information and dimensions about files on drive - LIMO-EEG-Toolbox/limo_tools GitHub Wiki
1st level analysis using mass univariate approach
In the subject analysis folder, the data, parameters and results are saved
LIMO.mat
The structure that contains all the information related to the data and the model see https://github.com/LIMO-EEG-Toolbox/limo_eeg/wiki/LIMO-files
Yr.mat
the EEG single trial data reorganized to fit X, that is grouped by conditions if Cat ~=0 dimension [channels x frames x trials]
Yhat.mat
the predicted data dimension [channels x frames x trials]
Beta.mat
the beta values (parameter estimates) dimension [channels x frames x number of parameters in the model (columns of X)]
Res.mat
the residuals (non modelled) data dimension [channels x frames x trials]
R2.mat
the model fit statistic, i.e. percentage of variance explained dimension [channels x frames x R2/F/p values]
Condition_effect_X
refers to a factor effect in categorical designs dimension [channels x frames x F/p values)
Interaction_effect_X
refers to an interaction between factors dimension [channels x frames x F/p values)
Covariate_effect_X
refers to the effect of a continuous regressor dimension [channels x frames x F/p values).
semi_partial_coef_X.mat
refers to the semi-partial coefficient of a factor (defined by LIMO.design.nb_conditions) or a covariate (defined in LIMO.design.nb_continuous) dimension [channels x frames x R2/F/p values].
con_X.mat
refers to a t contrast dimension [channels x frames x cB/standard error/df/t/p values]
ess_X.mat
refers to a F contrast dimension [channels x frames x cB/standard error/df/F/p values].
if bootstrap is selected at import, in the newly created H0 subfolder we have similar files computed under H0 with n bootstrap (by default 800):
boot_table
the resampling table used dimension [number of trials x number of bootstraps]
H0_Betas
dimension [channels x frames x number of parameters in the model (columns of X) x number of bootstraps]
H0_R2
dimension: [channels x frames x R2/F/p values x number of bootstraps]
H0_Condition_effect_X
dimension [channels x frames x F/p values x number of bootstraps]
H0_Interaction_effect_X
dimension [channels x frames x F/p values x number of bootstraps]
H0_Covariate_effect_X
dimension [channels x frames x F/p values x number of bootstraps]
H0_semi_partial_coef_X.mat
dimension [channels x frames x R2/F/p values x number of bootstraps].
H0_con_X.mat
dimension [channels x frames x cB/t/p values x number of bootstraps].
H0_ess_X.mat
dimension [channels x frames x cB/F/p values x number of bootstraps].
if tfce is selected at import, we have similar files as above but computed with tfce:
tfce_R2
dimension: [channels x frames]
tfce_Condition_effect_X
dimension [channels x frames]
tfce_Interaction_effect_X
dimension [channels x frames]
tfce_Covariate_effect_X
dimension [channels x frames]
tfce_semi_partial_coef_X.mat
dimension [channels x frames].
tfce_con_X.mat
dimension [channels x frames].
tfce_ess_X.mat
dimension [channels x frames].
In the H0 subfolder, the tfce score maps under H0
tfce_H0_R2
dimension [channels x frames x number of bootstraps]
tfce_H0_Condition_effect_X
dimension [channels x frames x number of bootstraps]
tfce_H0_Interaction_effect_X
dimension [channels x frames x number of bootstraps]
tfce_H0_Covariate_effect_X
dimension [channels x frames x number of bootstraps]
tfce_H0_semi_partial_coef_X.mat
dimension [channels x frames x number of bootstraps].
tfce_H0_con_X.mat
dimension [channels x frames x number of bootstraps].
tfce_H0_ess_X.mat
dimension [channels x frames x number of bootstraps].
2nd level analysis using mass univariate approach
LIMO.mat
The structure that contains all the information related to the data and the model see https://github.com/LIMO-EEG-Toolbox/limo_eeg/wiki/LIMO-files
One sample t-test
Yr.mat
The data used for the analysis.
one_sample_ttest_parameter_X.mat
Returns the trimmed mean parameter values and associated statistics. Dimensions: [electrodes x frames x 5]. The last dimension codes mean values, standard error, degrees of freedom, t and p.
