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