update limo

Author: dungscout96

code/plugins/reformat_plugin.py

@@ -130,6 +130,11 @@ def reformat_plugin_dir(plugin_input_dir, plugin_name, formatted_name, order, li
 '''.format(link=link, order=order)
         text = f.read()
         text = append_text + text
+
+        if plugin_name == 'LIMO':
+            with open(os.path.join(wiki_plugin_input_dir, 'Home.md')) as f:
+                text += f.read()
+
         with open(index_file, 'w') as out:
             out.write(text)
 # main

plugins/limo/1.-Preprocessing.md renamed to plugins/limo/01.-Preprocessing.md

@@ -4,8 +4,8 @@ parent: LIMO
 grand_parent: Plugins
 render_with_liquid: false
 
-title: Within Subject Categorical Designs
-long_title: Within Subject Categorical Designs
+title: Within Subject Categorical Designs intro
+long_title: Within Subject Categorical Designs intro
 nav_order: 4
 ---
 - [1 way repeated measures ANOVA (Famous, Unfamiliar, Scrambled faces as conditions)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions)))

plugins/limo/2.-Within-Subject-Categorical-Designs.md renamed to plugins/limo/02.-Within-Subject-Categorical-Designs.md

@@ -0,0 +1,39 @@
+---
+layout: default
+parent: LIMO
+grand_parent: Plugins
+render_with_liquid: false
+
+title: Between Subjects Categorical Designs
+long_title: Between Subjects Categorical Designs
+nav_order: 13
+---
+We replicate here the 1-way ANOVA with familiar, unfamiliar and scrambled faces but split the data in two age groups. Of course, we can take the txt files, edit them and save copies for each group – then in LIMO MEEG we simply use these files. Here, instead, we recompute the subjects model adding in the STUDY design our groups, which will consequently save txt files per group (but not change estimates per subjects). Since some subjects have unspecified age – we create three groups based on the median (figure 35).  
+
+Group 1 is under 26: sub- 3, 8, 15, 16, 17, 18  
+Group 2 is above or equal 26: sub- 2, 5, 9, 10, 11, 12, 14  
+Group 3: sub- 4, 6, 7, 13, 19 unspecified  
+
+![Figure 35. Edit study](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/master/resources/images/35.jpg) 
+_Figure 35. Editing STUDY adding groups_
+
+You can update the study using pop_study typing in command line:
+```matlab
+cd(STUDY.filepath)
+[STUDY ALLEEG] = std_editset( STUDY, ALLEEG, 'commands',{{'index',2,'group','1'}, ...
+    {'index',7,'group','1'},{'index',14,'group','1'},{'index',15,'group','1'}, ...
+    {'index',16,'group','1'},{'index',17,'group','1'},{'index',1,'group','2'}, ...
+    {'index',4,'group','2'},{'index',8,'group','2'},{'index',9,'group','2'}, ...
+    {'index',10,'group','2'},{'index',11,'group','2'},{'index',13,'group','2'}, ...
+    {'index',3,'group','3'},{'index',5,'group','3'},{'index',6,'group','3'}, ...
+    {'index',12,'group','3'}, {'index',18,'group','3'}}, 'updatedat','off','rmclust','on');
+[STUDY, EEG] = pop_savestudy( STUDY, EEG, 'savemode','resave');
+```
+
+Estimate the models, selecting the 1st design with face type only. As before, text files are created, with additionally a split per group of LIMO/Beta/con files. 
+
+From here, we can perform two 2nd level analyses:
+- [Between subjects’ ANOVAs with repeated factors](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/9.-Between-subjects%E2%80%99-ANOVAs-with-repeated-factors)
+- [Two sample t-tests](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/10.-Two-sample-t-tests)
+
+

plugins/limo/2.-One-way-repeated-measures-ANOVA-Famous-Unfamiliar-Scrambled-faces-as-conditions.md renamed to plugins/limo/03.1.-One-way-repeated-measures-ANOVA-Famous-Unfamiliar-Scrambled-faces-as-conditions.md

@@ -6,7 +6,7 @@ render_with_liquid: false
 
 title: Regression among subjects
 long_title: Regression among subjects
-nav_order: 16
+nav_order: 15
 ---
 In the [between subjects’ ANOVA with repeated factor](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/Between-subjects%E2%80%99-ANOVAs-with-repeated-factors), we artificially split subjects into young and old subjects. Such post-hoc splitting is not recommended and typically create spurious results. Instead, we could test how much age influences face perception. For this, we will use the contrast faces vs scrambled computed previously in the [one-sample t-test](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level)). 
 

plugins/limo/3.--One-way-repeated-measures-ANOVA-revised-Famous-Unfamiliar-Scrambled-faces-as-1st-level-contrasts.md renamed to plugins/limo/03.2.--One-way-repeated-measures-ANOVA-revised-Famous-Unfamiliar-Scrambled-faces-as-1st-level-contrasts.md

@@ -6,7 +6,7 @@ render_with_liquid: false
 
 title: Regression at the trial level
 long_title: Regression at the trial level
-nav_order: 17
+nav_order: 16
 ---
 In previous analyses, the repetition levels were either averaged or used as a categorical variable. Here, we instead used the time between each repetition of the same stimulus – thus for a given subject we have 3 conditions (familiar faces, unfamiliar faces and scrambled faces) and one continuous variable (the distance between the repeat of a stimulus type). 
 

