basic.probes: Some probes for partially-observed Markov processes

Description

Several simple and configurable probes are provided in the package. These can be used directly and as examples for building 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.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)

Arguments

Value

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.

Details

Each of these functions is relatively simple. See the source code for a complete understanding of what each does. [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References

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.