Cross correlation. Internal.
int.crosscor(Z, Y, con, nsim, alternative, test = "permutation",
adjust.n = FALSE, plotit)
vector, matrix or data frame. with variable/s (in matrix or data frame formats, variables in columns).
Vector with the second variable for cross-correlograms construction. if Z has multiple variables, the program will compute the cross correlograms between each and Y.
Connection network.
Number of Monte-Carlo simulations.
The alternative hypothesis. If "auto" is selected (default) the program determines the hypothesis by difference between the median of the simulations and the observed value. Other options are: "two.sided", "greater" and "less". if test == cross, for the first interval (d== 0) the p and CI are computed with cor.test.
Should be adjusted the number of individuals? (warning, this would change variances)
Should be generated a plot of the simulations?