Pull request for HRF Toolbox and RSA Toolbox

CanlabCore/@fmri_data/hrf_fit.m

@@ -126,10 +126,10 @@
 
 if iscell(obj)
 
-    for i = numel(obj)
+    for i = 1:numel(obj)
 
 
-        [params_obj{i}, hrf_obj{i}, params_obj_dat{i}, hrf_obj_dat{i}] = hrf_fit(obj{i},TR,Runc,T,method,mode, varargin);
+        [params_obj{i}, hrf_obj{i}, params_obj_dat{i}, hrf_obj_dat{i}] = hrf_fit(obj{i},TR,Runc,T,method,mode, varargin{:});
 
 
     end
@@ -203,8 +203,7 @@
     end
 
 
-    hrf_fit(obj)
-
+    error('Struct SPM input handling is not supported in this entrypoint. Use EstHRF_inAtlas/hrf_fit for SPM workflows.');
 
 end
 

CanlabCore/@fmri_data/rsa_compare_models.m

@@ -0,0 +1,6 @@
+function varargout = rsa_compare_models(dat, varargin)
+% rsa_compare_models  Thin alias for rsa_compare_lme_models.
+%
+% See rsa_compare_lme_models.m for full documentation.
+[varargout{1:nargout}] = rsa_compare_lme_models(dat, varargin{:});
+end

CanlabCore/@fmri_data/rsa_lm.m

@@ -0,0 +1,113 @@
+function [mdl, tbl, info] = rsa_lm(dat, varargin)
+% rsa_lm  Fixed-effects multi-level RSA via fitlm.
+%
+% Pools all (i, j) upper-triangle pairs from the omnibus image-level RSM
+% (including cross-subject pairs by default per design doc §6.6) and fits
+% a linear model with same-vs-different predictors. Subject is treated as
+% a same-vs-different predictor itself (SameSubject), NOT as a random effect.
+%
+% Use rsa_lme() instead if you want random effects.
+%
+% Usage examples
+% --------------
+%   % Default: all pairs, all predictors
+%   mdl = rsa_lm(dat, ...
+%       'predictors',  {'subject_id','session_number','condition','bodysite'});
+%
+%   % Within-subject only (subset of the data; conceptually equivalent to
+%   % the LME fixed-effect part)
+%   mdl = rsa_lm(dat, ...
+%       'predictors',  {'condition','bodysite','session_number'}, ...
+%       'pair_scope',  'within_subject');
+%
+%   % With interactions
+%   mdl = rsa_lm(dat, ...
+%       'predictors',   {'condition','bodysite','session_number'}, ...
+%       'interactions', {{'condition','bodysite'},{'session_number','condition'}});
+%
+% Optional name-value
+% -------------------
+%   'predictors'         cellstr of metadata columns.
+%   'interactions'       cell of pair-cellstr.
+%   'three_way'          cell of triple-cellstr.
+%   'subject_var'        Default 'subject_id'.
+%   'pair_scope'         'all' (default) | 'within_subject'.
+%   'response_transform' 'fisherz' (default) | 'none' | 'rank'.
+%   'metric'             'correlation' (default) | 'spearman' | 'cosine'.
+%   'standardize'        logical (default false). Standardize predictors
+%                        before fit.
+%   'verbose'            logical (default true).
+%
+% Outputs
+% -------
+%   mdl   LinearModel object
+%   tbl   long-format table used for fitting
+%   info  struct from assemble_lme_table
+
+p = inputParser;
