corr_sig (pyleoclim.utils.correlation.corr_sig)¶

pyleoclim.utils.correlation.corr_sig(y1, y2, nsim=1000, method='isospectral', alpha=0.05)[source]¶

Estimates the Pearson’s correlation and associated significance between two non IID time series

The significance of the correlation is assessed using one of the following methods:

‘ttest’: T-test adjusted for effective sample size.
This is a parametric test (data are Gaussian and identically distributed) with a rather ad-hoc adjustment. It is instantaneous but makes a lot of assumptions about the data, many of which may not be met.
‘isopersistent’: AR(1) modeling of x and y.
This is a parametric test as well (series follow an AR(1) model) but solves the issue by direct simulation.
‘isospectral’: phase randomization of original inputs. (default)
This is a non-parametric method, assuming only wide-sense stationarity.

For 2 and 3, computational requirements scale with nsim. When possible, nsim should be at least 1000.

Parameters

y1 (array) – vector of (real) numbers of same length as y2, no NaNs allowed
y2 (array) – vector of (real) numbers of same length as y1, no NaNs allowed
nsim (int) – the number of simulations [default: 1000]
method ({'ttest','isopersistent','isospectral' (default)}) – method for significance testing
alpha (float) – significance level for critical value estimation [default: 0.05]

Returns

res – the result dictionary, containing

rfloat
correlation coefficient
pfloat
the p-value
signifbool
true if significant; false otherwise Note that signif = True if and only if p <= alpha.

Return type

dict