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tutorial:cluster_permutation_timelock [2012/07/21 16:03] 89.156.125.19 [Plotting the results] |
tutorial:cluster_permutation_timelock [2013/03/29 11:33] (current) jan-mathijs |
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| We now briefly discuss the configuration fields that are not specific for **[[reference:ft_timelockstatistics|ft_timelockstatistics]]**: | We now briefly discuss the configuration fields that are not specific for **[[reference:ft_timelockstatistics|ft_timelockstatistics]]**: | ||
| - | cfg.channel = {'MEG'}; % cell-array with selected channel labels | + | cfg_neighb = []; |
| - | cfg.latency = [0 1]; % time interval over which the experimental | + | |
| - | % conditions must be compared (in seconds) | + | |
| cfg_neighb.method = 'distance'; | cfg_neighb.method = 'distance'; | ||
| - | neighbours = ... % specifies with which sensors other sensors | + | neighbours = ft_prepare_neighbours(cfg_neighb, dataFC_LP); |
| - | ft_prepare_neighbours(... % can form clusters | + | |
| - | cfg.neighb, dataFC_LP); | + | cfg.neighbours = neighbours; % the neighbours specify for each sensor with |
| - | cfg.neighbours = neighbours; | + | % which other sensors it can form clusters |
| + | cfg.channel = {'MEG'}; % cell-array with selected channel labels | ||
| + | cfg.latency = [0 1]; % time interval over which the experimental | ||
| + | % conditions must be compared (in seconds) | ||
| + | |||
| With these two options, we select the spatio-temporal dataset involving all MEG channels and the time interval between 0 and 1 second. The two experimental conditions will only be compared on this selection of the complete spatio-temporal dataset. Also, feel free to consult | With these two options, we select the spatio-temporal dataset involving all MEG channels and the time interval between 0 and 1 second. The two experimental conditions will only be compared on this selection of the complete spatio-temporal dataset. Also, feel free to consult | ||
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| clusterstat: -1.0251e+04 | clusterstat: -1.0251e+04 | ||
| - | It is possible that the p-values in your output are a little bit different from 0. This is because **[[reference:ft_timelockstatistics|ft_timelockstatistics]]** calculated as a Monte Carlo approximation of the permutation p-values: the p-value for the k-th positive cluster is calculated as the proportion of random draws from the permutation distribution in which the maximum of the cluster-level statistics is more larger than stat.posclusters(k).clusterstat. | + | It is possible that the p-values in your output are a little bit different from 0. This is because **[[reference:ft_timelockstatistics|ft_timelockstatistics]]** calculated as a Monte Carlo approximation of the permutation p-values: the p-value for the k-th positive cluster is calculated as the proportion of random draws from the permutation distribution in which the maximum of the cluster-level statistics is larger than stat.posclusters(k).clusterstat. |
| ==== Plotting the results ==== | ==== Plotting the results ==== | ||
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| raweffectFICvsFC = avgFIC; | raweffectFICvsFC = avgFIC; | ||
| % Then take the difference of the averages. | % Then take the difference of the averages. | ||
| - | raweffectFICvsFC.avg = avg_FIC.avg - avg_FC.avg; | + | raweffectFICvsFC.avg = avgFIC.avg - avgFC.avg; |
| We then construct a boolean matrix indicating membership in the significant clusters. This matrix has size [Number_of_MEG_channels × Number_of_temporal_samples], like stat.posclusterslabelmat. We'll make two such matrices: one for positive clusters (named pos), and one for negative (neg). All (channel,time)-pairs belonging to the significant clusters will be coded in the new boolean matrix as 1, and all those that don't will be coded as 0. | We then construct a boolean matrix indicating membership in the significant clusters. This matrix has size [Number_of_MEG_channels × Number_of_temporal_samples], like stat.posclusterslabelmat. We'll make two such matrices: one for positive clusters (named pos), and one for negative (neg). All (channel,time)-pairs belonging to the significant clusters will be coded in the new boolean matrix as 1, and all those that don't will be coded as 0. | ||
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| ----- | ----- | ||
| - | This tutorial was last tested by Jörn, with version 20110507 of FieldTrip, using Matlab 2009b on a Windows 7 platform. | + | This tutorial was last tested by Jan-Mathijs, with version 20130301 of FieldTrip, using Matlab 2011a on a Windows 7 platform. |
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