fixes

src/batches/stats/setBatchFactorialDesign.m

@@ -36,6 +36,8 @@
     return
   end
 
+  [BIDS, opt] = getData(opt, opt.dir.preproc);
+
   printBatchName(sprintf('specify group level fmri model for node "%s"', nodeName), opt);
 
   % now we fetch the contrast for each subject and allocate them in the batch
@@ -59,10 +61,10 @@
       contrastsList = {};
       groups = {};
 
-      [BIDS, opt] = getData(opt, opt.dir.preproc);
-
       contrasts = getContrastsListForFactorialDesign(opt, nodeName);
 
+      % TODO refactor
+
       designMatrix = opt.model.bm.get_design_matrix('Name', nodeName);
       designMatrix = cellfun(@(x) num2str(x), designMatrix, 'uniformoutput', false);
 
@@ -176,6 +178,7 @@
   node = opt.model.bm.get_nodes('Name', nodeName);
   groupBy = node.GroupBy;
 
+  % TODO refactor
   groupColumnHdr = setxor(groupBy, {'contrast'});
   groupColumnHdr = groupColumnHdr{1};
 
@@ -344,39 +347,45 @@
 
   commonMsg = sprintf('for the dataset level node: "%s"', nodeName);
 
+  % TODO refactor
   participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
   columns = fieldnames(participants);
   status = checkGroupBy(thisNode, columns);
 
   [glmType, ~, groupBy] = groupLevelGlmType(opt, nodeName, participants);
 
   % only certain type of model supported for now
-  if ismember(glmType, {'unknown'})
-    % should probably exit if we are on 2 sample T test
-
-    msg = sprintf('Models other than group average /comparisons not implemented yet %s', commonMsg);
+  if ismember(glmType, {'unknown', 'two_sample_t_test'})
+    % TODO update message to with better info for 2 sample T-Test
+    msg = sprintf(['Models other than group average / comparisons ', ...
+                   'not implemented yet %s'], commonMsg);
     notImplemented(mfilename(), msg, opt);
+    status = false;
+    return
+  end
+
+  datasetLvlContrasts = opt.model.bm.get_contrasts('Name', nodeName);
+  datasetLvlDummyContrasts = opt.model.bm.get_dummy_contrasts('Name', nodeName);
 
+  if isempty(datasetLvlContrasts) && isempty(datasetLvlDummyContrasts)
+    msg = sprintf('No contrast specified %s', commonMsg);
+    logger('WARNING', msg, 'id', id, 'filename', mfilename(), 'options', opt);
     status = false;
+    return
+  end
 
+  if ismember(glmType, {'one_sample_t_test'}) && ...
+    (not(isempty(datasetLvlContrasts)) || isempty(datasetLvlDummyContrasts))
+    msg = sprintf('For one-sample t-test only DummyContrasts are implemented %s', commonMsg);
+    notImplemented(mfilename(), msg, opt);
+    status = false;
   end
 
-  %   datasetLvlDummyContrasts = opt.model.bm.get_dummy_contrasts('Name', nodeName);
-  %   if isempty(datasetLvlDummyContrasts) || ~isfield(datasetLvlDummyContrasts, 'Test')
-  %
-  %     msg = sprintf('Only DummyContrasts are implemented %s', commonMsg);
-  %     notImplemented(mfilename(), msg, opt);
-  %
-  %     status = false;
-  %
-  %   end
-  %
-  %   datasetLvlContrasts = opt.model.bm.get_contrasts('Name', nodeName);
-  %   if ~isempty(datasetLvlContrasts)
-  %
-  %     msg = sprintf('Contrasts are not yet implemented %s', commonMsg);
-  %     notImplemented(mfilename(), msg, opt);
-  %
-  %   end
+  if ismember(glmType, {'one_way_anova'}) && ...
+          (isempty(datasetLvlContrasts) || not(isempty(datasetLvlDummyContrasts)))
+    msg = sprintf('For one-way ANOVA only contrasts are yet implemented %s', commonMsg);
+    notImplemented(mfilename(), msg, opt);
+    status = false;
+  end
 
 end

src/batches/stats/setBatchGroupLevelContrasts.m

@@ -26,6 +26,7 @@
 
   printBatchName('group level contrast estimation', opt);
 
+  % TODO refactor
   participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
   [groupGlmType, designMatrix, groupBy] =  groupLevelGlmType(opt, nodeName, participants);
 

src/batches/stats/setBatchTwoSampleTTest.m

@@ -66,6 +66,7 @@
   group1 = group1{2};
   group2 = group2{2};
 
