├── .DS_Store ├── .gitignore ├── DataClean ├── .DS_Store ├── BetweenData.RDS ├── Bringmann2013 │ ├── data_Bringmann2013.RDS │ └── data_Bringmann2013_between.RDS ├── Bringmann2016 │ └── data_Bringmann2016.RDS ├── Fisher2017 │ ├── data_Fisher2017.RDS │ └── data_Fisher2017_between.RDS ├── Fried2021 │ ├── data_Fried2021.RDS │ └── data_Fried2021_between.RDS ├── Rowland2020 │ └── data_Rowland2020.RDS ├── Vrijen2018 │ ├── data_Vrijen2018.RDS │ └── data_Vrijen2018_between.RDS └── Wright2017 │ ├── data_Wright2017.RDS │ └── data_Wright2017_between.RDS ├── DataFromAuthors ├── .DS_Store ├── Bringmann2013 │ ├── Bringmann2013.pdf │ └── pone.0060188.s004.txt ├── Bringmann2016 │ ├── Bringmann2016.pdf │ └── Data95.csv ├── Fisher2017 │ ├── .DS_Store │ ├── Fisher et al. (2017).pdf │ └── fisher2017 │ │ ├── .DS_Store │ │ ├── Complete_R_Code.txt │ │ ├── Fisher_In_Press.pdf │ │ ├── LISREL_Automatic_Search_Procedure.txt │ │ ├── Network Model PDFs.zip │ │ ├── Network Model PDFs │ │ ├── Lag-0 Contemporaneous │ │ │ ├── p001_concentration.pdf │ │ │ ├── p003_concentration.pdf │ │ │ ├── p004_concentration.pdf │ │ │ ├── p006_concentration.pdf │ │ │ ├── p007_concentration.pdf │ │ │ ├── p008_concentration.pdf │ │ │ ├── p009_concentration.pdf │ │ │ ├── p010_concentration.pdf │ │ │ ├── p012_concentration.pdf │ │ │ ├── p013_concentration.pdf │ │ │ ├── p014_concentration.pdf │ │ │ ├── p019_concentration.pdf │ │ │ ├── p021_concentration.pdf │ │ │ ├── p023_concentration.pdf │ │ │ ├── p025_concentration.pdf │ │ │ ├── p033_concentration.pdf │ │ │ ├── p037_concentration.pdf │ │ │ ├── p040_concentration.pdf │ │ │ ├── p048_concentration.pdf │ │ │ ├── p068_concentration.pdf │ │ │ ├── p072_concentration.pdf │ │ │ ├── p074_concentration.pdf │ │ │ ├── p075_concentration.pdf │ │ │ ├── p100_concentration.pdf │ │ │ ├── p111_concentration.pdf │ │ │ ├── p113_concentration.pdf │ │ │ ├── p115_concentration.pdf │ │ │ ├── p117_concentration.pdf │ │ │ ├── p127_concentration.pdf │ │ │ ├── p137_concentration.pdf │ │ │ ├── p139_concentration.pdf │ │ │ ├── p145_concentration.pdf │ │ │ ├── p160_concentration.pdf │ │ │ ├── p163_concentration.pdf │ │ │ ├── p169_concentration.pdf │ │ │ ├── p202_concentration.pdf │ │ │ ├── p203_concentration.pdf │ │ │ ├── p204_concentration.pdf │ │ │ ├── p215_concentration.pdf │ │ │ └── p217_concentration.pdf │ │ ├── Residual Contemporaneous │ │ │ ├── p001_residual.pdf │ │ │ ├── p003_residual.pdf │ │ │ ├── p004_residual.pdf │ │ │ ├── p006_residual.pdf │ │ │ ├── p007_residual.pdf │ │ │ ├── p008_residual.pdf │ │ │ ├── p009_residual.pdf │ │ │ ├── p010_residual.pdf │ │ │ ├── p012_residual.pdf │ │ │ ├── p013_residual.pdf │ │ │ ├── p014_residual.pdf │ │ │ ├── p019_residual.pdf │ │ │ ├── p021_residual.pdf │ │ │ ├── p023_residual.pdf │ │ │ ├── p025_residual.pdf │ │ │ ├── p033_residual.pdf │ │ │ ├── p037_residual.pdf │ │ │ ├── p040_residual.pdf │ │ │ ├── p048_residual.pdf │ │ │ ├── p068_residual.pdf │ │ │ ├── p072_residual.pdf │ │ │ ├── p074_residual.pdf │ │ │ ├── p075_residual.pdf │ │ │ ├── p100_residual.pdf │ │ │ ├── p111_residual.pdf │ │ │ ├── p113_residual.pdf │ │ │ ├── p115_residual.pdf │ │ │ ├── p117_residual.pdf │ │ │ ├── p127_residual.pdf │ │ │ ├── p137_residual.pdf │ │ │ ├── p139_residual.pdf │ │ │ ├── p145_residual.pdf │ │ │ ├── p160_residual.pdf │ │ │ ├── p163_residual.pdf │ │ │ ├── p169_residual.pdf │ │ │ ├── p202_residual.pdf │ │ │ ├── p203_residual.pdf │ │ │ ├── p204_residual.pdf │ │ │ ├── p215_residual.pdf │ │ │ └── p217_residual.pdf │ │ └── Temporal │ │ │ ├── p001_directed.pdf │ │ │ ├── p003_directed.pdf │ │ │ ├── p004_directed.pdf │ │ │ ├── p006_directed.pdf │ │ │ ├── p007_directed.pdf │ │ │ ├── p008_directed.pdf │ │ │ ├── p009_directed.pdf │ │ │ ├── p010_directed.pdf │ │ │ ├── p012_directed.pdf │ │ │ ├── p013_directed.pdf │ │ │ ├── p014_directed.pdf │ │ │ ├── p019_directed.pdf │ │ │ ├── p021_directed.pdf │ │ │ ├── p023_directed.pdf │ │ │ ├── p025_directed.pdf │ │ │ ├── p033_directed.pdf │ │ │ ├── p037_directed.pdf │ │ │ ├── p040_directed.pdf │ │ │ ├── p048_directed.pdf │ │ │ ├── p068_directed.pdf │ │ │ ├── p072_directed.pdf │ │ │ ├── p074_directed.pdf │ │ │ ├── p075_directed.pdf │ │ │ ├── p100_directed.pdf │ │ │ ├── p111_directed.pdf │ │ │ ├── p113_directed.pdf │ │ │ ├── p115_directed.pdf │ │ │ ├── p117_directed.pdf │ │ │ ├── p127_directed.pdf │ │ │ ├── p137_directed.pdf │ │ │ ├── p139_directed.pdf │ │ │ ├── p145_directed.pdf │ │ │ ├── p160_directed.pdf │ │ │ ├── p163_directed.pdf │ │ │ ├── p169_directed.pdf │ │ │ ├── p173_directed.pdf │ │ │ ├── p202_directed.pdf │ │ │ ├── p203_directed.pdf │ │ │ ├── p204_directed.pdf │ │ │ ├── p215_directed.pdf │ │ │ └── p217_directed.pdf │ │ ├── R Data.zip │ │ ├── R Data │ │ ├── .DS_Store │ │ ├── P001_final.RData │ │ ├── P009_final.RData │ │ ├── P010_final.RData │ │ ├── P012_final.RData │ │ ├── P013_final.RData │ │ ├── P014_final.RData │ │ ├── P019_final.RData │ │ ├── P021_final.RData │ │ ├── P023_final.RData │ │ ├── P025_final.RData │ │ ├── P033_final.RData │ │ ├── P037_final.RData │ │ ├── P040_final.RData │ │ ├── P048_final.RData │ │ ├── P068_final.RData │ │ ├── P072_final.RData │ │ ├── P074_final.RData │ │ ├── P075_final.RData │ │ ├── P100_final.RData │ │ ├── P111_final.RData │ │ ├── P113_final.RData │ │ ├── P115_final.RData │ │ ├── P117_final.RData │ │ ├── P127_final.RData │ │ ├── P137_final.RData │ │ ├── P139_final.RData │ │ ├── P145_final.RData │ │ ├── P160_final.RData │ │ ├── P163_final.RData │ │ ├── P169_final.RData │ │ ├── P202_final.RData │ │ ├── P203_final.RData │ │ ├── P204_final.RData │ │ ├── P215_final.RData │ │ ├── P217_final.RData │ │ ├── p003_final.RData │ │ ├── p004_final.RData │ │ ├── p006_final.RData │ │ ├── p007_final.RData │ │ └── p008_final.RData │ │ ├── S1_Survey_Items.docx │ │ ├── S2_Cubic_Spline_Simulation.docx │ │ ├── S3_SEM_Simulation.docx │ │ └── SEM Simulations.zip ├── Fried2021 │ ├── .DS_Store │ ├── 1. Preprint │ │ ├── 21677026211017839.pdf │ │ └── Covid19_Preprint.pdf │ ├── 2. Figures │ │ ├── Figure1.pdf │ │ ├── Figure2_hor.pdf │ │ ├── Figure2_ver.pdf │ │ ├── Figure3_diag.pdf │ │ ├── Figure3_nodiag.pdf │ │ ├── Suppl_MLtrends.pdf │ │ ├── Suppl_TS_detrended.pdf │ │ ├── Suppl_TS_items_means.pdf │ │ ├── Suppl_TS_items_ordered.pdf │ │ ├── Suppl_TS_mental.pdf │ │ └── Suppl_TS_social_covid.pdf │ ├── 3. Supplementary document │ │ └── COVID19_Supplementary.pdf │ ├── 4.Data │ │ ├── clean_ema.RData │ │ ├── clean_ema.csv │ │ ├── clean_network.RData │ │ ├── clean_prepost.RData │ │ └── clean_prepost.csv │ ├── 5. Code │ │ ├── .Rproj.user │ │ │ ├── 9B847183 │ │ │ │ ├── pcs │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources │ │ │ │ │ ├── per │ │ │ │ │ └── t │ │ │ │ │ │ ├── 7894D5AB │ │ │ │ │ │ └── 7894D5AB-contents │ │ │ │ │ └── prop │ │ │ │ │ ├── 4FB65BC7 │ │ │ │ │ ├── 52E9D2A3 │ │ │ │ │ └── INDEX │ │ │ └── shared │ │ │ │ └── notebooks │ │ │ │ ├── patch-chunk-names │ │ │ │ └── paths │ │ ├── 2. Timeseries.R │ │ ├── 3. Network models.R │ │ ├── 4. Compare pre-post.R │ │ ├── 5. COVID cases NL.R │ │ ├── 5. Code.Rproj │ │ └── 6. Multilevel regression.R │ └── 6. Measures │ │ ├── Codebook_Baseline.xlsx │ │ ├── Codebook_EMA.xlsx │ │ ├── Codebook_Post.xlsx │ │ ├── Measures_Baseline.pdf │ │ ├── Measures_EMA.pdf │ │ └── Measures_Post.pdf ├── Rowland2020 │ ├── .Rhistory │ ├── .Rproj.user │ │ ├── 9B847183 │ │ │ ├── pcs │ │ │ │ ├── files-pane.pper │ │ │ │ ├── source-pane.pper │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ └── workbench-pane.pper │ │ │ ├── rmd-outputs │ │ │ ├── saved_source_markers │ │ │ └── sources │ │ │ │ ├── per │ │ │ │ └── t │ │ │ │ │ ├── CC97737A │ │ │ │ │ └── CC97737A-contents │ │ │ │ └── prop │ │ │ │ ├── 821EF75F │ │ │ │ ├── BAE3F250 │ │ │ │ └── INDEX │ │ ├── 9DA1CC77 │ │ │ ├── pcs │ │ │ │ ├── files-pane.pper │ │ │ │ ├── source-pane.pper │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ └── workbench-pane.pper │ │ │ ├── rmd-outputs │ │ │ ├── saved_source_markers │ │ │ └── sources │ │ │ │ ├── per │ │ │ │ └── t │ │ │ │ │ ├── 9D893000 │ │ │ │ │ ├── 9D893000-contents │ │ │ │ │ ├── F11886C2 │ │ │ │ │ └── F11886C2-contents │ │ │ │ └── prop │ │ │ │ ├── 8A966CF1 │ │ │ │ ├── D4770C68 │ │ │ │ └── INDEX │ │ └── shared │ │ │ └── notebooks │ │ │ ├── patch-chunk-names │ │ │ └── paths │ ├── Readme_sep19.pdf │ ├── Rowland-Wenzel2020_Article_MindfulnessAndAffect-NetworkDe.pdf │ ├── Rowland2020.Rproj │ ├── Syntax1_IndividualNetworks.R │ ├── Syntax1_IndividualNetworks_OR.R │ ├── Syntax2_NetworkDensity_Mindfulness.do │ ├── betweennetworkOR.pdf │ ├── data_Rowland2020.RDS │ ├── data_affect.csv │ ├── data_net_density.csv │ ├── laggednetwork.pdf │ ├── laggednetworkOR.pdf │ └── residualnetworkOR.pdf ├── Vrijen2018 │ ├── .DS_Store │ ├── Vrijen2018.pdf │ ├── data and syntax main analyses │ │ ├── happybias data for permutation main.csv │ │ ├── highbias data OSF.sav │ │ ├── lowbias data OSF.sav │ │ ├── permutationtest1.r │ │ ├── permutationtest2.r │ │ ├── permutationtest3.r │ │ ├── permutationtest4.r │ │ ├── permutationtest5.r │ │ ├── permutationtest6.r │ │ ├── permutationtest7.r │ │ ├── r_prep_data.r │ │ └── syntax networkpaper main analyses OSF.R │ ├── dataprep_detrending │ │ ├── detrending momentary assessments.do │ │ ├── raw and detrended data.sav │ │ └── rawdata.dta │ └── general │ │ ├── 2018 01 15 Detailed description facial emotion identification morph task.docx │ │ ├── Codebook variables.docx │ │ ├── Data for descriptives.sav │ │ ├── Dataset happy bias selection.sav │ │ └── Flowchart.tif └── Wright2017 │ ├── .DS_Store │ ├── data preparation scripts │ ├── OSF_soed_minrev_sample1.R │ ├── OSF_soed_minrev_sample2.R │ └── OSF_soed_minrev_sample3.R │ ├── data │ ├── ffm1.csv │ ├── ffm2.csv │ ├── ffm3.csv │ ├── iads_sample2_dmgrph.csv │ ├── iads_sample2_ild.csv │ ├── iads_sample3_ild.csv │ ├── sample3_ema.csv │ ├── tbuch_days_items.sav │ ├── tbuch_days_scales.sav │ └── tbuch_scales.sav │ ├── main results script │ └── soed_minrev_main_results.R │ ├── per.2277.pdf │ └── wrightetal2017.pdf ├── EmotionTimeSeries.Rproj ├── ProcessBetween.R ├── ProcessData.R ├── ProcessingFiles ├── Bringmann2013.R ├── Bringmann2016.R ├── Fisher2017.R 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c("start","finish","energetic","enthusiastic","content","irritable","restless","worried","guilty","afraid","anhedonia","angry","hopeless","down","positive","fatigue","tension","concentrate","accepted","threatened","ruminate","avoid_act","reassure","procrast","hours","difficult","unsatisfy","avoid_people") 12 | 13 | ## Duplicate and lag time 14 | lagpad <- function(x, k) { 15 | c(rep(NA, k), x)[1 : length(x)] 16 | } 17 | 18 | data$lag=lagpad(data$start,1) 19 | 20 | ## Calculate time differences 21 | data$tdif=as.numeric(difftime(strptime(data$start,"%m/%d/%Y %H:%M"),strptime(data$lag,"%m/%d/%Y %H:%M"))) 22 | 23 | ## Replace NA 24 | data$tdif[is.na(data$tdif)]<- 0 25 | 26 | ## Calculate cumulative sum of numeric elapsed time 27 | data$cumsumT=cumsum(data$tdif) 28 | 29 | ##Subset data 30 | trim=data[,c(3:18,21:24,28)] 31 | dedat=data.frame(matrix(ncol = dim(trim)[2], nrow = dim(trim[1]))) 32 | colnames(dedat)<-colnames(trim) 33 | 34 | ##Detrend 35 | dedat[,1]=resid(lm(scale(trim[,1])~data$cumsumT,na.action=na.exclude)) 36 | dedat[,2]=resid(lm(scale(trim[,2])~data$cumsumT,na.action=na.exclude)) 37 | dedat[,3]=resid(lm(scale(trim[,3])~data$cumsumT,na.action=na.exclude)) 38 | dedat[,4]=resid(lm(scale(trim[,4])~data$cumsumT,na.action=na.exclude)) 39 | dedat[,5]=resid(lm(scale(trim[,5])~data$cumsumT,na.action=na.exclude)) 40 | dedat[,6]=resid(lm(scale(trim[,6])~data$cumsumT,na.action=na.exclude)) 41 | dedat[,7]=resid(lm(scale(trim[,7])~data$cumsumT,na.action=na.exclude)) 42 | dedat[,8]=resid(lm(scale(trim[,8])~data$cumsumT,na.action=na.exclude)) 43 | dedat[,9]=resid(lm(scale(trim[,9])~data$cumsumT,na.action=na.exclude)) 44 | dedat[,10]=resid(lm(scale(trim[,10])~data$cumsumT,na.action=na.exclude)) 45 | dedat[,11]=resid(lm(scale(trim[,11])~data$cumsumT,na.action=na.exclude)) 46 | dedat[,12]=resid(lm(scale(trim[,12])~data$cumsumT,na.action=na.exclude)) 47 | dedat[,13]=resid(lm(scale(trim[,13])~data$cumsumT,na.action=na.exclude)) 48 | dedat[,14]=resid(lm(scale(trim[,14])~data$cumsumT,na.action=na.exclude)) 49 | dedat[,15]=resid(lm(scale(trim[,15])~data$cumsumT,na.action=na.exclude)) 50 | dedat[,16]=resid(lm(scale(trim[,16])~data$cumsumT,na.action=na.exclude)) 51 | dedat[,17]=resid(lm(scale(trim[,17])~data$cumsumT,na.action=na.exclude)) 52 | dedat[,18]=resid(lm(scale(trim[,18])~data$cumsumT,na.action=na.exclude)) 53 | dedat[,19]=resid(lm(scale(trim[,19])~data$cumsumT,na.action=na.exclude)) 54 | dedat[,20]=resid(lm(scale(trim[,20])~data$cumsumT,na.action=na.exclude)) 55 | dedat[,21]=resid(lm(scale(trim[,21])~data$cumsumT,na.action=na.exclude)) 56 | 57 | ##Apply cubic spline interpolation to resample to even sampling 58 | datcub=data.frame(matrix(ncol=21,nrow=nrow(data))) 59 | datcub[,1]=(spline(x=data$cumsumT, y=dedat[,1],nrow(data),method='fmm'))$y 60 | datcub[,2]=(spline(x=data$cumsumT, y=dedat[,2],nrow(data),method='fmm'))$y 61 | datcub[,3]=(spline(x=data$cumsumT, y=dedat[,3],nrow(data),method='fmm'))$y 62 | datcub[,4]=(spline(x=data$cumsumT, y=dedat[,4],nrow(data),method='fmm'))$y 63 | datcub[,5]=(spline(x=data$cumsumT, y=dedat[,5],nrow(data),method='fmm'))$y 64 | datcub[,6]=(spline(x=data$cumsumT, y=dedat[,6],nrow(data),method='fmm'))$y 65 | datcub[,7]=(spline(x=data$cumsumT, y=dedat[,7],nrow(data),method='fmm'))$y 66 | datcub[,8]=(spline(x=data$cumsumT, y=dedat[,8],nrow(data),method='fmm'))$y 67 | datcub[,9]=(spline(x=data$cumsumT, y=dedat[,9],nrow(data),method='fmm'))$y 68 | datcub[,10]=(spline(x=data$cumsumT, y=dedat[,10],nrow(data),method='fmm'))$y 69 | datcub[,11]=(spline(x=data$cumsumT, y=dedat[,11],nrow(data),method='fmm'))$y 70 | datcub[,12]=(spline(x=data$cumsumT, y=dedat[,12],nrow(data),method='fmm'))$y 71 | datcub[,13]=(spline(x=data$cumsumT, y=dedat[,13],nrow(data),method='fmm'))$y 72 | datcub[,14]=(spline(x=data$cumsumT, y=dedat[,14],nrow(data),method='fmm'))$y 73 | datcub[,15]=(spline(x=data$cumsumT, y=dedat[,15],nrow(data),method='fmm'))$y 74 | datcub[,16]=(spline(x=data$cumsumT, y=dedat[,16],nrow(data),method='fmm'))$y 75 | datcub[,17]=(spline(x=data$cumsumT, y=dedat[,17],nrow(data),method='fmm'))$y 76 | datcub[,18]=(spline(x=data$cumsumT, y=dedat[,18],nrow(data),method='fmm'))$y 77 | datcub[,19]=(spline(x=data$cumsumT, y=dedat[,19],nrow(data),method='fmm'))$y 78 | datcub[,20]=(spline(x=data$cumsumT, y=dedat[,20],nrow(data),method='fmm'))$y 79 | datcub[,21]=(spline(x=data$cumsumT, y=dedat[,21],nrow(data),method='fmm'))$y 80 | 81 | ##Lag function 82 | lagpad <- function(x, k) { 83 | c(rep(NA, k), x)[1 : length(x)] 84 | } 85 | 86 | ##Array of lagged values 87 | lag=datcub 88 | lag[,22]=lagpad(lag[,1]) 89 | lag[,23]=lagpad(lag[,2]) 90 | lag[,24]=lagpad(lag[,3]) 91 | lag[,25]=lagpad(lag[,4]) 92 | lag[,26]=lagpad(lag[,5]) 93 | lag[,27]=lagpad(lag[,6]) 94 | lag[,28]=lagpad(lag[,7]) 95 | lag[,29]=lagpad(lag[,8]) 96 | lag[,30]=lagpad(lag[,9]) 97 | lag[,31]=lagpad(lag[,10]) 98 | lag[,32]=lagpad(lag[,11]) 99 | lag[,33]=lagpad(lag[,12]) 100 | lag[,34]=lagpad(lag[,13]) 101 | lag[,35]=lagpad(lag[,14]) 102 | lag[,36]=lagpad(lag[,15]) 103 | lag[,37]=lagpad(lag[,16]) 104 | lag[,38]=lagpad(lag[,17]) 105 | lag[,39]=lagpad(lag[,18]) 106 | lag[,40]=lagpad(lag[,19]) 107 | lag[,41]=lagpad(lag[,20]) 108 | lag[,42]=lagpad(lag[,21]) 109 | 110 | names=scan("names.txt",what = "character", sep = "\n") 111 | colnames(lag)<-names 112 | lag=lag[-1,] 113 | 114 | #Export data for idiographic SEM 115 | write.table(lag[,c(22:42,1:21)],'lag.txt',row.names=F,col.names=F) 116 | 117 | #Return number of observations for analysis 118 | nrow(lag) 119 | 120 | #Separate code has been provided for conducting idiographic SEM models in LISREL 121 | #LISREL output is then deconstructued into standardized output for 122 | #(a) Psi matrix [1:21,1:21] 123 | #(b) Psi matrix [22:42,22:42] 124 | #(c) Beta matrix [2:42,1:21] 125 | #These provide input matrices for 126 | #(a) Lag-0 correlations at time t 127 | #(b) Residual correlations at time t+1 128 | #(c) Lagged regression structure 129 | 130 | #Read in lagged regression structure 131 | beta_input = read.csv("beta.csv", sep=",", row.names=1) 132 | beta=as.matrix(beta_input) 133 | 134 | #Transpose for temporal model 135 | betamatrix=t(beta) 136 | 137 | #Read in/prep lag-0 correlations (t) and residual correlations (t+1) 138 | t_input = read.csv("t.csv", sep=",", row.names=1) 139 | tmat=as.matrix(t_input) 140 | tmatrix=as.matrix(forceSymmetric(t(tmat))) 141 | 142 | t1_input = read.csv("t1.csv", sep=",", row.names=1) 143 | t1mat=as.matrix(t1_input) 144 | t1matrix=as.matrix(forceSymmetric(t(t1mat))) 145 | 146 | #Read in node labels 147 | names.new<-scan("names_new.txt",what = "character", sep = "\n") 148 | 149 | #Generate graphs 150 | lag0=qgraph(tmatrix,layout='spring',graph='glasso',sampleSize=nrow(lag),cut=0.1,maximum=1,minimum=0,esize=25,vsize=6.5,labels=names.new,color=c("darkorange"),label.cex=.75,label.scale=F,aspect=T,legend=F,tuning=0) 151 | residual=qgraph(t1matrix,layout='spring',graph='glasso',sampleSize=nrow(lag),cut=0.1,maximum=1,minimum=0,esize=25,vsize=6.5,labels=names.new,color=c("steelblue1"), label.cex=.75,label.scale=F,aspect=T,legend=F,tuning=0) 152 | temporal=qgraph(betamatrix,layout='spring', cut=0.1,maximum=1,minimum=0,esize=15,vsize=6.5, labels=names.new, label.cex=.75, label.scale=F, aspect=T, legend=F) 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | -------------------------------------------------------------------------------- /DataFromAuthors/Fisher2017/fisher2017/Fisher_In_Press.