CCM

Convergent cross mapping between columns : target; and, reverse mapping between target : columns.

Python :

CCM(pathIn='./', dataFile='', dataFrame=None, pathOut='./',
predictFile='', E=0, Tp=0, knn=0, tau=-1, exclusionRadius=0,
columns='', target='', libSizes='', sample=0, random=True,
replacement=False, seed=0, embedded=False, includeData=False,
parameterList=False, verbose=False, showPlot=False, noTime=False)

R :

CCM(pathIn="./", dataFile="", dataFrame=NULL, E=0, 
Tp=0, knn=0, tau=-1, exclusionRadius=0,
columns="", target="", libSizes="", sample=0,
random=TRUE, seed=0, embedded=FALSE, includeData=FALSE,
parameterList=FALSE, verbose=FALSE, showPlot=FALSE, noTime=FALSE)

Parameter	Type	Default	Purpose
pathIn	string	"./"	Input data file path
dataFile	string	""	Data file name
dataFrame	pyEDM: pandas DataFrame rEDM: data.frame	None	Input DataFrame
pathOut	string	"./"	Output file path
predictFile	string	""	Prediction output file
E	int	0	Data dimension
Tp	int	0	Prediction Interval
knn	int	0	Number nearest neighbors (if 0 then set as E+1)
tau	int	-1	Embedding time shift (time series rows)
exclusionRadius	int	0	Prediction vector exclusion radius
columns	string or []	""	Column name(s) for library
target	string	""	Prediction target column name
libSizes	string	""	CCM library sizes
sample	int	0	CCM number of random samples
random	bool	True	CCM use random samples?
replacement	bool	False	CCM random sample with replacement?
seed	unsigned	0	RNG seed, 0 = random seed
embedded	bool	False	Is data an embedding? If False, embed to E
includeData	bool	False	Include output statistics on all predictions
parameterList	bool	False	Include parameter dictionary in return
verbose	bool	False	Echo messages
showPlot	bool	False	Plot results (pyEDM, rEDM)
noTime	bool	False	Do not require first data column of time or index

Refer to the parameters table for general parameter definitions.

Notes :
Normally, one column and one target are specified. The column time series is time-delay embedded to dimension E, then cross mapped with the target time series. In a separate thread, the target time series is embedded to E and cross mapped against the column acting as the "target".

If there are multiple columns and embedded is false, each column is time-delay embedded to dimension E creating an N-columns * E dimensional "mixed" embedding. If embedded is true, no time-delay embedding is done, creating a multivariate embedding of the speficied columns. The same logic applies if multiple target are specified for the "reverse" mapping. If embedded is false, each target is time-delay embedded to dimension E creating an N-target * E dimensional "mixed" embedding cross mapped to only the first column as the cross map target. If embedded is true, no time-delay embedding is done, creating a multivariate embedding of the specified target(s).

libSizes specifies a string of whitespace or comma separated library sizes. If the string has 3 values, and, if the third value is less than the second value, the three values are interpreted as a sequence generator specifying "start stop increment" row values, i.e. "10 80 10" will evaluate library sizes from 10 to 80 in increments of 10.

If random is true, sample observations are randomly selected from the subset of each library size.

If random is false, sample is ignored and contiguous library rows up to the current library size are used. Note this is not convergent cross mapping.

If seed=0 , then a random seed is generated for the random number generator. Otherwise, seed is used to initialise the random number generator.

Returns :
If includeData is False: returns DataFrame with 3 columns.
The first column is LibSize, the second and third columns are Pearson correlation coefficients for column : target and target : column cross mapping.

If includeData is True: returns a list with the above DataFrame and a DataFrame of all Simplex projection statistics. If parameterList = True, a dictionary of parameters is added.