Description Usage Arguments Value Notes See Also Examples

View source: R/dcf_functions.R

`dcf`

returns the Discrete Correlation Function estimates.

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`ts.1` |
(array or dataframe) data for time series 1 and 2. |

`ts.2` |
(array or dataframe) data for time series 1 and 2. |

`tau` |
(vector) list of lags at which to compute the CCF. |

`min.pts` |
(integer) each DCF bin must contain at least |

`local.est` |
(logical) use 'local' (not 'global') means and variances? |

`zero.clip` |
(logical) remove pairs of points with exactly zero lag? |

`use.errors` |
(logical) if |

`cov` |
(logical) if |

`chatter` |
(integer) set the level of feedback. |

A data frame containing columns:

`tau` |
the centre of the lag bins (vector) |

`ccf` |
the correlation coefficent in each lag bin |

`n` |
the number of data point pairs included in each lag bin |

Input are two time series (data frames with columns: `t, y, dy`

[optional]) Output is the correlation coefficient `ccf[i]`

in different
lag bins `tau[i]`

.

In what follows we refer to the `t, y`

values of time series 1
(`ts.1`

) as `t.1, y.1`

, and similarly for time series 2.

We first subtract the mean values from `y.1`

and `y.2`

. Then,
within the `i`

th lag bin (`tau[i] - dtau/2`

, ```
tau[i] +
dtau/2
```

) we collect all pairs (`y.1, y.2`

) of data for which ```
t.1 -
t.2
```

falls within the lag bin. Using these pairs of data we compute the sum
of their product and normalise it:

`cov[i] = (1/n) * sum_{j,k=1..n} (y.1[j] * y.2[k])`

Here, `j, k`

are index arrays of length `n`

that specify the points
of time series 1 and 2 (respectively) which pair-up within lag bin `i`

.
This gives a covariance. If `cov = TRUE`

we keep these values. Otherwise
(default: `cov = FALSE`

) we normalise by the product of the standard
deviations of `y.1`

and `y.2`

.

The number of output lags `tau`

is `2*lag.bins+1`

, and the lags are
centred on zero. So they run from `-max.lag`

to `+max.lag`

. Any lag
bins containing fewer than min.pts pairs of points will have `ccf = NA`

.

If 'errors' are suppled for either or both time series (dx.1, dx.2)
and `use.errors = TRUE`

then the variance used
in the denominator terms of correlation coefficient calculation will be
the 'excess variance', i.e. the total sample variance minus the
mean square error. If dy.1 and/or dy.2 are a single number, this is
assumed to be the same error for each data point. If `use.errors = FALSE`

(default) then the usual sample variance will be used.

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