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Fisher2017/fisher2017/Fisher_In_Press.pdf -------------------------------------------------------------------------------- /DataFromAuthors/Fisher2017/fisher2017/Network Model PDFs.zip: 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COVID cases NL.R: -------------------------------------------------------------------------------- 1 | ################################################################################## 2 | # # 3 | # COVID-19 student mental health study # 4 | # to be published in Clinical Psychological Science, 2021 # 5 | # # 6 | # Eiko Fried, eikofried@gmail.com # 7 | # with code from Faidra Papanikolaou & Sacha Epskamp # 8 | # # 9 | # Last updated: April 13 2021 # 10 | # (date of paper acceptance) # 11 | # # 12 | # Part V: COVID cases in the Netherlands # 13 | # # 14 | ################################################################################## 15 | 16 | 17 | 18 | # ------------------------------------------------------------------------- 19 | # --------------- 1. Loading packages & Data ------------------------------ 20 | # ------------------------------------------------------------------------- 21 | 22 | library("ggplot2") 23 | library("viridis") 24 | 25 | figs <- "./figures/" # figure directory 26 | datapath <- "./data/" # data directory 27 | 28 | 29 | 30 | # ------------------------------------------------------------------------- 31 | # --------------- 2. March 2020 cases, deaths, and transit requests ------- 32 | # ------------------------------------------------------------------------- 33 | 34 | 35 | # data for deaths and cases from https://www.worldometers.info/coronavirus/country/netherlands/ 36 | # accessed April 12th 2020 37 | 38 | # data for mobility requests from https://www.apple.com/covid19/mobility 39 | # accessed April 18th 2020 40 | 41 | 42 | 43 | # period: march 01 to 31 44 | totalNL <- c(10,18,23,38,83, 45 | 128,188,265,321,382, 46 | 503,614,804,959,1135, 47 | 1413,1705,2051,2460,2994, 48 | 3631,4204,4749,5560,6412, 49 | 7431,8603,9762,10866,11750, 50 | 12595) 51 | deathsNL <- c(0,0,0,0,0, 52 | 1,1,8,4,4, 53 | 5,5,10,12,20, 54 | 24,43,58,76,106, 55 | 136,179,213,276,356, 56 | 434,546,639,771,864, 57 | 1038) 58 | transit <- c(102.7,105.5,101.4,102.3,108.8, 59 | 122.0,119.5,101.4,98.01,94.36, 60 | 90.51,77.33,69.44,61.84,43.19, 61 | 32.44,27.85,24.39,22.51,22.32, 62 | 21.25,18.55,19.1,19.48,20.08, 63 | 20.37,20.62,19.38,17.62,20.2, 64 | 20.92) 65 | transit <- transit*100 66 | 67 | df_case <- as.data.frame(matrix(NA, nrow=62, ncol=3)) 68 | df_case[c(1:31),1] <- totalNL 69 | df_case[c(32:62),1] <- deathsNL 70 | df_case[c(63:93),1] <- transit 71 | df_case[c(1:31),2] <- "Cases" 72 | df_case[c(32:62),2] <- "Deaths" 73 | df_case[c(63:93),2] <- "Apple Maps \nrequests (%)" 74 | df_case[c(1:31),3] <- df_case[c(32:62),3] <- df_case[c(63:93),3] <-c(1:31) 75 | 76 | colnames(df_case) <- c("People", "Type", "Time") 77 | 78 | pdf(paste0(figs, "FIG_casesdeathsNL.pdf"), width=5.5, height=3.5) 79 | ggplot(df_case, aes(Time, People, color=factor(Type))) + 80 | xlab("Days in March 2020") + ylab("") + 81 | labs(color="COVID-19") + 82 | ylim(-500, 12600) + 83 | geom_vline(aes(xintercept = 16), color='#777777', alpha=1, linetype=11, size=.3) + 84 | geom_vline(aes(xintercept = 29), color='#777777', alpha=1, linetype=11, size=.3) + 85 | geom_rect(aes(xmin=16,xmax=29,ymin=-Inf,ymax=Inf), 86 | fill="#eeeeee", alpha=.1, color=NA) + 87 | annotate("text", x=27.7, y=12000, hjust = 1, size=2.5, color='#444444', 88 | label="14 days of Ecological \nMomentary Assessment") + 89 | annotate("text", x=.5, y=9800, hjust = 0, size=2.5, color=viridis(9)[8], 90 | label="100%") + 91 | annotate("text", x=12.6, y=5200, hjust = 0, size=2.5, color=viridis(9)[8], 92 | label="50%") + 93 | theme_light() + 94 | theme(legend.title = element_blank()) + 95 | theme(panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank(), 96 | panel.grid.major.y = element_blank(), panel.grid.major.x = element_blank()) + 97 | scale_color_viridis_d(begin=0.3, end=.9, alpha=1, direction=-1) + 98 | geom_hline(yintercept=c(0,5000,10000),colour="grey70", size=.2) + 99 | geom_point(alpha=.9, shape=16) + 100 | geom_line(alpha=0.4) 101 | dev.off() 102 | -------------------------------------------------------------------------------- /DataFromAuthors/Fried2021/5. 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Measures/Measures_Baseline.pdf -------------------------------------------------------------------------------- /DataFromAuthors/Fried2021/6. Measures/Measures_EMA.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Fried2021/6. Measures/Measures_EMA.pdf -------------------------------------------------------------------------------- /DataFromAuthors/Fried2021/6. Measures/Measures_Post.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Fried2021/6. Measures/Measures_Post.pdf -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rhistory: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Rowland2020/.Rhistory -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9B847183/pcs/files-pane.pper: -------------------------------------------------------------------------------- 1 | { 2 | "sortOrder": [ 3 | { 4 | "columnIndex": 2, 5 | "ascending": true 6 | } 7 | ], 8 | "path": "C:/Users/ryano/Dropbox/BerkThoseEmotions/Data/Rowland2020" 9 | } -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9B847183/pcs/source-pane.pper: -------------------------------------------------------------------------------- 1 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9B847183/saved_source_markers: -------------------------------------------------------------------------------- 1 | {"active_set":"","sets":[]} -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9B847183/sources/per/t/CC97737A: -------------------------------------------------------------------------------- 1 | { 2 | "id": "CC97737A", 3 | "path": "C:/Users/ryano/Dropbox/BerkThoseEmotions/Data/Rowland2020/Syntax1_IndividualNetworks_OR.R", 4 | "project_path": "Syntax1_IndividualNetworks_OR.R", 5 | "type": "r_source", 6 | "hash": "102701963", 7 | "contents": "", 8 | "dirty": false, 9 | "created": 1634740275854.0, 10 | "source_on_save": false, 11 | "relative_order": 1, 12 | "properties": { 13 | "cursorPosition": "112,9", 14 | "scrollLine": "84" 15 | }, 16 | "folds": "", 17 | "lastKnownWriteTime": 1634740760, 18 | "encoding": "UTF-8", 19 | "collab_server": "", 20 | "source_window": "", 21 | "last_content_update": 1634740760074, 22 | "read_only": false, 23 | "read_only_alternatives": [] 24 | } -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9B847183/sources/per/t/CC97737A-contents: -------------------------------------------------------------------------------- 1 | install.packages("qgraph") 2 | install.packages("graphicalVAR") 3 | 4 | library("qgraph") 5 | library("graphicalVAR") 6 | 7 | getwd() 8 | 9 | ###include path 10 | getwd() 11 | setwd("") 12 | 13 | ### outcome names 14 | outnames <- c("happy", "excited", "relaxed", "satisfied", "angry","anxious", "depressed", "sad") 15 | dat <- read.table("data_affect.csv", sep=",", header=TRUE, na.strings="") 16 | ##sort data 17 | dat <- dat[order(dat$subjno, dat$group, dat$dayno, dat$beep),] 18 | names(dat)[pmatch(c("emo1_m", "emo2_m", "emo3_m", "emo4_m", "emo5_m", "emo6_m", "emo7_m", "emo8_m"), 19 | names(dat))] <- outnames 20 | 21 | ## modeling temporal networks 22 | myData_ind_dat<- mlGraphicalVAR(dat, vars = c("happy", "excited", "relaxed", "satisfied", "angry", "anxious", "depressed", "sad"), dayvar = "dayno", idvar = "subjno",lags = 1, beepvar = "beep", subjectNetworks = TRUE, centerWithin = TRUE, gamma = 0, lambda_kappa = 0, lambda_beta = 0) 23 | ID = myData_ind_dat$ids 24 | 25 | myData_ind_dat$fixedResults$N 26 | 27 | ## extracting individuals' temporal network density 28 | t_oad=c() 29 | for (i in 1:125){ 30 | t_oad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:8]))} 31 | 32 | t_pad=c() 33 | for (i in 1:125){ 34 | t_pad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:4]))} 35 | 36 | t_nad=c() 37 | for (i in 1:125){ 38 | t_nad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:8]))} 39 | 40 | 41 | 42 | ## extracting individuals' contemporaneous network density 43 | c_oad=c() 44 | for (i in 1:125){ 45 | c_oad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:8, 1:8]))))/2/28} 46 | 47 | c_pad=c() 48 | for (i in 1:125){ 49 | c_pad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:4, 1:4]))))/2/6} 50 | 51 | c_nad=c() 52 | for (i in 1:125){ 53 | c_nad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][5:8, 5:8]))))/2/6} 54 | 55 | c_pa_nad=c() 56 | for (i in 1:125){ 57 | c_pa_nad[i] <- ((mean(abs(myData_ind_dat$subjectPCC[[i]][1:4, 5:8]))))} 58 | 59 | options(max.print = 99999999) 60 | 61 | 62 | ##extracting temporal single-affect network densities 63 | t_happy=c() 64 | for (i in 1:125){ 65 | t_happy[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:1]))} 66 | 67 | t_excited=c() 68 | for (i in 1:125){ 69 | t_excited[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 2:2]))} 70 | 71 | t_relaxed=c() 72 | for (i in 1:125){ 73 | t_relaxed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 3:3]))} 74 | 75 | t_satisfied=c() 76 | for (i in 1:125){ 77 | t_satisfied[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 4:4]))} 78 | 79 | t_angry=c() 80 | for (i in 1:125){ 81 | t_angry[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:5]))} 82 | 83 | t_anxious=c() 84 | for (i in 1:125){ 85 | t_anxious[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 6:6]))} 86 | 87 | t_depressed=c() 88 | for (i in 1:125){ 89 | t_depressed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 7:7]))} 90 | 91 | t_sad=c() 92 | for (i in 1:125){ 93 | t_sad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 8:8]))} 94 | 95 | 96 | ## combining all individual network densities 97 | all_matrix <- cbind(ID, t_oad, t_pad, t_nad, c_oad, c_pad, c_nad, c_pa_nad, t_happy, t_excited, t_relaxed, t_satisfied, t_angry, t_anxious, t_depressed, t_sad) 98 | write.csv(all_matrix, "all_matrix.csv") 99 | 100 | 101 | 102 | ## population networks: Figure 1 103 | pdf("laggednetworkOR.pdf") 104 | qgraph(myData_ind_dat$fixedPDC, layout = "circle", edge.labels = TRUE, theme = "colorblind", fade = T) 105 | dev.off() 106 | 107 | pdf("residualnetworkOR.pdf") 108 | qgraph(myData_ind_dat$fixedPCC, layout = "circle", edge.labels = T,theme = "colorblind") 109 | dev.off() 110 | 111 | pdf("betweennetworkOR.pdf") 112 | qgraph(myData_ind_dat$betweenNet, layout = "circle", theme = "gray") 113 | dev.off() -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9B847183/sources/prop/821EF75F: -------------------------------------------------------------------------------- 1 | { 2 | "cursorPosition": "101,0", 3 | "scrollLine": "85" 4 | } -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9B847183/sources/prop/BAE3F250: -------------------------------------------------------------------------------- 1 | { 2 | "cursorPosition": "112,9", 3 | "scrollLine": "84" 4 | } 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-------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/rmd-outputs: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/saved_source_markers: -------------------------------------------------------------------------------- 1 | {"active_set":"","sets":[]} -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/sources/per/t/9D893000: -------------------------------------------------------------------------------- 1 | { 2 | "id": "9D893000", 3 | "path": "~/Dropbox/MyData/_PhD/__projects/BerkThoseEmotions/Data/Rowland2020/Syntax1_IndividualNetworks.R", 4 | "project_path": "Syntax1_IndividualNetworks.R", 5 | "type": "r_source", 6 | "hash": "509169021", 7 | "contents": "", 8 | "dirty": false, 9 | "created": 1634822052458.0, 10 | "source_on_save": false, 11 | "relative_order": 1, 12 | "properties": {}, 13 | "folds": "", 14 | "lastKnownWriteTime": 1634732926, 15 | "encoding": "UTF-8", 16 | "collab_server": "", 17 | "source_window": "", 18 | "last_content_update": 1634732926, 19 | "read_only": false, 20 | "read_only_alternatives": [] 21 | } -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/sources/per/t/9D893000-contents: -------------------------------------------------------------------------------- 1 | install.packages("qgraph") 2 | install.packages("graphicalVAR") 3 | 4 | library("qgraph") 5 | library("graphicalVAR") 6 | 7 | getwd() 8 | 9 | ###include path 10 | getwd() 11 | setwd("") 12 | 13 | ### outcome names 14 | outnames <- c("happy", "excited", "relaxed", "satisfied", "angry","anxious", "depressed", "sad") 15 | dat <- read.table("data_affect.csv", sep=",", header=TRUE, na.strings="") 16 | ##sort data 17 | dat <- dat[order(dat$subjno, dat$group, dat$dayno, dat$beep),] 18 | names(dat)[pmatch(c("emo1_m", "emo2_m", "emo3_m", "emo4_m", "emo5_m", "emo6_m", "emo7_m", "emo8_m"), 19 | names(dat))] <- outnames 20 | 21 | ## modeling temporal networks 22 | myData_ind_dat<- mlGraphicalVAR(dat, vars = c("happy", "excited", "relaxed", "satisfied", "angry", "anxious", "depressed", "sad"), dayvar = "dayno", idvar = "subjno",lags = 1, beepvar = "beep", subjectNetworks = TRUE, centerWithin = TRUE, gamma = 0, lambda_kappa = 0, lambda_beta = 0) 23 | ID = myData_ind_dat$ids 24 | 25 | myData_ind_dat$fixedResults$N 26 | 27 | ## extracting individuals' temporal network density 28 | t_oad=c() 29 | for (i in 1:125){ 30 | t_oad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:8]))} 31 | 32 | t_pad=c() 33 | for (i in 1:125){ 34 | t_pad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:4]))} 35 | 36 | t_nad=c() 37 | for (i in 1:125){ 38 | t_nad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:8]))} 39 | 40 | 41 | 42 | ## extracting individuals' contemporaneous network density 43 | c_oad=c() 44 | for (i in 1:125){ 45 | c_oad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:8, 1:8]))))/2/28} 46 | 47 | c_pad=c() 48 | for (i in 1:125){ 49 | c_pad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:4, 1:4]))))/2/6} 50 | 51 | c_nad=c() 52 | for (i in 1:125){ 53 | c_nad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][5:8, 5:8]))))/2/6} 54 | 55 | c_pa_nad=c() 56 | for (i in 1:125){ 57 | c_pa_nad[i] <- ((mean(abs(myData_ind_dat$subjectPCC[[i]][1:4, 5:8]))))} 58 | 59 | options(max.print = 99999999) 60 | 61 | 62 | ##extracting temporal single-affect network densities 63 | t_happy=c() 64 | for (i in 1:125){ 65 | t_happy[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:1]))} 66 | 67 | t_excited=c() 68 | for (i in 1:125){ 69 | t_excited[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 2:2]))} 70 | 71 | t_relaxed=c() 72 | for (i in 1:125){ 73 | t_relaxed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 3:3]))} 74 | 75 | t_satisfied=c() 76 | for (i in 1:125){ 77 | t_satisfied[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 4:4]))} 78 | 79 | t_angry=c() 80 | for (i in 1:125){ 81 | t_angry[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:5]))} 82 | 83 | t_anxious=c() 84 | for (i in 1:125){ 85 | t_anxious[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 6:6]))} 86 | 87 | t_depressed=c() 88 | for (i in 1:125){ 89 | t_depressed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 7:7]))} 90 | 91 | t_sad=c() 92 | for (i in 1:125){ 93 | t_sad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 8:8]))} 94 | 95 | 96 | ## combining all individual network densities 97 | all_matrix <- cbind(ID, t_oad, t_pad, t_nad, c_oad, c_pad, c_nad, c_pa_nad, t_happy, t_excited, t_relaxed, t_satisfied, t_angry, t_anxious, t_depressed, t_sad) 98 | write.csv(all_matrix, "all_matrix.csv") 99 | 100 | 101 | 102 | ## population networks: Figure 1 103 | qgraph(myData_ind_dat$fixedPDC, layout = "circle", theme = "gray") 104 | 105 | qgraph(myData_ind_dat$fixedPCC, layout = "circle", theme = "gray") 106 | 107 | qgraph(myData_ind_dat$betweenNet, layout = "circle", theme = "gray") 108 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/sources/per/t/F11886C2: -------------------------------------------------------------------------------- 1 | { 2 | "id": "F11886C2", 3 | "path": "~/Dropbox/MyData/_PhD/__projects/BerkThoseEmotions/Data/Rowland2020/Syntax1_IndividualNetworks_OR.R", 4 | "project_path": "Syntax1_IndividualNetworks_OR.R", 5 | "type": "r_source", 6 | "hash": "102701963", 7 | "contents": "", 8 | "dirty": false, 9 | "created": 1634822069972.0, 10 | "source_on_save": false, 11 | "relative_order": 2, 12 | "properties": { 13 | "cursorPosition": "12,0", 14 | "scrollLine": "0" 15 | }, 16 | "folds": "", 17 | "lastKnownWriteTime": 1634740760, 18 | "encoding": "UTF-8", 19 | "collab_server": "", 20 | "source_window": "", 21 | "last_content_update": 1634740760, 22 | "read_only": false, 23 | "read_only_alternatives": [] 24 | } -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/sources/per/t/F11886C2-contents: -------------------------------------------------------------------------------- 1 | install.packages("qgraph") 2 | install.packages("graphicalVAR") 3 | 4 | library("qgraph") 5 | library("graphicalVAR") 6 | 7 | getwd() 8 | 9 | ###include path 10 | getwd() 11 | setwd("") 12 | 13 | ### outcome names 14 | outnames <- c("happy", "excited", "relaxed", "satisfied", "angry","anxious", "depressed", "sad") 15 | dat <- read.table("data_affect.csv", sep=",", header=TRUE, na.strings="") 16 | ##sort data 17 | dat <- dat[order(dat$subjno, dat$group, dat$dayno, dat$beep),] 18 | names(dat)[pmatch(c("emo1_m", "emo2_m", "emo3_m", "emo4_m", "emo5_m", "emo6_m", "emo7_m", "emo8_m"), 19 | names(dat))] <- outnames 20 | 21 | ## modeling temporal networks 22 | myData_ind_dat<- mlGraphicalVAR(dat, vars = c("happy", "excited", "relaxed", "satisfied", "angry", "anxious", "depressed", "sad"), dayvar = "dayno", idvar = "subjno",lags = 1, beepvar = "beep", subjectNetworks = TRUE, centerWithin = TRUE, gamma = 0, lambda_kappa = 0, lambda_beta = 0) 23 | ID = myData_ind_dat$ids 24 | 25 | myData_ind_dat$fixedResults$N 26 | 27 | ## extracting individuals' temporal network density 28 | t_oad=c() 29 | for (i in 1:125){ 30 | t_oad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:8]))} 31 | 32 | t_pad=c() 33 | for (i in 1:125){ 34 | t_pad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:4]))} 35 | 36 | t_nad=c() 37 | for (i in 1:125){ 38 | t_nad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:8]))} 39 | 40 | 41 | 42 | ## extracting individuals' contemporaneous network density 43 | c_oad=c() 44 | for (i in 1:125){ 45 | c_oad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:8, 1:8]))))/2/28} 46 | 47 | c_pad=c() 48 | for (i in 1:125){ 49 | c_pad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:4, 1:4]))))/2/6} 50 | 51 | c_nad=c() 52 | for (i in 1:125){ 53 | c_nad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][5:8, 