dc_win | R Documentation |
Calculates Dynamic Complexity, a complexity index for short and coarse grained time series.
dc_win(
df,
win = 0,
scale_min,
scale_max,
doPlot = FALSE,
doPlotF = FALSE,
doPlotD = FALSE,
returnFandD = FALSE,
useVarNames = TRUE,
colOrder = TRUE,
useTimeVector = NA,
timeStamp = "31-01-1999",
markID = NA,
markIDcolour = "grey",
markIDlabel = "Time points of interest marked grey",
markIDalpha = 0.5,
NAdates = 1:(win - 1),
trimFirstWin = TRUE
)
df |
A data frame containing multivariate time series data from 1 person. Rows should indicate time, columns should indicate the time series variables. All time series in |
win |
Size of window in which to calculate Dynamic Complexity. If |
scale_min |
The theoretical minimum value of the scale. Used to calculate expected values, so it is important to set this to the correct value. |
scale_max |
The theoretical maximum value of the scale. Used to calculate expected values, so it is important to set this to the correct value. |
doPlot |
If |
doPlotF |
If |
doPlotD |
If |
returnFandD |
Returns a list object containing the dynamic complexity series as well as the |
useVarNames |
Use the column names of |
colOrder |
If |
useTimeVector |
Parameter used for plotting. A vector of length |
timeStamp |
If |
markID |
Numeric vector of integers in the range |
markIDcolour |
Colour of time point markers (default = |
markIDlabel |
Label added to subtitle explaining time point markers (default = |
markIDalpha |
Alpha of time point marker colour (default = |
NAdates |
Should some dates be considered |
trimFirstWin |
Display the first empty window ( |
plotMeanCD |
Plot the mean Dynamic Complexity at the top row of the resonance diagram? (default = |
If doPlot = TRUE
, a list object containing a data frame of Dynamic Complexity values and a ggplot2
object of the dynamic complexity resonance diagram. If doPlot = FALSE
the data frame with Dynamic Complexity series is returned.
Merlijn Olthof
Fred Hasselman
Haken H, & Schiepek G. (2006). Synergetik in der Psychologie. Selbstorganisation verstehen und gestalten. Hogrefe, Göttingen.
Schiepek, G. (2003). A Dynamic Systems Approach to Clinical Case Formulation. European Journal of Psychological Assessment, 19, 175-184. https://doi.org/10.1027//1015-5759.19.3.175
Schiepek, G., & Strunk, G. (2010). The identification of critical fluctuations and phase transitions in short term and coarse-grained time series-a method for the real-time monitoring of human change processes. Biological cybernetics, 102(3), 197-207. https://doi.org/10.1007/s00422-009-0362-1
Other Dynamic Complexity functions:
dc_ccp()
,
dc_d()
,
dc_f()
,
plotDC_ccp()
,
plotDC_lvl()
,
plotDC_res()
# Dynamic Complexity analysis on part of the coloured noise dataset:
data(ColouredNoise)
# Make unit scale
df <- data.frame(Brownianish = elascer(rowSums(ColouredNoise[,1:6])), pinkish = elascer(rowSums(ColouredNoise[,7:12])), whiteish = elascer(rowSums(ColouredNoise[,12:17])))
dc_win(df = df, win = 56, scale_min = 0, scale_max = 1, doPlot = TRUE)
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