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
,
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
,
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
,
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