CalculateInfDim {RHRV} | R Documentation |
Information dimension of the RR time series
Description
Information dimension of the RR time series
Usage
CalculateInfDim(HRVData,
indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
minEmbeddingDim = NULL, maxEmbeddingDim = NULL, timeLag = NULL,
minFixedMass = 1e-04, maxFixedMass = 0.005, numberFixedMassPoints = 50,
radius = 1, increasingRadiusFactor = 1.05, numberPoints = 500,
theilerWindow = 100, doPlot = TRUE)
EstimateInfDim(HRVData,
indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis),
regressionRange = NULL, useEmbeddings = NULL, doPlot = TRUE)
PlotInfDim(HRVData,
indexNonLinearAnalysis = length(HRVData$NonLinearAnalysis), ...)
Arguments
HRVData |
Data structure that stores the beats register and information related to it |
indexNonLinearAnalysis |
Reference to the data structure that will contain the nonlinear analysis. |
minEmbeddingDim |
Integer denoting the minimum dimension in which we shall embed the time series. |
maxEmbeddingDim |
Integer denoting the maximum dimension in which we shall embed the time series. Thus, we shall estimate the correlation dimension between minEmbeddingDim and maxEmbeddingDim. |
timeLag |
Integer denoting the number of time steps that will be use to construct the Takens' vectors. |
minFixedMass |
Minimum percentage of the total points that the algorithm shall use for the estimation. |
maxFixedMass |
Maximum percentage of the total points that the algorithm shall use for the estimation. |
numberFixedMassPoints |
The number of different fixed mass fractions between minFixedMass and maxFixedMass that the algorithm will use for estimation. |
radius |
Initial radius for searching neighbour points in the phase space. Ideally, it should be small enough so that the fixed mass contained in this radius is slightly greater than the minFixedMass. However, whereas the radius is not too large (so that the performance decreases) the choice is not critical. |
increasingRadiusFactor |
Numeric value. If no enough neighbours are found within radius, the radius is increased by a factor increasingRadiusFactor until succesful. Default: 1.05. |
numberPoints |
Number of reference points that the routine will try to use, saving computation time. |
theilerWindow |
Integer denoting the Theiler window: Two Takens' vectors must be separated by more than theilerWindow time steps in order to be considered neighbours. By using a Theiler window, we exclude temporally correlated vectors from our estimations. |
doPlot |
Logical value. If TRUE (default), a plot of the correlation sum with q=1 is shown |
regressionRange |
Vector with 2 components denoting the range where the function will perform linear regression |
useEmbeddings |
A numeric vector specifying which embedding dimensions should the algorithm use to compute the information dimension. |
... |
Additional plot parameters. |
Details
The information dimension is a particular case of the generalized correlation dimension
when setting the order q = 1. It is possible to demonstrate that the information dimension
D_1
may be defined as:
D_1=lim_{r \rightarrow 0} <\log p(r)>/\log(r)
.
Here, p(r)
is the probability of finding a neighbour in a neighbourhood of size r
and
<> is the mean value. Thus, the information dimension specifies how the average
Shannon information scales with the radius r
.
In order to estimate D_1
, the algorithm looks for the scaling behaviour of the average
radius that contains a given portion (a "fixed-mass") of the total points in the phase space. By performing
a linear regression of \log(p)\;Vs.\;\log(<r>)
(being p
the fixed-mass of the total points), an estimate
of D_1
is obtained. The user should run
the method for different embedding dimensions for checking if D_1
saturates.
The calculations for the information dimension are heavier than those needed for the correlation dimension.
Value
The CalculateCorrDim returns the HRVData structure containing a infDim object storing the results
of the correlation sum (see infDim
) of the RR time series.
The EstimateInfDim function estimates the information dimension of the RR time series by averaging the slopes of the correlation sums with q=1. The slopes are determined by performing a linear regression over the radius' range specified in regressionRange.If doPlot is TRUE, a graphic of the regression over the data is shown. The results are returned into the HRVData structure, under the NonLinearAnalysis list.
PlotInfDim shows a graphics of the correlation sum with q=1.
Note
In order to run EstimateInfDim, it is necessary to have performed the correlation sum before with ComputeInfDim.
References
H. Kantz and T. Schreiber: Nonlinear Time series Analysis (Cambridge university press)