R: Rational Interpolation

ratinterp {pracma} | R Documentation

## Rational Interpolation

### Description

Burlisch-Stoer rational interpolation.

### Usage

    ratinterp(x, y, xs = x)

### Arguments

`x` | numeric vector; points on the x-axis; needs to be sorted; at least three points required.
`y` | numeric vector; values of the assumed underlying function; `x` and `y` must be of the same length.
`xs` | numeric vector; points at which to compute the interpolation; all points must lie between `min(x)` and `max(x)`.

### Details

The Burlisch-Stoer approach to rational interpolation is a recursive procedure (similar to the Newton form of polynomial interpolation) that produces a “diagonal” rational function, that is the degree of the numerator is either the same or one less than the degree of the denominator.

Polynomial interpolation will have difficulties if some kind of singularity exists in the neighborhood, even if the pole occurs in the complex plane. For instance, Runge's function has a pole at z = 0.2 i, quite close to the interval [-1, 1].

### Value

Numeric vector representing values at points `xs`.

### Note

The algorithm does not yield a simple algebraic expression for the rational function found.

### References

Stoer, J., and R. Bulirsch (2002). Introduction to Numerical Analysis. Third Edition, Springer-Verlag, New York.

Fausett, L. V. (2008). Applied Numerical Analysis Using Matlab. Second Edition, Pearson Education.

### See Also

`rationalfit`, `pade`

### Examples

    ## Rational interpolation of Runge's function
    x <- c(-1, -0.5, 0, 0.5, 1.0)
    y <- runge(x)
    xs <- linspace(-1, 1)
    ys <- runge(xs)
    yy <- ratinterp(x, y, xs)  # returns exactly the Runge function

    ## Not run:
    plot(xs, ys, type="l", col="blue", lty = 2, lwd = 3)
    points(x, y)
    yy <- ratinterp(x, y, xs)
    lines(xs, yy, col="red")
    grid()
    ## End(Not run)

[Package pracma version 2.0.4 Index]