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LIMO.cache
Whenever you ask for results at a given threshold and method (say TDCE at p<0.05), an analysis is run to threshold data and get the equivalent under the null (i.e. on bootstrapped data if you have computed them). That result is saved in LIMO.cache, which is primarily intended for quickly redisplaying the results. Note that each time you ask for a new result (a different effect, or the same effect but with a different threshold or method), the cache is overwritten. Only the last result is cached.
LIMO.cache.fig
is the result of the whole space analysis when clicking 'Image all'.
name: 'xxx.mat' % the file you choose to look at
MCC: 2 % the method used 1=None, 2=Cluster, 3=TFCE, 4=max
stats: [32×188 double] % the t or F values
threshold: 0.0500 % the alpha threshold used
pval: [32×188 double] % the (corrected) p-values
mask: [32×188 double] % the mask of significant cells (clusters are numbered)
title: 'XXX t/ F values cluster correction (N clusters)'
LIMO.cache.Courseplot
is the result of the plot done at a given channel for ERP or Power at the subject level
extra: 'Modelled' % data type plotted (original data, modelled or adjusted)
average: [4×750 double] % mean values per regressor
channel: XX % channel selected
regressor: [1 2 3 4] % which regressor were plotted
ci: [4×2×750 double] % 95% CI of each regressor
title: 'Modelled ERP at channel XX (XX)'
- how many clusters?
max(unique(LIMO.cache.fig.mask(:)))
- what is the p-value of cluster N?
LIMO.cache.fig.pval(LIMO.cache.fig.mask == N)
- what is the largest p-value?
max(LIMO.cache.fig.pval(LIMO.cache.fig.mask ~= 0))
- what is the range of significant F/t values?
[min(LIMO.cache.fig.stats(LIMO.cache.fig.mask ~= 0)) max(LIMO.cache.fig.stats(LIMO.cache.fig.mask ~= 0))]
etc ..
You can atually replot using limo_display_image(LIMO,LIMO.cache.fig.stats,LIMO.cache.fig.mask,'your own title,0)
Downsampling or not before analyzing
Defining conditions defining
~ categorical.txt ~continuous.txt
EEGLAB-STUDY: run, session, condition and group
Basic Stats: LIMO tests and CI
Repeated measures ANOVA
Results in the workspace
Results in LIMO.cache
Checking data under the plots
Reordering plots
Compute & Plot conditions
Compute & Plot differences
Channel neighbourhood
Editing a neighbourhood matrix
Scripting 1st level
Debugging 1st level errors
Skip 1st level
Scripting 2nd level
Getting stats results with a script