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reference:ft_statistics_montecarlo [2018/08/23 14:43] (current)
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 +=====  FT_STATISTICS_MONTECARLO =====
 +
 +Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_statistics_montecarlo"​.
 +
 +<​html><​pre>​
 +  <a href=/​reference/​ft_statistics_montecarlo><​font color=green>​FT_STATISTICS_MONTECARLO</​font></​a>​ performs a nonparametric statistical test by calculating
 +  Monte-Carlo estimates of the significance probabilities and/or critical values
 +  from the permutation distribution. This function should not be called
 +  directly, instead you should call the function that is associated with the
 +  type of data on which you want to perform the test.
 + 
 +  Use as
 +    stat = ft_timelockstatistics(cfg,​ data1, data2, data3, ...)
 +    stat = ft_freqstatistics ​   (cfg, data1, data2, data3, ...)
 +    stat = ft_sourcestatistics ​ (cfg, data1, data2, data3, ...)
 + 
 +  Where the data is obtained from <a href=/​reference/​ft_timelockanalysis><​font color=green>​FT_TIMELOCKANALYSIS</​font></​a>,​ <a href=/​reference/​ft_freqanalysis><​font color=green>​FT_FREQANALYSIS</​font></​a>​
 +  or <a href=/​reference/​ft_sourceanalysis><​font color=green>​FT_SOURCEANALYSIS</​font></​a>​ respectively,​ or from <a href=/​reference/​ft_timelockgrandaverage><​font color=green>​FT_TIMELOCKGRANDAVERAGE</​font></​a>,​
 +  <a href=/​reference/​ft_freqgrandaverage><​font color=green>​FT_FREQGRANDAVERAGE</​font></​a>​ or <a href=/​reference/​ft_sourcegrandaverage><​font color=green>​FT_SOURCEGRANDAVERAGE</​font></​a>​ respectively and with
 +  cfg.method = '​montecarlo'​
 + 
 +  The configuration options that can be specified are:
 +    cfg.numrandomization = number of randomizations,​ can be '​all'​
 +    cfg.correctm ​        = string, apply multiple-comparison correction, '​no',​ '​max',​ cluster',​ '​bonferroni',​ '​holm',​ '​hochberg',​ '​fdr'​ (default = '​no'​)
 +    cfg.alpha ​           = number, critical value for rejecting the null-hypothesis per tail (default = 0.05)
 +    cfg.tail ​            = number, -1, 1 or 0 (default = 0)
 +    cfg.correcttail ​     = string, correct p-values or alpha-values when doing a two-sided test, '​alpha','​prob'​ or '​no'​ (default = '​no'​)
 +    cfg.ivar ​            = number or list with indices, independent variable(s)
 +    cfg.uvar ​            = number or list with indices, unit variable(s)
 +    cfg.wvar ​            = number or list with indices, within-cell variable(s)
 +    cfg.cvar ​            = number or list with indices, control variable(s)
 +    cfg.feedback ​        = string, '​gui',​ '​text',​ '​textbar'​ or '​no'​ (default = '​text'​)
 +    cfg.randomseed ​      = string, '​yes',​ '​no'​ or a number (default = '​yes'​)
 + 
 +  If you use a cluster-based statistic, you can specify the following
 +  options that determine how the single-sample or single-voxel
 +  statistics will be thresholded and combined into one statistical
 +  value per cluster.
 +    cfg.clusterstatistic = how to combine the single samples that belong to a cluster, '​maxsum',​ '​maxsize',​ '​wcm'​ (default = '​maxsum'​)
 +                           ​option '​wcm'​ refers to '​weighted cluster mass',
 +                           a statistic that combines cluster size and
 +                           ​intensity;​ see Hayasaka & Nichols (2004) NeuroImage
 +                           for details
 +    cfg.clusterthreshold = method for single-sample threshold, '​parametric',​ '​nonparametric_individual',​ '​nonparametric_common'​ (default = '​parametric'​)
 +    cfg.clusteralpha ​    = for either parametric or nonparametric thresholding per tail (default = 0.05)
 +    cfg.clustercritval ​  = for parametric thresholding (default is determined by the statfun)
 +    cfg.clustertail ​     = -1, 1 or 0 (default = 0)
 + 
 +  To include the channel dimension for clustering, you should specify
 +    cfg.neighbours ​      = neighbourhood structure, see <a href=/​reference/​ft_prepare_neighbours><​font color=green>​FT_PREPARE_NEIGHBOURS</​font></​a>​
 +  If you specify an empty neighbourhood structure, clustering will only be done
 +  over frequency and/or time and not over neighbouring channels.
 + 
 +  The statistic that is computed for each sample in each random reshuffling
 +  of the data is specified as
 +    cfg.statistic ​      = '​indepsamplesT' ​          ​independent samples T-statistic,​
 +                          '​indepsamplesF' ​          ​independent samples F-statistic,​
 +                          '​indepsamplesregrT' ​      ​independent samples regression coefficient T-statistic,​
 +                          '​indepsamplesZcoh' ​       independent samples Z-statistic for coherence,
 +                          '​depsamplesT' ​            ​dependent samples T-statistic,​
 +                          '​depsamplesFmultivariate'​ dependent samples F-statistic MANOVA,
 +                          '​depsamplesregrT' ​        ​dependent samples regression coefficient T-statistic,​
 +                          '​actvsblT' ​               activation versus baseline T-statistic.
 +  or you can specify your own low-level statistical function.
 + 
 +  You can also use a custom statistic of your choise that is sensitive
 +  to the expected effect in the data. You can implement the statistic
 +  in a "​statfun"​ that will be called for each randomization. The
 +  requirements on a custom statistical function is that the function
 +  is called statfun_xxx,​ and that the function returns a structure
 +  with a "​stat"​ field containing the single sample statistical values.
 +  Check the private functions statfun_xxx (e.g.  with xxx=tstat) for
 +  the correct format of the input and output.
 + 
 +  See also <a href=/​reference/​ft_timelockstatistics><​font color=green>​FT_TIMELOCKSTATISTICS</​font></​a>,​ <a href=/​reference/​ft_freqstatistics><​font color=green>​FT_FREQSTATISTICS</​font></​a>,​ <a href=/​reference/​ft_sourcestatistics><​font color=green>​FT_SOURCESTATISTICS</​font></​a>​
 +</​pre></​html>​