+  % TODO refactor
   availableGroups = unique(BIDS.raw.participants.content.(groupField));
 
   if any(~ismember({group1, group2}, availableGroups))

src/bids_model/checkGroupBy.m

@@ -2,7 +2,7 @@
   %
   % Only certain type of GroupBy supported for now for each level
   %
-  % This helps doing some defensive programming
+  % This helps doing some defensive programming.
   %
 
   % (C) Copyright 2022 bidspm developers
@@ -53,19 +53,23 @@
 
     case 'dataset'
 
-      supportedGroupBy = {'["contrast"]', '["contrast", "group"]'};
+      supportedGroupBy = { ...
+                          '["contrast"]', ...
+                          '["contrast", "x"] for "x" being a participant.tsv column name.'};
 
       % only certain type of GroupBy supported for now
       status = false;
       if numel(groupBy) == 1 && all(ismember(lower(groupBy), {'contrast'}))
         status = true;
+
       elseif numel(groupBy) == 2 && iscellstr(extraVar) && numel(extraVar) > 0
         for i = 1:numel(extraVar)
           if all(ismember(groupBy, {'contrast', extraVar{i}}))
             status = true;
             break
           end
         end
+
       end
 
   end

src/bids_model/getContrastsList.m

@@ -1,4 +1,4 @@
-function contrastsList = getContrastsList(model, node)
+function contrastsList = getContrastsList(model, node, extraVar)
   %
   % Get list of names of Contrast from this Node
   % or gets its from the previous Nodes
@@ -18,6 +18,10 @@
 
   % (C) Copyright 2022 bidspm developers
 
+  if nargin < 3
+    extraVar = {};
+  end
+
   contrastsList = {};
 
   if ischar(node)
@@ -44,7 +48,7 @@
         % TODO relax those assumptions
 
         % assumptions
-        assert(checkGroupBy(node));
+        assert(checkGroupBy(node, extraVar));
         assert(node.Model.X == 1);
 
         contrastsList = getContrastsFromParentNode(model, node);

src/bids_model/getContrastsListForFactorialDesign.m

@@ -15,8 +15,13 @@
   % (C) Copyright 2022 bidspm developers
 
   % assuming we want to only average at the group level
+
+  % TODO refactor
   participants = bids.util.tsvread(fullfile(opt.dir.raw, 'participants.tsv'));
+  columns = fieldnames(participants);
+
   [groupGlmType, ~, ~] =  groupLevelGlmType(opt, nodeName, participants);
+
   if ismember(groupGlmType, {'one_sample_t_test', 'one_way_anova'})
 
     edge = opt.model.bm.get_edge('Destination', nodeName);
@@ -30,14 +35,14 @@
     else
 
       % this assumes DummyContrasts exist
-      contrastsList = getDummyContrastsList(opt.model.bm, nodeName);
+      contrastsList = getDummyContrastsList(opt.model.bm, nodeName, columns);
 
       node = opt.model.bm.get_nodes('Name', nodeName);
 
       % if no specific dummy contrasts mentioned also include all contrasts from previous levels
       % or if contrasts are mentioned we grab them
       if ~isfield(node.DummyContrasts, 'Contrasts') || isfield(node, 'Contrasts')
-        tmp = getContrastsList(opt.model.bm, nodeName);
+        tmp = getContrastsList(opt.model.bm, nodeName, columns);
         for i = 1:numel(tmp)
           contrastsList{end + 1} = tmp{i}.Name;
         end
@@ -48,7 +53,8 @@
   else
 
     commonMsg = sprintf('for the dataset level node: "%s"', nodeName);
-    msg = sprintf('Models other than group average not implemented yet %s', commonMsg);
+    msg = sprintf(['Models other than group average / comparisons ', ...
+                   'not implemented yet %s'], commonMsg);
     notImplemented(mfilename(), msg, opt);
 
     contrastsList = {};

src/bids_model/getDummyContrastsList.m

@@ -1,4 +1,4 @@
-function dummyContrastsList = getDummyContrastsList(model, node)
+function dummyContrastsList = getDummyContrastsList(model, node, extraVar)
   %
   % Get list of names of DummyContrast from this Node or gets its from the
   % previous Nodes
@@ -18,6 +18,10 @@
 
   % (C) Copyright 2022 bidspm developers
 
+  if nargin < 3
+    extraVar = {};
+  end
+
   dummyContrastsList = {};
 
   if ischar(node)
@@ -33,7 +37,7 @@
 
   else
 
-    assert(checkGroupBy(node));
+    assert(checkGroupBy(node, extraVar));
 
     switch lower(node.Level)
 

src/stats/group_level/groupLevelGlmType.m

@@ -6,6 +6,8 @@
   if nargin < 3
     participants = struct();
   end
+
+  % TODO refactor
   columns = fieldnames(participants);
 
   srcDesignMatrix = opt.model.bm.getBidsDesignMatrix('Name', nodeName);