H0_one_sample_ttest_parameter_X.mat
This file contains the T and p values obtained under H0 for each bootstrap. Dimensions: [electrodes x frames x 2 x number of bootstraps]
tfce_one_sample_ttest_parameter_X.mat
tfce scores of the t-test. Dimensions: [electrodes x frames]
H0_tfce_one_sample_ttest_parameter_X.mat
tfce scores of the t–test under H0. Dimensions: [electrodes x frames x number of bootstraps]
Two samples t-test
Yr1.mat and Yr2.mat
This is the data for each group. Dimensions: [electrodes x frames x nb subjects in group]
two_samples_ttest_parameter_X.mat
Returns the parameter values and associated statistics. Dimensions: [electrodes x frames x 5]. The last dimension codes for trimmed mean differences, standard error, degrees of freedom, t and p.
H0_two_samples_ttest_parameter_X.mat
This file contains the trimmed mean differences, t and p values obtained under H0 for each bootstrap. Dimensions: [electrodes x frames x 3 x number of bootstraps]
tfce_two_samples_ttest_parameter_X.mat
tfce scores of the t-test. Dimensions: [electrodes x frames]
H0_tfce_two_samples_ttest_parameter_X.mat
tfce scores of the t–test under H0. Dimensions: [electrodes x frames x number of bootstraps]
Paired t-test
Yr1.mat and Yr2.mat
This is the data for each parameter. Dimensions: [electrodes x frames x subjects]
paired_samples_ttest_parameter_X.mat
Returns the parameter values and associated statistics. Dimensions: [electrodes x frames x 5]. The last dimension codes for trimmed mean difference, df, standard error, t and p.
H0_paired_samples_ttest_parameter_X.mat
This file contains the trimmed mean differences, T and p values obtained under H0 for each bootstrap. Dimensions: [electrodes x frames x 3 x number of bootstraps]
tfce_paired_samples_ttest_paramater_X.mat
tfce scores of the t-test. Dimensions: [electrodes x frames]
H0_tfce_paired_samples_ttest_parameter_X.mat
tfce scores of the t–test under H0. Dimensions: [electrodes x frames x number of bootstraps]
Regression analysis
Same as for 1st level.
R2.mat
The model fit statistic, i.e. percentage of variance explained. Dimensions: [electrodes x frames x R2/F/p values]
H0_R2.mat
Dimensions: [electrodes x frames x R2/F/p values x number of bootstraps]
Covariate_effect_X.mat
Refers to the effect of a continuous regressor. Dimensions: [electrodes x frames x F/p values).
H0_Covariate_effect_X.mat
Dimensions: [electrodes x frames x F/p values x number of bootstraps]
ANOVA/ANCOVA
Same as for 1st level.
Condition_effect_X.mat
refers to a factor effect in categorical designs dimension [channels x frames x F/p values)
H0_Condition_effect_X.mat
dimension [channels x frames x F/p values x number of bootstraps]
Covariate_effect_X.mat
refers to the effect of a continuous regressor dimension [channels x frames x F/p values).
H0_Covariate_effect_X.mat
dimension [channels x frames x F/p values x number of bootstraps]
repeated measure ANOVA
Yr.mat
Dimensions: [electrodes x frames x subjects x rep measures]
Rep_ANOVA_Factor_X.mat
Dimensions: [electrodes x frames x F/p values]
Rep_ANOVA_Gp_effect.mat
Dimensions: [electrodes, frames, F/p values]
Rep_ANOVA_Interaction_gp_Factor_X.mat
Dimensions: [electrodes x frames x F/p values]
H0_XXX same as above including number of bootstraps as last dimension
e.g., H0_Rep_ANOVA_Factor_X.mat Dimensions: [electrodes x frames x F/ p values x number of bootstraps]