stepfun {stats} | R Documentation |
Given the vectors (x_1,\ldots, x_n)
and
(y_0,y_1,\ldots, y_n)
(one value
more!), stepfun(x,y,...)
returns an interpolating
‘step’ function, say fn
. I.e., fn(t) =
c
_i
(constant) for t \in (x_i, x_{i+1})
and at the abscissa values, if (by default)
right = FALSE
, fn(x_i) = y_i
and for
right = TRUE
, fn(x_i) = y_{i-1}
, for
i=1,\ldots,n
.
The value of the constant c_i
above depends on the
‘continuity’ parameter f
.
For the default, right = FALSE, f = 0
,
fn
is a cadlag function, i.e., continuous at right,
limit (‘the point’) at left.
In general, c_i
is interpolated in between the
neighbouring y
values,
c_i= (1-f) y_i + f\cdot y_{i+1}
.
Therefore, for non-0 values of f
, fn
may no longer be a proper
step function, since it can be discontinuous from both sides, unless
right = TRUE, f = 1
which is right-continuous.
stepfun(x, y, f = as.numeric(right), ties = "ordered",
right = FALSE)
is.stepfun(x)
knots(Fn, ...)
as.stepfun(x, ...)
## S3 method for class 'stepfun'
print(x, digits = getOption("digits") - 2, ...)
## S3 method for class 'stepfun'
summary(object, ...)
x |
numeric vector giving the knots or jump locations of the step
function for |
y |
numeric vector one longer than |
f |
a number between 0 and 1, indicating how interpolation outside
the given x values should happen. See |
ties |
Handling of tied |
right |
logical, indicating if the intervals should be closed on the right (and open on the left) or vice versa. |
Fn , object |
an R object inheriting from |
digits |
number of significant digits to use, see |
... |
potentially further arguments (required by the generic). |
A function of class "stepfun"
, say fn
.
There are methods available for summarizing ("summary(.)"
),
representing ("print(.)"
) and plotting ("plot(.)"
, see
plot.stepfun
) "stepfun"
objects.
The environment
of fn
contains all the
information needed;
"x" , "y" |
the original arguments |
"n" |
number of knots (x values) |
"f" |
continuity parameter |
"yleft" , "yright" |
the function values outside the knots |
"method" |
(always |
The knots are also available via knots(fn)
.
Martin Maechler, maechler@stat.math.ethz.ch with some basic code from Thomas Lumley.
ecdf
for empirical distribution functions as
special step functions and plot.stepfun
for plotting
step functions.
approxfun
and splinefun
.
y0 <- c(1.,2.,4.,3.)
sfun0 <- stepfun(1:3, y0, f = 0)
sfun.2 <- stepfun(1:3, y0, f = .2)
sfun1 <- stepfun(1:3, y0, f = 1)
sfun1c <- stepfun(1:3, y0, right=TRUE)# hence f=1
sfun0
summary(sfun0)
summary(sfun.2)
## look at the internal structure:
unclass(sfun0)
ls(envir = environment(sfun0))
x0 <- seq(0.5,3.5, by = 0.25)
rbind(x=x0, f.f0 = sfun0(x0), f.f02= sfun.2(x0),
f.f1 = sfun1(x0), f.f1c = sfun1c(x0))
## Identities :
stopifnot(identical(y0[-1], sfun0 (1:3)),# right = FALSE
identical(y0[-4], sfun1c(1:3)))# right = TRUE