+p.addParameter('predictors',         {},             @(x) iscellstr(x) || isstring(x) || isempty(x)); %#ok<ISCLSTR>
+p.addParameter('interactions',       {},             @iscell);
+p.addParameter('three_way',          {},             @iscell);
+p.addParameter('subject_var',        'subject_id',   @(x) ischar(x) || isstring(x));
+p.addParameter('pair_scope',         'all',          @(x) (ischar(x) || isstring(x)) && ismember(lower(char(x)), {'all','within_subject'}));
+p.addParameter('response_transform', 'fisherz',      @(x) ischar(x) || isstring(x));
+p.addParameter('metric',             'correlation',  @(x) ischar(x) || isstring(x));
+p.addParameter('standardize',        false,          @(x) islogical(x) || isnumeric(x));
+p.addParameter('verbose',            true,           @(x) islogical(x) || isnumeric(x));
+p.parse(varargin{:});
+opt = p.Results;
+
+predictors = cellstr(opt.predictors);
+if isempty(predictors)
+    error('rsa_lm:noPredictors', 'Pass at least one predictor.');
+end
+
+% =========================================================================
+% Assemble table
+% =========================================================================
+[tbl, info] = assemble_lme_table(dat, ...
+    'predictors',         predictors, ...
+    'interactions',       opt.interactions, ...
+    'three_way',          opt.three_way, ...
+    'subject_var',        opt.subject_var, ...
+    'pair_scope',         opt.pair_scope, ...
+    'response_transform', opt.response_transform, ...
+    'metric',             opt.metric, ...
+    'verbose',            opt.verbose);
+
+% =========================================================================
+% Build formula
+% =========================================================================
+% All predictor + interaction Same<X> columns are the fixed effects.
+% For pair_scope='all', subject_var was folded into predictors by
+% assemble_lme_table, so SameSubject is already in info.predictor_names.
+rhs_terms = [info.predictor_names, info.interaction_names, info.three_way_names];
+rhs_terms = unique(rhs_terms, 'stable');   % guard against any duplication
+
+% Optional standardize: z-score predictor columns in-place
+if logical(opt.standardize)
+    for i = 1:numel(rhs_terms)
+        col = rhs_terms{i};
+        v = tbl.(col);
+        if std(v) > 0, tbl.(col) = (v - mean(v)) ./ std(v); end
+    end
+end
+
+formula = sprintf('Y ~ %s', strjoin(rhs_terms, ' + '));
+
+if opt.verbose
+    fprintf('rsa_lm: fitting formula:\n  %s\n  (n=%d rows, scope=%s)\n', ...
+        formula, height(tbl), opt.pair_scope);
+end
+
+% =========================================================================
+% Fit
+% =========================================================================
+mdl = fitlm(tbl, formula);
+
+if opt.verbose
+    fprintf('rsa_lm: R^2 = %.4f, adjusted R^2 = %.4f\n', ...
+        mdl.Rsquared.Ordinary, mdl.Rsquared.Adjusted);
+end
+
+end