5:8]))))/2/6} 54 | 55 | c_pa_nad=c() 56 | for (i in 1:125){ 57 | c_pa_nad[i] <- ((mean(abs(myData_ind_dat$subjectPCC[[i]][1:4, 5:8]))))} 58 | 59 | options(max.print = 99999999) 60 | 61 | 62 | ##extracting temporal single-affect network densities 63 | t_happy=c() 64 | for (i in 1:125){ 65 | t_happy[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:1]))} 66 | 67 | t_excited=c() 68 | for (i in 1:125){ 69 | t_excited[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 2:2]))} 70 | 71 | t_relaxed=c() 72 | for (i in 1:125){ 73 | t_relaxed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 3:3]))} 74 | 75 | t_satisfied=c() 76 | for (i in 1:125){ 77 | t_satisfied[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 4:4]))} 78 | 79 | t_angry=c() 80 | for (i in 1:125){ 81 | t_angry[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:5]))} 82 | 83 | t_anxious=c() 84 | for (i in 1:125){ 85 | t_anxious[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 6:6]))} 86 | 87 | t_depressed=c() 88 | for (i in 1:125){ 89 | t_depressed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 7:7]))} 90 | 91 | t_sad=c() 92 | for (i in 1:125){ 93 | t_sad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 8:8]))} 94 | 95 | 96 | ## combining all individual network densities 97 | all_matrix <- cbind(ID, t_oad, t_pad, t_nad, c_oad, c_pad, c_nad, c_pa_nad, t_happy, t_excited, t_relaxed, t_satisfied, t_angry, t_anxious, t_depressed, t_sad) 98 | write.csv(all_matrix, "all_matrix.csv") 99 | 100 | 101 | 102 | ## population networks: Figure 1 103 | pdf("laggednetworkOR.pdf") 104 | qgraph(myData_ind_dat$fixedPDC, layout = "circle", edge.labels = TRUE, theme = "colorblind", fade = T) 105 | dev.off() 106 | 107 | pdf("residualnetworkOR.pdf") 108 | qgraph(myData_ind_dat$fixedPCC, layout = "circle", edge.labels = T,theme = "colorblind") 109 | dev.off() 110 | 111 | pdf("betweennetworkOR.pdf") 112 | qgraph(myData_ind_dat$betweenNet, layout = "circle", theme = "gray") 113 | dev.off() -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/sources/prop/8A966CF1: -------------------------------------------------------------------------------- 1 | { 2 | "cursorPosition": "12,0", 3 | "scrollLine": "0" 4 | } -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/sources/prop/D4770C68: -------------------------------------------------------------------------------- 1 | {} -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/9DA1CC77/sources/prop/INDEX: -------------------------------------------------------------------------------- 1 | ~%2FDropbox%2FMyData%2F_PhD%2F__projects%2FBerkThoseEmotions%2FData%2FRowland2020%2FSyntax1_IndividualNetworks.R="D4770C68" 2 | ~%2FDropbox%2FMyData%2F_PhD%2F__projects%2FBerkThoseEmotions%2FData%2FRowland2020%2FSyntax1_IndividualNetworks_OR.R="8A966CF1" 3 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/shared/notebooks/patch-chunk-names: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Rowland2020/.Rproj.user/shared/notebooks/patch-chunk-names -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/.Rproj.user/shared/notebooks/paths: -------------------------------------------------------------------------------- 1 | C:/Users/ryano/Dropbox/BerkThoseEmotions/Data/Rowland2020/Syntax1_IndividualNetworks.R="C3A95269" 2 | C:/Users/ryano/Dropbox/BerkThoseEmotions/Data/Rowland2020/Syntax1_IndividualNetworks_OR.R="3CFBDA81" 3 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/Readme_sep19.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Rowland2020/Readme_sep19.pdf -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/Rowland-Wenzel2020_Article_MindfulnessAndAffect-NetworkDe.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Rowland2020/Rowland-Wenzel2020_Article_MindfulnessAndAffect-NetworkDe.pdf -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/Rowland2020.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/Syntax1_IndividualNetworks.R: -------------------------------------------------------------------------------- 1 | install.packages("qgraph") 2 | install.packages("graphicalVAR") 3 | 4 | library("qgraph") 5 | library("graphicalVAR") 6 | 7 | getwd() 8 | 9 | ###include path 10 | getwd() 11 | setwd("") 12 | 13 | ### outcome names 14 | outnames <- c("happy", "excited", "relaxed", "satisfied", "angry","anxious", "depressed", "sad") 15 | dat <- read.table("data_affect.csv", sep=",", header=TRUE, na.strings="") 16 | ##sort data 17 | dat <- dat[order(dat$subjno, dat$group, dat$dayno, dat$beep),] 18 | names(dat)[pmatch(c("emo1_m", "emo2_m", "emo3_m", "emo4_m", "emo5_m", "emo6_m", "emo7_m", "emo8_m"), 19 | names(dat))] <- outnames 20 | 21 | ## modeling temporal networks 22 | myData_ind_dat<- mlGraphicalVAR(dat, vars = c("happy", "excited", "relaxed", "satisfied", "angry", "anxious", "depressed", "sad"), dayvar = "dayno", idvar = "subjno",lags = 1, beepvar = "beep", subjectNetworks = TRUE, centerWithin = TRUE, gamma = 0, lambda_kappa = 0, lambda_beta = 0) 23 | ID = myData_ind_dat$ids 24 | 25 | myData_ind_dat$fixedResults$N 26 | 27 | ## extracting individuals' temporal network density 28 | t_oad=c() 29 | for (i in 1:125){ 30 | t_oad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:8]))} 31 | 32 | t_pad=c() 33 | for (i in 1:125){ 34 | t_pad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:4]))} 35 | 36 | t_nad=c() 37 | for (i in 1:125){ 38 | t_nad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:8]))} 39 | 40 | 41 | 42 | ## extracting individuals' contemporaneous network density 43 | c_oad=c() 44 | for (i in 1:125){ 45 | c_oad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:8, 1:8]))))/2/28} 46 | 47 | c_pad=c() 48 | for (i in 1:125){ 49 | c_pad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:4, 1:4]))))/2/6} 50 | 51 | c_nad=c() 52 | for (i in 1:125){ 53 | c_nad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][5:8, 5:8]))))/2/6} 54 | 55 | c_pa_nad=c() 56 | for (i in 1:125){ 57 | c_pa_nad[i] <- ((mean(abs(myData_ind_dat$subjectPCC[[i]][1:4, 5:8]))))} 58 | 59 | options(max.print = 99999999) 60 | 61 | 62 | ##extracting