ccm_legacy: Convergent cross mapping using simplex projection

ccmR Documentation

Convergent cross mapping using simplex projection


ccm uses time delay embedding on one time series to generate an attractor reconstruction, and then applies the simplex projection algorithm to estimate concurrent values of another time series. This method is typically applied, varying the library sizes, to determine if one time series contains the necessary dynamic information to recover the influence of another, causal variable.


ccm(block, lib = NULL, pred = NULL, norm = 2, E = 1, tau = -1, 
    tp = 0, num_neighbors = "e+1", lib_sizes = c(10, 75, 5), 
    random_libs = TRUE, num_samples = 100, replace = FALSE, lib_column = 1, 
    target_column = 2, first_column_time = FALSE, RNGseed = NULL, 
    exclusion_radius = NULL, epsilon = NULL, stats_only = TRUE, 
    silent = TRUE)



either a vector to be used as the time series, or a data.frame or matrix where each column is a time series


a 2-column matrix, data.frame, 2-element vector or string of row indice pairs, where each pair specifies the first and last *rows* of the time series to create the library. If not specified, all available rows are used


(same format as lib), but specifying the sections of the time series to forecast. If not specified, set equal to lib


the distance measure to use. see 'Details'


the embedding dimensions to use for time delay embedding


the time-delay offset to use for time delay embedding


the prediction horizon (how far ahead to forecast)


the number of nearest neighbors to use. Note that the default value will change depending on the method selected. (any of "e+1", "E+1", "e + 1", "E + 1" will set this parameter to E+1 for each run


three integers specifying the start, stop and increment index of library sizes


indicates whether to use randomly sampled libs


is the number of random samples at each lib size (this parameter is ignored if random_libs is FALSE)


indicates whether to sample vectors with replacement


name (index) of the column to cross map from


name (index) of the column to forecast


indicates whether the first column of the given block is a time column


will set a seed for the random number generator, enabling reproducible runs of ccm with randomly generated libraries


excludes vectors from the search space of nearest neighbors if their *time index* is within exclusion_radius (NULL turns this option off)


not implemented


specify whether to output just the forecast statistics or the raw predictions for each run


prevents warning messages from being printed to the R console


ccm runs both forward and reverse cross maps in seperate threads. Results are returned for both mappings. The default parameters are set so that passing a matrix as the only argument will use E = 1 (embedding dimension), and leave-one-out cross-validation over the whole time series to compute cross-mapping from the first column to the second column, letting the library size vary from 10 to 75 in increments of 5.

norm = 2 (only option currently available) uses the "L2 norm", Euclidean distance:

distance(a, b) := √(∑(a_i - b_i)^2)


If stats_only = TRUE: a data.frame with forecast statistics for both the forward and reverse mappings:

LibSize library length (number of vectors)
x:y cross mapped correlation coefficient between observations x and predictions y
y:x cross mapped correlation coefficient between observations y and predictions x
E embedding dimension
tau time delay offset
tp forecast interval
nn number nearest neighbors

If stats_only = FALSE: a named list with the following items: settings:

LibMeans data.frame with the mean bidirectional forecast statistics
CCM1_PredictStat data.frame with forward mapped prediction statistics for each prediction of the ensemble
CCM1_Predictions list of prediction result data.frame each forward mapped prediction of the ensemble
CCM2_PredictStat data.frame with reverse mapped prediction statistics for each prediction of the ensemble
CCM2_Predictions list of prediction result data.frame each reverse mapped prediction of the ensemble

CCM1_PredictStat and CCM2_PredictStat data.frames have columns:

N prediction number
E embedding dimension
nn number of nearest neighbors
tau embedding time delay offset
LibSize library size
rho correlation coefficient
RMSE root mean square error
MAE maximum absolute error
lib column name of the library vector
target column name of the target vector


anchovy_xmap_sst <- ccm(sardine_anchovy_sst, E = 3, 
  lib_column = "anchovy", target_column = "np_sst", 
  lib_sizes = c(10, 75, 5), num_samples = 100)

rEDM documentation built on Aug. 6, 2022, 5:08 p.m.