FT_STATFUN_DEPSAMPLESREGRT

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

  FT_STATFUN_DEPSAMPLESREGRT calculates dependent samples regression T-statistic 
  on the biological data in dat (the dependent variable), using the information on 
  the independent variable (ivar) in design.
 
  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_depsamplesregrT'
  see FT_TIMELOCKSTATISTICS, FT_FREQSTATISTICS or FT_SOURCESTATISTICS for details.
 
  For low-level use, the external interface of this function has to be
    [s,cfg] = ft_statfun_depsamplesregrT(cfg, dat, design);
  where
    dat    contains the biological data, Nsamples x Nreplications
    design contains the independent variable (ivar) and the unit-of-observation (uvar) 
           factor,  Nreplications x Nvar
 
  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).
 
  Design specification
    cfg.ivar  = row number of the design that contains the independent variable.
    cfg.uvar  = row number of design that contains the labels of the units-of-observation (subjects or trials)
                (default=2). The labels are assumed to be integers ranging from 1 to 
                the number of units-of-observation.

reference/ft_statfun_depsamplesregrt.txt · Last modified: 2016/08/17 23:02 (external edit)

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