stepfun {stats} R Documentation

## Step Function Class

### Description

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.

### Usage

```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, ...)
```

### Arguments

 `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).

### Value

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)`.

### Author(s)

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`.

### Examples

```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 2.5.0 Index]