plugins/limo/4.-Summary-statistics-Effects-and-Effect-sizes.md renamed to plugins/limo/04.-Summary-statistics-Effects-and-Effect-sizes.md

@@ -8,23 +8,25 @@ render_with_liquid: false
 
 # [Access Scripts reproducing the tutorial](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/tree/master/resources/code)
 
-# [Preprocessing](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/1.-Preprocessing)
+# [Preprocessing](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/01.-Preprocessing)
 - getting the data
 - preprocessing
 
-# [Within Subject Categorical Designs](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/2.-Within-Subject-Categorical-Designs)
-- [One way repeated measures ANOVA (Famous, Unfamiliar, Scrambled faces as conditions)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions)))
-- [One way repeated measures ANOVA revised (Famous, Unfamiliar, Scrambled faces as 1st level contrasts)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts))
-- [Summary statistics to measure and report effects and effect sizes](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/4.-Summary-statistics:-Effects-and-Effect-sizes)
-- [One sample t-test (contrasting Full Faces vs Scrambled Faces at the subject level)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level))
-- [Summary statistics of differences](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/6.-Summary-statistics-of-differences)
-- [Two-ways ANOVA (Faces x Repetition)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/7.-Two-ways-ANOVA-(Faces-x-Repetition))
-- [Paired t-test (Famous vs Unfamiliar)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/8.-Paired-t-test-(Famous-vs-Unfamiliar))
+# Within Subject Categorical Designs
+- [Within Subject Categorical Designs intro](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/02.-Within-Subject-Categorical-Designs)
+- [One way repeated measures ANOVA (Famous, Unfamiliar, Scrambled faces as conditions)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/03.1.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions))
+- [One way repeated measures ANOVA revised (Famous, Unfamiliar, Scrambled faces as 1st level contrasts)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/03.2.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts))
+- [Summary statistics to measure and report effects and effect sizes](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/04.-Summary-statistics:-Effects-and-Effect-sizes)
+- [One sample t-test (contrasting Full Faces vs Scrambled Faces at the subject level)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/05.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level))
+- [Summary statistics of differences](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/06.-Summary-statistics-of-differences)
+- [Two-ways ANOVA (Faces x Repetition)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/07.-Two-ways-ANOVA-(Faces-x-Repetition))
+- [Paired t-test (Famous vs Unfamiliar)](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/08.-Paired-t-test-(Famous-vs-Unfamiliar))
 
-# [Between Subjects Categorical Designs](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/Between-Subjects-Categorical-Designs)
-- [Between subjects’ ANOVAs with repeated factors](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/9.-Between-subjects%E2%80%99-ANOVAs-with-repeated-factors)
+# Between Subjects Analysis
+- [Between subjects’ ANOVAs with repeated factors](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/09.1.-Between-subjects%E2%80%99-ANOVAs-with-repeated-factors)
+- [Between Subjects Categorical Designs](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/09.2.-Between-Subjects-Categorical-Designs)
 - [Two sample t-tests](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/10.-Two-sample-t-tests)
 
-# [Designs with Continuous variables](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/Designs-with-Continuous-variables/)
+# Designs with Continuous variables
 - [Regression among subjects](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/11.-Regression-among-subjects)
 - [Regression at the trial level](https://raw.githubusercontent.com/LIMO-EEG-Toolbox/limo_meeg/wiki/12.-Regression-at-the-trial-level)

plugins/limo/5.-One-sample-t-test-contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level.md renamed to plugins/limo/05.-One-sample-t-test-contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level.md

@@ -37,3 +37,19 @@ You can also email directly or raise a github issue, in particular for bugs.
 Anyone is welcome to contribute ! check here [how you can get involved](https://github.com/LIMO-EEG-Toolbox/limo_eeg/blob/master/contributing.md), the [code of conduct](https://github.com/LIMO-EEG-Toolbox/limo_eeg/blob/master/code_of_conduct.md).
 
 Contributors are listed [here](https://github.com/LIMO-EEG-Toolbox/limo_eeg/blob/master/contributors.md)
+# **Tutorial**
+
+Details on the different functions and usage can be found on the [wiki pages here](https://github.com/LIMO-EEG-Toolbox/limo_tools/wiki) while this is a step-by-step tutorial.
+
+_Important notice_
+
+Whatever you display using LIMO plotting functions, all the variables are returned in the Matlab workspace. For instance, if you plot all channels vs time (ERP results), then the raw statistical map and the significance mask are returned. If you plot a time course, that time course with confidence interval is returned, etc. You may type "who" on the Matlab command line to see these variables.
+
+## Getting started
+
+The tutorial is using [Wakeman and Henson (2015)](https://www.nature.com/articles/sdata20151) face data. In short, famous, unfamiliar and scrambled faces were presented, and repeated immediately or later. Subjects had to do a judgment task orthogonal to the design to keep them engaged. The EEG channels were extracted and preprocessed. [DOWNLOAD THE DATA FOR THIS TUTORIAL HERE](https://openneuro.org/datasets/ds002718/versions/1.0.5) in BIDS format.
+
+This tutorial assumes you are using the latest [EEGLAB](https://github.com/sccn/eeglab) version (2020.0 or later) that uses [STUDY](https://sccn.ucsd.edu/wiki/Chapter_02:_STUDY_Creation) to link with [LIMO tools](https://github.com/LIMO-EEG-Toolbox/limo_tools).
+
+
+