Index

_ README.md: an overview of the project
|___ data: data files used in the project
|___ processed_data: intermediate files from the analysis
|___ results: results of the analyses (data, tables, figures)
|___ R: contains all R-code in the project

SAMexplore

Questionnaire scoring, preprocessing, and explorative analyses on SAM study data

The results of this project are described in: Sep MSC, Joëls M, Geuze E. Individual differences in the encoding of contextual details following acute stress: An explorative study. Eur J Neurosci. 2020; ejn.15067. doi:10.1111/ejn.15067

Note, source only works properly in a R markdown file, if Rstudio > Tools > Global options > R Markdown > evaluate chunks in directory = project. (see) AND if source is not in 'setup' chunk

Scoring of the personality, life events, and state questionnaires

• script: SAM_netq_calculate_scores.RMD and SAM_netq_function_scoring.r
• input:
  • SAM Questionnaire Masterfile.sav
  • SAM_MCT.csv
  • SAM_FGT.csv
  • SAM_Codes_Task_Protocol_Versions.csv
• actions: descriptive statistics MCT and FGT papers of the SAM study and questionnaire scoring:
  • symptoms: SCL90
  • (early) life adversity) CTQ, LSCR
  • Personality: STAI-T, s-TCI, HEXACO
  • emotional state: VAS, STAI-S, PANAS
• output dataset: SAM_netq_scored.rds

Merge questionnaire, endocrine, memory data

• script: SAMexplore_data_merge.R
• input:
  • Questionnaires: SAM_netq_scored.rds
  • Endocrine: sAA_sCORT_condition_dataset.rds (available via this repository)
  • Memory: SAM_MCT.csv & SAM_FGT.csv
• actions: merge data from different sources and calculate context-memory summary scores (neutral, emotional & fearful)
• output: SAM_complete.rds

Imputation of missing values

• script: SAMexplore_imputation.Rmd
• input: SAM_complete.rds
• actions: (passive) imputations of missing values
• output:
  • imputed data: Direct imputation is saved in impTS.rds, Passive imputation is saved in pas.imp.rds
  • combined imputed data: implist.rds

Preprocessing for analyses in the SAM explore project

1. Variable reduction: AUC

• script: SAMexplore_AUC_calculations.RMD
• input:
  • implist.rds
  • SAM_Timepoints_reactivity_measures.csv
• actions: Calculation AUCg and AUCi for repeated measures of emotional/stress state (self-report & endocrine)
• output: SAMimputed_with.AUC.variables.RDS

2. Standardization: Z-scores

• script: SAMexplore_prepare.for.analyses.r
• input: SAMimputed_with.AUC.variables.RDS
• actions:
  • variable selection
  • z-scrore transformation (of all numeric variables)
  • Calculation cumulative stress exposure score Kok & Sep et al. 2016
  • split data per experimental condition
• output:
  • lists with imputed data per experimental condition:
    • No-stress group: imp.nos
    • Immediate stress group: imp.ims
    • Delayed stress group: imp.des
  • functions to prepare boruta/rf data

Descriptive tables

• script: SAMexplore_descriptive_tables.Rmd
• input: processed_data/implist.withRAW11.rds (created with preparation code in SAMexplore_imputation.Rmd, and SAMexplore_AUC_calculations.RMD)
• actions: create a descriptive table (see supporting information in manuscript)
• output: table

Theoretical Model (TM): Linear Regression Models

• scripts: SAMexplore_TM_distributions.Rmd & SAMexplore_TM_assumptions.RMD & SAMexplore_TM_analysis.Rmd & SAMexplore_TM_visualization.Rmd
• input: SAMimputed_with.AUC.variables.RDS via SAMexplore_prepare.for.analyses.r
• actions: inspect variable distributions, assumptions checks, linear models analysis, and the visualization of marginal effects
• output: tables & plots

Random Forest (RF): Boruta Variable Selection & Significance testing & Interpretation

• scripts: SAMexplore_RF_Tuning_Boruta.Rmd & SAMexplore_RF_Boruta_variable.selection.Rmd & SAMexplore_RF_Boruta_interpretation.Rmd
• input: SAMimputed_with.AUC.variables.RDS via SAMexplore_prepare.for.analyses.r
• actions: RF tuning, Boruta variable selection, Random permutation statistics, follow-up (PD and ALE) plots
• output: tables & plots

Model performance evaluation: TM, RF, ensemble (TM+RF)

• script: SAMexplore_model.performance.evaluation.Rmd
• input: SAMimputed_with.AUC.variables.RDS via SAMexplore_prepare.for.analyses.r
• actions: 10 times 5-fold crossvalidated "predictions", to calculate R2 and RMSE of models with:
  1. significant terms TM
  2. selected variables RF/Boruta
  3. combination TM terms & RF variables: linear model with polynomial terms (if required)
• output: tables & plots

Other files

• pseudoData.R was used to create fake pseudo-data for an independent check of the R-code
• SAMexplore_RF_R2_vs_PseudoR2.rmd was used to explore the differences in R2 and pseudo-R2 calculations (also see information in SAMexplore_RF_Boruta_interpretation.Rmd)