Description Usage Arguments Value Author(s) References See Also

Several simple and configurable probes are provided with in the package. These can be used directly and as templates for custom probes.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
probe.mean(var, trim = 0, transform = identity, na.rm = TRUE)
probe.median(var, na.rm = TRUE)
probe.var(var, transform = identity, na.rm = TRUE)
probe.sd(var, transform = identity, na.rm = TRUE)
probe.period(var, kernel.width, transform = identity)
probe.quantile(var, probs, ...)
probe.acf(
var,
lags,
type = c("covariance", "correlation"),
transform = identity
)
probe.ccf(
vars,
lags,
type = c("covariance", "correlation"),
transform = identity
)
probe.marginal(var, ref, order = 3, diff = 1, transform = identity)
probe.nlar(var, lags, powers, transform = identity)
``` |

`var, vars` |
character; the name(s) of the observed variable(s). |

`trim` |
the fraction of observations to be trimmed (see |

`transform` |
transformation to be applied to the data before the probe is computed. |

`na.rm` |
if |

`kernel.width` |
width of modified Daniell smoothing kernel to be used
in power-spectrum computation: see |

`probs` |
the quantile or quantiles to compute: see |

`...` |
additional arguments passed to the underlying algorithms. |

`lags` |
In In |

`type` |
Compute autocorrelation or autocovariance? |

`ref` |
empirical reference distribution. Simulated data will be
regressed against the values of |

`order` |
order of polynomial regression. |

`diff` |
order of differencing to perform. |

`powers` |
the powers of each term (corresponding to |

A call to any one of these functions returns a probe function,
suitable for use in `probe`

or `probe_objfun`

. That
is, the function returned by each of these takes a data array (such as
comes from a call to `obs`

) as input and returns a single
numerical value.

Daniel C. Reuman, Aaron A. King

1999

\Wood2010

More on pomp methods based on summary statistics:
`abc()`

,
`probe_matching`

,
`probe()`

,
`spect()`

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