文件列表:

cumulative_gaussian.m (1315, 2004-07-14)
cumulative_gaussian_inv.m (1446, 2004-07-14)
gpl.txt (12609, 2002-11-19)
normalize.m (1266, 2007-03-31)
percentile.m (1138, 2004-07-28)
plotroi.doc (29696, 2006-04-05)
plotroi.m (71, 2006-01-17)
plscmd.m (173, 2005-02-01)
pls_analysis.m (54963, 2008-08-22)
read_ixy.m (1702, 2006-04-12)
roi_fill_map.m (891, 2006-01-18)
roi_plot_ui.m (10069, 2006-02-20)
roi_select.m (15687, 2006-04-05)
roi_session_ui.m (35323, 2006-04-12)
rri_bootprocrust.m (283, 2003-03-27)
rri_boot_check.m (7770, 2007-10-09)
rri_boot_order.m (12644, 2007-10-09)
rri_boot_samples.m (871, 2004-12-08)
rri_corr_maps.m (447, 2003-03-27)
rri_corr_maps_notall.m (418, 2005-07-19)
rri_file_menu.m (4153, 2006-01-25)
rri_get_behavscores.m (1622, 2003-03-27)
rri_islowvariability.m (436, 2007-02-22)
rri_perm_order.m (3739, 2007-02-06)
rri_progress_status.m (5990, 2005-06-14)
rri_progress_ui.m (3093, 2005-06-14)
rri_select_file.m (17235, 2005-03-29)
rri_task_mean.m (563, 2007-11-06)
rri_task_mean1.m (628, 2007-11-06)
rri_txtbox.m (8813, 2004-08-09)
rri_xcor.m (848, 2003-03-27)

% This document provides some simple guidelines for command-line PLS usage: % % Usage: result = pls_analysis(datamat_lst, num_subj_lst, num_cond); % For details of input/output parameters, type: help pls_analysis % =========================================================================== % Usually, the first thing to be checked is singular value, and % the percentage of eigenvalue. If the percentage is very low for % certain LVs, simply discard those LVs % figure; bar(result.s); % display singular value bar graph pct=(result.s.^2/sum(result.s.^2)); % calculate eigenvalue percentage figure; bar(pct); % notice that sum(pct) should be 1 % If permutation loop is applied, you can also check whether those LVs % can be trust by plot bar graph of the probability of permuted values % greater than observed values. If for certain LVs, the chance of permuted % values greater than observed values are very large, you also need to % discard those LVs % figure; bar(result.perm_result.sprob); % Next, for Task PLS, the outlier subjects should be checked for each % meaningful LV. If there is any outlier subjects, you may want to % reconsider if you want to keep them or remove them. If you decide % to remove them, you will have to run analysis again without those % subjects. % LV = 1; % just an example. can be 2,3, ... figure; plot(result.vsc(:,LV),result.usc(:,LV),'*'); % You also need to render the active area map "result.u" depending on % whether your data is a brain image or a brain wave. If it is a % brain image, you must have a brain coordinates and dimensions handy. % Simply create a brain and put the result.u into the brain, and % then render the brain. % For Task PLS, Design LV can help you to interpret which condition % is distinguished by which LV % LV = 1; % just an example. can be 2,3, ... figure; bar(result.v(:,1)); % If the bootstrap is applied, you can find which active area indicated % by result.u is reliable. Let's use 95 percentile as threshold, we can % find out the indices of the brain coordinate that are stable. % thresh = percentile(result.boot_result.compare_u(:,1), 95) idx = find(abs(result.boot_result.compare_u(:,1)) > thresh);

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