Merge branch 'master' of https://github.com/behinger/unfold

docs/docs_sphinx/index.rst

@@ -11,10 +11,11 @@
       :align: center
       :width: 30 %
 
-Unfold 1.1 - EEG Deconvolution Toolbox
+Unfold 1.2 - EEG Deconvolution Toolbox
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 A toolbox for *deconvolution* of overlapping EEG (Pupil, LFP etc.) signals and *(non)-linear modeling*
 
+New in 1.2 (December 2020): More automatic cleaning tools (ASR, Entropy-Based). Many Bugfixes, better Documentation and better error-codes.
 
 Reference Papers
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -41,9 +42,13 @@ What can you do with *unfold*?
 * (Optional) **regularization** using glmnet
 * Temporal Response Functions (TRFs)
 
-Unmixed - Unfold addon for mixed Models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-A new addon *unmixed*, allowing to use mixed models can be found in its alpha version version `here <https://github.com/unfoldtoolbox/unmixed>`_
+Unfold.jl - a Beta-Toolbox in Julia
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+We are developing a sister-toolbox for Julia. It is not feature-par with matlab-unfold yet, e.g. splines are missing and no plotting tools. But it allows to specify more flexible basisfunctions, e.g. different types for different events and different lengths. It also fully supports MixedModels (but that part is still untested in practice, besides unittests).
+
+(deprecated) Unmixed - Unfold addon for mixed Models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(deprecated) The addon *unmixed*, allowing to use mixed models can be found in its alpha version version `here <https://github.com/unfoldtoolbox/unmixed>`_
 
 Requirements
 ^^^^^^^^^^^^^^^^^

init_unfold.m

@@ -4,6 +4,14 @@
 projectFolder  = fileparts(which('init_unfold.m'));
 scriptDir = fullfile(projectFolder,'src');
 
+if ~exist('eeg_checkset','file')
+    try
+        eeglab redraw
+    catch
+        warning('%s(): EEGLAB could not be found in your path. Please add it to your path. Otherwise, you should be fine EXCEPT if you want to use uf_epoch() (for massive univariate modeling without overlap correction) which requires EEGLAB.\n',mfilename)
+        addpath(fullfile(projectFolder,'lib','eeglab'))
+    end
+end
 
 addpath(genpath(scriptDir))
 addpath(fullfile(projectFolder,'lib','erplab'))
@@ -16,17 +24,8 @@
 
 addpath(fullfile(projectFolder,'lib','glmnet_matlab'))
 
-if ~exist('eeg_checkset','file')
-    try
-        eeglab redraw
-    catch
-        warning('%s(): EEGLAB could not be found in your path. Please add it to your path. Otherwise, you should be fine EXCEPT if you want to use uf_epoch() (for massive univariate modeling without overlap correction) which requires EEGLAB.\n',mfilename)
-        addpath(fullfile(projectFolder,'lib','eeglab'))
-    end
-end
-
 if ~exist('gramm','file')
     warning('%s():\ngramm could not be found. Did you run "git submodule update --init" to initialize submodules?\n',mfilename)
 end
 
-fprintf('Done.\n')
+fprintf('Done.\n')

readme.md

@@ -27,8 +27,8 @@ run('init_unfold.m')
 Check out the [toolbox tutorials](https://www.unfoldtoolbox.org/toolboxtutorials.html) for more information!
 ```
 EEG = tutorial_simulate_data('2x2')
-EEG = uf_designmat('eventtypes',{'fixation'},'formula','y ~ 1+ cat(stimulusType)*cat(color)')
-EEG = uf_timeexpandDesignmat('timelimits',[-0.5 1])
+EEG = uf_designmat(EEG,'eventtypes',{'fixation'},'formula','y ~ 1+ cat(stimulusType)*cat(color)')
+EEG = uf_timeexpandDesignmat(EEG,'timelimits',[-0.5 1])
 EEG = uf_glmfit(EEG)
 % (strictly speaking optional, but recommended)
 ufresult = uf_condense(EEG)

src/uf_toolbox/internal/uf_unfoldbetaSetname.m

@@ -4,7 +4,7 @@
 
 % parse inputs
 cfg = finputcheck(varargin,...
-    {'deconv','integer',[0,1,-1],-1, ... % -1 is autodetect
+    {'deconv','integer',[0,1,-1],-1; ... % -1 is autodetect
     'dataField','string','',''...
     },'mode','ignore');
 
@@ -14,11 +14,13 @@
 if cfg.deconv == -1
 
     % autodetect 
-    assert(isfield(ufresult,'beta') | isfield(ufresult,'beta_nodc'),'Error: to use autodetect at least the field ufresult.beta or ufresult.beta_nodc needs to exist')
+    assert(~isempty(cfg.dataField) || isfield(ufresult,'beta') || isfield(ufresult,'beta_nodc'),'Error: to use autodetect at least the field ufresult.beta or ufresult.beta_nodc needs to exist')
     fn = fieldnames(ufresult);
 
     % get size of betas
-    if isfield(ufresult,'beta')
+    if ~isempty(cfg.dataField)
+        sizeBeta = size(ufresult.(cfg.dataField));
+    elseif isfield(ufresult,'beta')
         sizeBeta = size(ufresult.beta);
     else
         sizeBeta = size(ufresult.beta_nodc);

src/uf_toolbox/uf_addmarginal.m

@@ -86,7 +86,9 @@
     % RECURSION ALERT!
     if length(betaSetname) > 1
         for b = betaSetname
-            ufresult_tmp    = uf_addmarginal(ufresult,'betaSetname',b{1});
+            cfg.betaSetname = b{1};
+            ufresult_tmp = uf_addmarginal(ufresult,cfg);
+
             ufresult.(b{1}) = ufresult_tmp.(b{1});
         end
         return
@@ -110,7 +112,9 @@
 fprintf('\nRe-running uf_condense() to recover unconverted splines\n')
 ufresult_avg = uf_condense(ufresult); % re-genererate, (without "evaluated" predictors)
 
-
+if ~isempty(setdiff(cfg.betaSetname,fieldnames(ufresult_avg)))
+    error('currently we do not support custom fieldnames in uf_addmarginal. The reason is that we have to get the values at the average via uf_condense which does not return it for the other values. You have to do this calculations manually')
+end
 % Calculate marginal effect
 if strcmp(cfg.type,'MEM')
     fprintf('Calculating marginal effect at the mean of each spline/continuous predictor.\n');
@@ -196,7 +200,10 @@
     % multiple times, we can add the same marginal
 
     for p = unique(eventParamNames)
-
+        if isempty(p) % happens if TRF is in the mix
+            fprintf("found an TRF, ignoring it")
+            continue
+        end
         % Find the names & types of the other parameters
         currEvent       = e_Idx(strcmp(p,eventParamNames));
         otherEvents     = setdiff(e_Idx,currEvent);