basic-probes: Some useful probes for partially-observed Markov processes
In pomp: Statistical Inference for Partially Observed Markov Processes

Description Usage Arguments Details 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.

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.marginal(var, ref, order = 3, diff = 1, transform = identity)
probe.nlar(var, lags, powers, transform = identity)
probe.acf(var, lags, type = c("covariance", "correlation"),
          transform = identity)
probe.ccf(vars, lags, type = c("covariance", "correlation"),
          transform = identity)
probe.period(var, kernel.width, transform = identity)
probe.quantile(var, prob, transform = identity)

`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`.
`prob`	a single probability; the quantile to compute: see `quantile`.
`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.
`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.
`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.
`...`	Additional arguments to be passed through to the probe computation.

Each of these functions is relatively simple. See the source code for a complete understanding of what each does.

probe.mean, probe.median, probe.var, probe.sd: return functions that compute the mean, median, variance, and standard deviation of variable var, respectively.
probe.period: returns a function that estimates the period of the Fourier component of the var series with largest power.
probe.marginal: returns a function that regresses the marginal distribution of variable var against the reference distribution ref. If diff>0, the data and the reference distribution are first differenced diff times and centered. Polynomial regression of order order is used. This probe returns order regression coefficients (the intercept is zero).
probe.nlar: returns a function that fit a nonlinear (polynomial) autoregressive model to the univariate series (variable var). Specifically, a model of the form y[t] = ∑ beta[k] y[t-tau[k]]^p[k]+e[t] will be fit, where tau[k] are the lags and p[k] are the powers. The data are first centered. This function returns the regression coefficients, beta[k].
probe.acf: returns a function that, if type=="covariance", computes the autocovariance of variable var at lags lags; if type=="correlation", computes the autocorrelation of variable var at lags lags.
probe.ccf: returns a function that, if type=="covariance", computes the cross covariance of the two variables named in vars at lags lags; if type=="correlation", computes the cross correlation.
probe.quantile: returns a function that estimates the prob-th quantile of variable var.

A call to any one of these functions returns a probe function, suitable for use in probe or probe.match. 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 (d.reuman at imperial dot ac dot uk)

Aaron A. King (kingaa at umich dot edu)

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

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

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pomp documentation built on May 2, 2019, 4:09 p.m.