0,1,2,3, lower=function(x) x, upper=function(x) 1-x), add=TRUE, col=2)
# }
Run the code above in your browser using DataLab