Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_statfun_correlationT”.

```  FT_STATFUN_CORRELATIONT calculates correlation coefficient T-statistics on the
biological data in dat (the dependent variable), using the information on the
independent variable (predictor) in design. The correlation coefficients are stored
in the rho field of output s.

Use this function by calling one of the high-level statistics functions as
[stat] = ft_timelockstatistics(cfg, timelock1, timelock2, ...)
[stat] = ft_freqstatistics(cfg, freq1, freq2, ...)
[stat] = ft_sourcestatistics(cfg, source1, source2, ...)
with the following configuration option
cfg.statistic = 'ft_statfun_correlationT'

Configuration options
cfg.computestat    = 'yes' or 'no', calculate the statistic (default='yes')
cfg.computecritval = 'yes' or 'no', calculate the critical values of the test statistics (default='no')
cfg.computeprob    = 'yes' or 'no', calculate the p-values (default='no')
The following options are relevant if cfg.computecritval='yes' and/or
cfg.computeprob='yes'.
cfg.alpha = critical alpha-level of the statistical test (default=0.05)
cfg.tail  = -1, 0, or 1, left, two-sided, or right (default=1)
cfg.tail in combination with cfg.computecritval='yes'
determines whether the critical value is computed at
quantile cfg.alpha (with cfg.tail=-1), at quantiles
cfg.alpha/2 and (1-cfg.alpha/2) (with cfg.tail=0), or at
quantile (1-cfg.alpha) (with cfg.tail=1).
cfg.type  = 'Pearson' to compute Pearson's correlation (default), see 'help corr' for other options.

Design specification
cfg.ivar  = row number of the design that contains the independent variable (default=1)