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