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

  FT_STATFUN_DEPSAMPLESFMULTIVARIATE calculates the MANOVA dependent samples 
  F-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 = 'depsamplesF'
  For low-level use, the external interface of this function has to be
    [s,cfg] = statfun_depsamplesF(cfg, dat, design);
    dat    contains the biological data, Nsamples x Nreplications
    design contains the independent variable (ivar) and the unit-of-observation (uvar) 
           factor, Nfac x Nreplications
  Configuration options
    cfg.contrastcoefs  = matrix of contrast coefficients determining the
                         effect being tested. The number of columns of this
                         matrix has to be equal to the number of conditions. 
                         The default is a matrix that specifies the
                         main effect of the independent variable. This matrix
                         has size [(ncond-1),ncond]. 
    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.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 labels of the conditions that must be 
                compared (default=1). The labels range from 1 to the number of conditions.
    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.