# basic_probes: Useful probes for partially-observed Markov processes In pomp: Statistical Inference for Partially Observed Markov Processes

 basic probes 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 `mean`). `transform` transformation to be applied to the data before the probe is computed. `na.rm` if `TRUE`, remove all NA observations prior to computing the probe. `kernel.width` width of modified Daniell smoothing kernel to be used in power-spectrum computation: see `kernel`. `probs` the quantile or quantiles to compute: see `quantile`. `...` additional arguments passed to the underlying algorithms. `lags` In `probe.ccf`, a vector of lags between time series. Positive lags correspond to `x` advanced relative to `y`; negative lags, to the reverse. In `probe.nlar`, a vector of lags present in the nonlinear autoregressive model that will be fit to the actual and simulated data. See Details, below, for a precise description. `type` Compute autocorrelation or autocovariance? `ref` empirical reference distribution. Simulated data will be regressed against the values of `ref`, sorted and, optionally, differenced. The resulting regression coefficients capture information about the shape of the marginal distribution. A good choice for `ref` is the data itself. `order` order of polynomial regression. `diff` order of differencing to perform. `powers` the powers of each term (corresponding to `lags`) in the the nonlinear autoregressive model that will be fit to the actual and simulated data. See Details, below, for a precise description.

### 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

\Kendall

1999

\Wood

2010

More on methods based on summary statistics: `approximate Bayesian computation`, `nonlinear forecasting`, `probe matching`, `probe()`, `spectrum matching`, `spect()`