CanlabCore/@fmri_data/rsa_lm_by_subject.m

@@ -0,0 +1,134 @@
+function [T, mdls, info] = rsa_lm_by_subject(dat, varargin)
+% rsa_lm_by_subject  Per-subject fitlm fits with aggregated coefficient table.
+%
+% Replicates the per-subject loop from `08072024 Run-Level RDM Analysis with
+% RSA Toolbox.mlx` lines 1879-1900 + lines 2050-2079 (partial R²): for each
+% subject, fits a separate `fitlm` of similarity ~ same-vs-different predictors
+% on that subject's within-subject pairs only. Returns a long-format table of
+% coefficients with subject IDs so you can run group-level inference on the
+% per-subject betas (paired ttest across subjects, etc.).
+%
+% Differs from rsa_lme: per-subject FIXED-effects fits aggregated post hoc,
+% rather than one mixed-effects model treating Subject as a random effect.
+% Useful for assessing between-subject variability in coefficients and for
+% sanity-checking the LME estimates.
+%
+% Usage
+% -----
+%   T = rsa_lm_by_subject(dat, ...
+%       'predictors',   {'condition','bodysite','sesno'}, ...
+%       'interactions', {{'condition','bodysite'}}, ...
+%       'subject_var',  'sub');
+%
+% Optional name-value
+% -------------------
+%   Same as rsa_lme except no random-effects spec. Plus:
+%   'partial_r2'    logical (default true). Compute per-term partial R² by
+%                   refitting the reduced model dropping each term. Adds
+%                   columns to T.
+%   'verbose'       logical (default true).
+%
+% Outputs
+% -------
+%   T     long-format table:
+%           sub | term | beta | se | t | p | partial_R2 | full_R2 | full_adj_R2
+%   mdls  cell array of fitted LinearModel objects, one per subject
+%   info  struct from the underlying assemble_lme_table call (uses the
+%         first subject's metadata as representative)
+
+p = inputParser;
+p.addParameter('predictors',         {},             @(x) iscellstr(x) || isstring(x) || isempty(x)); %#ok<ISCLSTR>
+p.addParameter('interactions',       {},             @iscell);
+p.addParameter('three_way',          {},             @iscell);
+p.addParameter('subject_var',        'subject_id',   @(x) ischar(x) || isstring(x));
+p.addParameter('response_transform', 'fisherz',      @(x) ischar(x) || isstring(x));
+p.addParameter('metric',             'correlation',  @(x) ischar(x) || isstring(x));
+p.addParameter('partial_r2',         true,           @(x) islogical(x) || isnumeric(x));
+p.addParameter('verbose',            true,           @(x) islogical(x) || isnumeric(x));
+p.parse(varargin{:});
+opt = p.Results;
+
+predictors = cellstr(opt.predictors);
+if isempty(predictors)
+    error('rsa_lm_by_subject:noPredictors', 'Pass at least one predictor.');
+end
+
+% Build the full within-subject pairs table; we'll slice by Subject below
+[tbl_all, info] = assemble_lme_table(dat, ...
+    'predictors',         predictors, ...
+    'interactions',       opt.interactions, ...
+    'three_way',          opt.three_way, ...
+    'subject_var',        opt.subject_var, ...
+    'pair_scope',         'within_subject', ...
+    'response_transform', opt.response_transform, ...
+    'metric',             opt.metric, ...
+    'verbose',            opt.verbose);
+
+sub_short = info.subject_var_short;
+sub_levels = categories(tbl_all.(sub_short));
+n_subj = numel(sub_levels);
+
+% Predictor + interaction column names in the table
+term_cols = [info.predictor_names, info.interaction_names, info.three_way_names];
+n_terms = numel(term_cols);
+
+% Build base formula (no random effects)
+rhs = strjoin(term_cols, ' + ');
+formula = sprintf('Y ~ %s', rhs);
+
+% =========================================================================
+% Per-subject fits
+% =========================================================================
+mdls = cell(n_subj, 1);
+rows = cell(n_subj, 1);
+
+for s = 1:n_subj
+    is_s = tbl_all.(sub_short) == sub_levels{s};
+    tbl_s = tbl_all(is_s, :);
+    mdls{s} = fitlm(tbl_s, formula);
+
+    n_coefs = mdls{s}.NumCoefficients;
+    sub_col   = repmat({char(sub_levels{s})}, n_coefs, 1);
+    coef_name = mdls{s}.Coefficients.Properties.RowNames;
+    betas     = mdls{s}.Coefficients.Estimate;
+    ses       = mdls{s}.Coefficients.SE;
+    ts        = mdls{s}.Coefficients.tStat;
+    ps        = mdls{s}.Coefficients.pValue;
+    full_r2     = repmat(mdls{s}.Rsquared.Ordinary, n_coefs, 1);
+    full_adj_r2 = repmat(mdls{s}.Rsquared.Adjusted, n_coefs, 1);
+
+    % Partial R² per term: refit dropping that term, R²_full - R²_reduced
+    partial_r2 = nan(n_coefs, 1);
+    if logical(opt.partial_r2)
+        for c = 1:n_coefs
+            name = coef_name{c};
+            if strcmp(name, '(Intercept)'), continue; end
+            others = setdiff(term_cols, {name});
+            if isempty(others)
+                f_red = 'Y ~ 1';
+            else
+                f_red = sprintf('Y ~ %s', strjoin(others, ' + '));
+            end
+            try
+                mdl_red = fitlm(tbl_s, f_red);
+                partial_r2(c) = mdls{s}.Rsquared.Ordinary - mdl_red.Rsquared.Ordinary;
+            catch
+                partial_r2(c) = NaN;
+            end
+        end
+    end
+
+    rows{s} = table(sub_col, coef_name, betas, ses, ts, ps, partial_r2, ...
+        full_r2, full_adj_r2, ...
+        'VariableNames', {sub_short, 'term', 'beta', 'se', 't', 'p', ...
+                          'partial_R2', 'full_R2', 'full_adj_R2'});
+end
+
+T = vertcat(rows{:});
+
+if opt.verbose
+    fprintf('rsa_lm_by_subject: fit %d per-subject models with %d terms each.\n', ...
+        n_subj, n_terms);
+end
+
+end