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 — reference:ft_statistics_montecarlo [2018/08/23 14:43] (current) Line 1: Line 1: + =====  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>​ + <​font color=green>​FT_STATISTICS_MONTECARLO​ 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 <​font color=green>​FT_TIMELOCKANALYSIS,​ <​font color=green>​FT_FREQANALYSIS​ + or <​font color=green>​FT_SOURCEANALYSIS​ respectively,​ or from <​font color=green>​FT_TIMELOCKGRANDAVERAGE,​ + <​font color=green>​FT_FREQGRANDAVERAGE​ or <​font color=green>​FT_SOURCEGRANDAVERAGE​ 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 <​font color=green>​FT_PREPARE_NEIGHBOURS​ + 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 <​font color=green>​FT_TIMELOCKSTATISTICS,​ <​font color=green>​FT_FREQSTATISTICS,​ <​font color=green>​FT_SOURCESTATISTICS​ +