basic_probes {pomp} | R Documentation |

## Useful probes for partially-observed Markov processes

### Description

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

### Usage

```
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)
```

### Arguments

`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 |

### Value

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.

### Author(s)

Daniel C. Reuman, Aaron A. King

### References

B.E. Kendall, C.J. Briggs, W.W. Murdoch, P. Turchin, S.P. Ellner, E. McCauley, R.M. Nisbet, and S.N. Wood. Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches. *Ecology* **80**, 1789–1805, 1999. doi:10.2307/176658.

S. N. Wood Statistical inference for noisy nonlinear ecological dynamic systems. *Nature* **466**, 1102–1104, 2010. doi:10.1038/nature09319.

### See Also

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

,
`nlf`

,
`probe()`

,
`probe_match`

,
`spect()`

,
`spect_match`

*pomp*version 5.11.0.0 Index]