Calculate the Multi-Scale (Sample) Entropy of a time series.
inf_MSE(
y,
summaryFunction = c("mean", "median", "min", "max")[1],
retainLength = FALSE,
m = 2,
r = 0.2,
D = NA,
fs = NULL,
transformBefore = TRUE,
removeTrend = c("no", "poly", "adaptive", "bridge")[1],
polyOrder = 1,
standardise = c("none", "mean.sd", "median.mad")[1],
adjustSumOrder = FALSE,
scaleMin = 1,
scaleMax = floor(NROW(y)/10),
scaleS = NA,
overlap = 0,
dataMin = 4,
relativeEntropy = FALSE,
doPlot = FALSE,
returnPlot = FALSE,
returnPLAW = FALSE,
returnInfo = FALSE,
silent = FALSE,
noTitle = FALSE,
tsName = "y"
)A numeric vector
How should the data be summarized in the bins? Can be "mean", "median", "min", "max", or, "maxFreq". Value "maxFreq" is for categorical data and will pick the most frequently occurring category within the bin (default = "mean")
Return only the bin values (FALSE), or retain the length of the original series? (default = TRUE)
The size of the window in which tho evaluate whether a pattern repeats (default = 2)
A factor that will determine the threshold for similarity of values, calculated as r x D (default = 0.2)
Commonly the standard deviation of the time series, the similarity threshold will be calculated as r x D. Note that if the series is detrended and/or standardised and D = NA the standard deviation will be calculated after the transformations (default = NA)
Sample rate
Detrend/standardise before coarse graining. If set to FALSE, each coarsegrained series will be detrended/standardised separately (default = TRUE)
Method to use for global detrending (default = "poly")
Order of global polynomial trend to remove if removeTrend = "poly". If removeTrend = "adaptive" polynomials 1 to polyOrder will be evaluated and the best fitting curve (R squared) will be removed (default = 1)
Standardise the series using ts_standardise() with adjustN = FALSE (default = "mean.sd")
Adjust the time series (summation or difference), based on the global scaling exponent, see e.g. Ihlen (2012) (default = FALSE)
Minimum scale (in data points) to use for log-log regression (default = 4)
Maximum scale (in data points) to use for log-log regression. This value will be ignored if dataMin is not NA, in which case bins of size < dataMin will be removed (default = stats::nextn(floor(NROW(y)/4), factors = 2))
If not NA, it should be a numeric vector listing the scales on which to evaluate the detrended fluctuations. Arguments scaleMax, scaleMin, scaleResolution and dataMin will be ignored (default = NA)
A number in [0 ... 1] representing the amount of 'bin overlap' when calculating the fluctuation. This reduces impact of arbitrary time series begin and end points. If length(y) = 1024 and overlap is .5, a scale of 4 will be considered a sliding window of size 4 with step-size floor(.5 * 4) = 2, so for scale 128 step-size will be 64 (default = NA)
The relative entropy, SampEn / (-1 * log(1/length(y))) will be returned (default = FALSE)
Output the log-log scale versus fluctuation plot with linear fit by calling function plotFD_loglog() (default = TRUE)
Return ggplot2 object (default = FALSE)
Return the power law data (default = FALSE)
Return all the data used in SDA (default = FALSE)
Silent-ish mode (default = FALSE)
Do not generate a title (only the subtitle) (default = FALSE)
Name of y added as a subtitle to the plot (default = "y")
MSE
The transformation settings (detrending and/or normalisation), transformBefore will determine the value of the product r x D if D = NA.
Other Information based complexity measures:
inf_SampEn()
y <- rnorm(100)
out <- inf_MSE(y, r=.5, scaleMin=1, scaleMax=10, returnInfo = TRUE, doPlot = FALSE)
#>
#> ~~~o~~o~~casnet~~o~~o~~~
#>
#> Multi Scale Entropy
#>
#> Full range (n = 10)
#> Slope = -0.14
#>
#> Fit range (n = 7)
#> Slope = -0.12
#>
#> ~~~o~~o~~casnet~~o~~o~~~