A package to estimate local statistical complexity (LSC), a measure for automated pattern discovery in spatio-temporal data using optimal predictors (see References).
This package is very tightly linked to the LICORS
package, which can be used to estimate these optimal
predictors and state space from data. The LSC
builds on a known or estimated state space; most
estimation is handled by LICORS (see
There are two ways the state space can be represented: either as a unique state label or as a vector of weights. These two are the principal arguments in the functions of this package:
an N \times K matrix, where N are the samples and K are the states. That is, each row contains a vector of length K that adds up to one (the mixture weights).
a vector of length N with entry i being the label k = 1, …, K of PLC i
This is an early release: some function names and arguments might/will (slightly) change in the future, so regularly check with new package updates.
Georg M. Goerg [email protected]
Shalizi, C. R., R. Haslinger, J.-B. Rouquier, K. L. Klinkner, and C. Moore (2006). “Automatic filters for the detection of coherent structure in spatiotemporal systems.” Physical Review E 73, 036104
Shalizi, C. R., K. L. Klinkner, and R. Haslinger (2004a). “Quantifying self-organization with optimal predictors.” Physical Review Letters 93, 118701.
The main functions in this package are
states2LSC to estimate LSC from the state
LICORS2LSC which is a
wrapper for estimating LSC from a
Since pattern discovery without visualization
is only of very limited use, the
function shows informative plots for (1+1)D and
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## known predictive state space with a state-vector data(contCA00) ll <- states2LSC(states = contCA00$predictive_states - min(contCA00$predictive_states) + 1) image2(ll, density = TRUE, legend = FALSE) # An example using estimates from LICORS ## Not run: example(LICORS) # this will give an object 'mod' of class 'LICORS' image2(LICORS2LSC(mod)) ## End(Not run)
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