stepfun {stats} | R Documentation |

Given the vectors *(x[1],..., x[n])* and
*(y[0],y[1],..., 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,...,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*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 `stepfun()` . For the other functions, `x` is
as `object` below. |

`y` |
numeric vector one longer than `x` , giving the heights of
the function values between the x values. |

`f` |
a number between 0 and 1, indicating how interpolation outside
the given x values should happen. See `approxfun` . |

`ties` |
Handling of tied `x` values. Either a function or
the string `"ordered"` . See `approxfun` . |

`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 `"stepfun"` . |

`digits` |
number of significant digits to use, see `print` . |

`...` |
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 `== "constant"` , from
`approxfun(.)` ). |

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.

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

[Package *stats* version 2.2.1 Index]