# surrogate

##### Generate Surrogate Data and Statistics

Generates `ns`

surrogate samples from the original data `x`

and computes the standard error and the bias of `statistic`

as in
a bootstrap setup, if `statistic`

is given.

- Keywords
- ts

##### Usage

```
surrogate(x, ns = 1, fft = FALSE, amplitude = FALSE,
statistic = NULL, ...)
```

##### Arguments

- x
- a numeric vector or time series.
- ns
- the number of surrogate series to compute.
- fft
- a logical indicating whether phase randomized surrogate data is generated.
- amplitude
- a logical indicating whether amplitude-adjusted surrogate data is computed.
- statistic
- a function which when applied to a time series returns a vector containing the statistic(s) of interest.
- ...
- Additional arguments for
`statistic`

which are passed unchanged each time it is called.

##### Details

If `fft`

is `FALSE`

, then `x`

is mixed in temporal
order, so that all temporal dependencies are eliminated, but the
histogram of the original data is preserved. If `fft`

is
`TRUE`

, then surrogate data with the same spectrum as `x`

is
computed by randomizing the phases of the Fourier coefficients of
`x`

. If in addition `amplitude`

is `TRUE`

, then also
the amplitude distribution of the original series is preserved.

Note, that the interpretation of the computed standard error and bias is different than in a bootstrap setup.

To compute the phase randomized surrogate and the amplitude adjusted data algorithm 1 and 2 from Theiler et al. (1992), pp. 183, 184 are used. Missing values are not allowed.

##### Value

- If
`statistic`

is`NULL`

, then it returns a matrix or time series with`ns`

columns and`length(x)`

rows containing the surrogate data. Each column contains one surrogate sample.If

`statistic`

is given, then a list of class`"resample.statistic"`

with the following elements is returned: statistic the results of applying `statistic`

to each of the simulated time series.orig.statistic the results of applying `statistic`

to the original series.bias the bias of the statistics computed as in a bootstrap setup. se the standard error of the statistics computed as in a bootstrap setup. call the original call of `surrogate`

.

##### References

J. Theiler, B. Galdrikian, A. Longtin, S. Eubank, and J. D. Farmer
(1992): Using Surrogate Data to Detect
Nonlinearity in Time Series, in *Nonlinear Modelling and
Forecasting*, Eds. M. Casdagli and S. Eubank, Santa Fe Institute,
Addison Wesley, 163--188.

##### See Also

##### Examples

```
x <- 1:10 # Simple example
surrogate(x)
n <- 500 # Generate AR(1) process
e <- rnorm(n)
x <- double(n)
x[1] <- rnorm(1)
for(i in 2:n) {
x[i] <- 0.4 * x[i-1] + e[i]
}
x <- ts(x)
theta <- function(x) # Autocorrelations up to lag 10
return(acf(x, plot=FALSE)$acf[2:11])
surrogate(x, ns=50, fft=TRUE, statistic=theta)
```

*Documentation reproduced from package tseries, version 0.10-10, License: GPL-2*