## Fast Discrete Fourier Transform

## Usage

    fft(z, inverse = FALSE)
    mvfft(z, inverse = FALSE)

## Arguments

`z` | a real or complex array containing the values to be transformed
`inverse` | if `TRUE`, the unnormalized inverse transform is computed (the inverse has a `+` in the exponent of e, but here, we do not divide by `1/length(x)`).

## Value

When `z` is a vector, the value computed and returned by `fft` is the unnormalized univariate Fourier transform of the sequence of values in `z`. When `z` contains an array, `fft` computes and returns the multivariate (spatial) transform. If `inverse` is `TRUE`, the (unnormalized) inverse Fourier transform is returned, i.e., if `y <- fft(z)`, then `z` is `fft(y, inv=TRUE) / length(y)`.

By contrast, `mvfft` takes a real or complex matrix as argument, and returns a similar shaped matrix, but with each column replaced by its discrete Fourier transform. This is useful for analyzing vector-valued series.

The FFT is fastest when the length of of the series being transformed is highly composite (i.e. has many factors). If this is not the case, the transform may take a long time to compute and will use a large amount of memory.

## References

Singleton, R. C. (1979). Mixed Radix Fast Fourier Transforms, in Programs for Digital Signal Processing, IEEE Digital Signal Processing Committee eds. IEEE Press.

## See Also

`convolve`, `nextn`.

## Examples

    x <- 1:4
    fft(x)
    all(fft(fft(x), inverse = TRUE)/(x*length(x)) == 1+0i)
    ## In general, not exactly, but still:
    x <- rnorm(100)
    all(Mod(fft(fft(x), inverse=TRUE)/(x*length(x)) - 1) < 1e-12)

    plot(fft(c(9:0,0:13, numeric(301))), type = "l")
    periodogram <- function(x) { # simple periodogram
      n <- length(x)
      Mod(fft(unclass(x)))[1:(n%/%2 + 1)]^2 / (2*pi*n)
    }
    data(sunspots)
    plot(10*log10(periodogram(sunspots)), type = "b", col = "blue")

[Package Contents]