Description Usage Arguments Details Value Notes See Also Examples

View source: R/iccf_functions.R

`iccf_core`

returns the basic interpolated correlation coefficients.

1 |

`t.1, x.1` |
time and value for time series 1 |

`t.2, x.2` |
time and value for time series 2 |

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

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

`cov` |
(logical) if |

The main loop for the ICCF. In this part we take time series 1, `x.1`

at
`t.1`

, pair them with values from time series 2, `x.2`

at
`t.1-tau[i]`

produce by linearly interpolating between the nearest
values of `x.2`

. At a given `tau[i]`

we sum the product of the
paired `x.1`

and `x.2`

values ```
r[i] = (1/n) * sum(x.1 * x.2) /
(sd.1 * sd.2)
```

In the simplest case `n`

, `sd.1`

and `sd.2`

are
constant and are the number of pairs at `lag=0`

and the total
`sqrt(var)`

of each time series. If `local.est = TRUE`

then
`n`

, `sd.1`

and `sd.2`

are evaluated 'locally' i.e. they are
vary for each lag `tau[i]`

. In this case they are the number of good
pairs at lag `tau[i]`

, and the `sqrt(vars)`

of just the `x.1`

and `x.2`

data points involved. We assume `x.1`

and `x.2`

have
zero sample mean.

A list with components

`r` |
(array) A one dimensional array containing the correlation coefficients at each lag. |

`n` |
(array) A one dimensional array containing the number of pairs of points used at each lag. |

We assume that the input data `x.1`

and `x.2`

have been
mean-subtracted.

1 2 3 4 5 6 7 8 9 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.