Added cluster-based permutation testing over timeseries#64
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- need to figure out most efficient way to write `all_res['time']`
Based on the large DataFrame of time-resolved regression coefficients generated across all electrodes using `statistics_utils.time_resolved_regression_single_channel(timeseries, regressors, standardize=True, smooth=False)`, can subselect rows for a given region of interest (e.g., 'AMY"):
```
roi_df = all_channels_df[all_channels_df.region == 'AMY'][['Original_Estimate', 'ts']]
```
Can run the following: `roi_ttest, cluster_tstats = cluster_based_permutation(roi_df)`, which produces the following outputs:
- roi_ttest: DataFrame with t-statistics and p-values for each timepoint.
- cluster_tstats: DataFrame with summed t-statistics and timepoint range for each cluster identified.
`cluster_based_permutation()` relies on the following dependencies:
- `find_clusters()`
- `ts_permutation_test()` and `ts_permutation_test()`
It also depends on the following packages and functions: pandas (pd.DataFrame, groupby, apply, reset_index, loc), numpy (np.where, np.nan, np.abs, np.random.seed, np.random.permutation), scipy.stats (ttest_1samp), scipy.ndimage (label), joblib (Parallel, delayed), tqdm (tqdm)
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This mostly looks good but I don't think shuffling the 'ts' values of the regression outputs is exactly the right way to do this. I think to mirror the original category-based permutations in Maris and Oostenveld 2007 I think we want to shuffle the regressors going into |
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Based on the large DataFrame of time-resolved regression coefficients generated across all electrodes using
statistics_utils.time_resolved_regression_single_channel(timeseries, regressors, standardize=True, smooth=False), can subselect rows for a given region of interest (e.g., 'AMY"):Can run the following:
roi_ttest, cluster_tstats = cluster_based_permutation(roi_df), which produces the following outputs:-
roi_ttest: DataFrame with t-statistics and p-values for each timepoint.-
cluster_tstats: DataFrame with summed t-statistics and timepoint range for each cluster identified.cluster_based_permutation()relies on the following dependencies:find_clusters()ts_permutation_test()andts_permutation_test()It also depends on the following packages:
pandas,numpy,scipy.stats.ttest_1samp,scipy.ndimage.label,joblib.Parallel,joblib.delayed,tqdm.tqdm