Description Usage Arguments Details Value
The function works by first finding the minimal embedding dimension E by either the false nearest neighbours (FNN) method, or by Whitney's embedding theorem (the minimum embedding dimension must be at least the first integer dimension larger than the box counting dimension of the attractor). The minimal embedding dimension found is then used as input for the simplex projection algorithm, which estimates a suitable E by optimizing self-prediciton.
1 2 3 4 5 | optimise_embedding_dim(v, optimise.simplex = T, optimise.FNNdim = T,
optimise.boxcountdim = T, min.embedding.dim = 2, max.embedding.dim = 10,
orbital.lag = NULL, lag.max = ceiling(length(v) * 0.2),
lag.method = "mi", embedding.lag = 1, plot.simplex.projection = F,
return.all = T)
|
v |
Numeric vector containing the series. |
optimise.simplex |
Optimise using simplex projection? |
optimise.FNNdim |
Should false nearest neighbour criteria be applied? |
optimise.boxcountdim |
Should box counting dimension criteria be applied? |
min.embedding.dim |
The minimum embedding dimension to consider. |
max.embedding.dim |
The maximum embedding dimension to consider. |
orbital.lag |
The Theiler window. An orbital lag to avoid temporal correlation. Defaults to NULL, in which case the orbital lag is chosen as the first local minima of the autocorrelation function (lag.method = "acf" or lag.method = "autocorrelationfunction") or the lagged mutual information function (lag.method = "mi" or lag.method = "mutual information"). |
lag.max |
The maximum number of lags for the autocorrelation function (lag.method = "acf" or lag.method = "autocorrelation function") or the mutual information function (lag.method = "mi" or lag.method = "mutual information function"). |
lag.method |
The method to compute the orbital lag (Theiler window), which excludes temporal neighbours to reduce correlation bias. Either use the autocorrelation function (lag.method = "acf" or "autocorrelation") or the lagged mutual information function (lag.method = "mi" or "mutual information"). |
embedding.lag |
The embedding lag. |
plot.simplex.projection |
Plot the results of the simplex projection? |
return.all |
Should all optimisation results be returned? Defaults to TRUE. |
Both pre-estimation methods may be used simultaneously. In this case, the minimal embedding dimension is the maximum of the FNN and the boxcount estimates. The final embedding dimension is then determined by simplex projection over dimensions E:max.E.
If no optimization is done prior to simplex projection, optimization is performed over dimensions min.E:max.E. min.E defaults to 2.
The embedding lag is estimated from the simplex projection routine.
A data frame containing the optimal embeddings.
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