temporal single-affect network densities 63 | t_happy=c() 64 | for (i in 1:125){ 65 | t_happy[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:1]))} 66 | 67 | t_excited=c() 68 | for (i in 1:125){ 69 | t_excited[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 2:2]))} 70 | 71 | t_relaxed=c() 72 | for (i in 1:125){ 73 | t_relaxed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 3:3]))} 74 | 75 | t_satisfied=c() 76 | for (i in 1:125){ 77 | t_satisfied[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 4:4]))} 78 | 79 | t_angry=c() 80 | for (i in 1:125){ 81 | t_angry[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:5]))} 82 | 83 | t_anxious=c() 84 | for (i in 1:125){ 85 | t_anxious[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 6:6]))} 86 | 87 | t_depressed=c() 88 | for (i in 1:125){ 89 | t_depressed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 7:7]))} 90 | 91 | t_sad=c() 92 | for (i in 1:125){ 93 | t_sad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 8:8]))} 94 | 95 | 96 | ## combining all individual network densities 97 | all_matrix <- cbind(ID, t_oad, t_pad, t_nad, c_oad, c_pad, c_nad, c_pa_nad, t_happy, t_excited, t_relaxed, t_satisfied, t_angry, t_anxious, t_depressed, t_sad) 98 | write.csv(all_matrix, "all_matrix.csv") 99 | 100 | 101 | 102 | ## population networks: Figure 1 103 | qgraph(myData_ind_dat$fixedPDC, layout = "circle", theme = "gray") 104 | 105 | qgraph(myData_ind_dat$fixedPCC, layout = "circle", theme = "gray") 106 | 107 | qgraph(myData_ind_dat$betweenNet, layout = "circle", theme = "gray") 108 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/Syntax1_IndividualNetworks_OR.R: -------------------------------------------------------------------------------- 1 | install.packages("qgraph") 2 | install.packages("graphicalVAR") 3 | 4 | library("qgraph") 5 | library("graphicalVAR") 6 | 7 | getwd() 8 | 9 | ###include path 10 | getwd() 11 | setwd("") 12 | 13 | ### outcome names 14 | outnames <- c("happy", "excited", "relaxed", "satisfied", "angry","anxious", "depressed", "sad") 15 | dat <- read.table("data_affect.csv", sep=",", header=TRUE, na.strings="") 16 | ##sort data 17 | dat <- dat[order(dat$subjno, dat$group, dat$dayno, dat$beep),] 18 | names(dat)[pmatch(c("emo1_m", "emo2_m", "emo3_m", "emo4_m", "emo5_m", "emo6_m", "emo7_m", "emo8_m"), 19 | names(dat))] <- outnames 20 | 21 | ## modeling temporal networks 22 | myData_ind_dat<- mlGraphicalVAR(dat, vars = c("happy", "excited", "relaxed", "satisfied", "angry", "anxious", "depressed", "sad"), dayvar = "dayno", idvar = "subjno",lags = 1, beepvar = "beep", subjectNetworks = TRUE, centerWithin = TRUE, gamma = 0, lambda_kappa = 0, lambda_beta = 0) 23 | ID = myData_ind_dat$ids 24 | 25 | myData_ind_dat$fixedResults$N 26 | 27 | ## extracting individuals' temporal network density 28 | t_oad=c() 29 | for (i in 1:125){ 30 | t_oad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:8]))} 31 | 32 | t_pad=c() 33 | for (i in 1:125){ 34 | t_pad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:4]))} 35 | 36 | t_nad=c() 37 | for (i in 1:125){ 38 | t_nad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:8]))} 39 | 40 | 41 | 42 | ## extracting individuals' contemporaneous network density 43 | c_oad=c() 44 | for (i in 1:125){ 45 | c_oad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:8, 1:8]))))/2/28} 46 | 47 | c_pad=c() 48 | for (i in 1:125){ 49 | c_pad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][1:4, 1:4]))))/2/6} 50 | 51 | c_nad=c() 52 | for (i in 1:125){ 53 | c_nad[i] <- ((sum(abs(myData_ind_dat$subjectPCC[[i]][5:8, 5:8]))))/2/6} 54 | 55 | c_pa_nad=c() 56 | for (i in 1:125){ 57 | c_pa_nad[i] <- ((mean(abs(myData_ind_dat$subjectPCC[[i]][1:4, 5:8]))))} 58 | 59 | options(max.print = 99999999) 60 | 61 | 62 | ##extracting temporal single-affect network densities 63 | t_happy=c() 64 | for (i in 1:125){ 65 | t_happy[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 1:1]))} 66 | 67 | t_excited=c() 68 | for (i in 1:125){ 69 | t_excited[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 2:2]))} 70 | 71 | t_relaxed=c() 72 | for (i in 1:125){ 73 | t_relaxed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 3:3]))} 74 | 75 | t_satisfied=c() 76 | for (i in 1:125){ 77 | t_satisfied[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 4:4]))} 78 | 79 | t_angry=c() 80 | for (i in 1:125){ 81 | t_angry[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 5:5]))} 82 | 83 | t_anxious=c() 84 | for (i in 1:125){ 85 | t_anxious[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 6:6]))} 86 | 87 | t_depressed=c() 88 | for (i in 1:125){ 89 | t_depressed[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 7:7]))} 90 | 91 | t_sad=c() 92 | for (i in 1:125){ 93 | t_sad[i] <- mean(abs(myData_ind_dat$subjectPDC[[i]][1:8, 8:8]))} 94 | 95 | 96 | ## combining all individual network densities 97 | all_matrix <- cbind(ID, t_oad, t_pad, t_nad, c_oad, c_pad, c_nad, c_pa_nad, t_happy, t_excited, t_relaxed, t_satisfied, t_angry, t_anxious, t_depressed, t_sad) 98 | write.csv(all_matrix, "all_matrix.csv") 99 | 100 | 101 | 102 | ## population networks: Figure 1 103 | pdf("laggednetworkOR.pdf") 104 | qgraph(myData_ind_dat$fixedPDC, layout = "circle", edge.labels = TRUE, theme = "colorblind", fade = T) 105 | dev.off() 106 | 107 | pdf("residualnetworkOR.pdf") 108 | qgraph(myData_ind_dat$fixedPCC, layout = "circle", edge.labels = T,theme = "colorblind") 109 | dev.off() 110 | 111 | pdf("betweennetworkOR.pdf") 112 | qgraph(myData_ind_dat$betweenNet, layout = "circle", theme = "gray") 113 | dev.off() -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/Syntax2_NetworkDensity_Mindfulness.do: -------------------------------------------------------------------------------- 1 | import delimited "data_net_density.csv", clear 2 | 3 | 4 | ///Table 1 5 | ///Intercorrelations 6 | pwcorr t_oad t_nad t_pad smaas, sig 7 | 8 | 9 | ///Table 2 10 | ///Hierarchical mutiple regression: temporal overall affect network density 11 | nestreg: reg t_oad (gc_smaas group) (mind) /// 12 | (smaas_mind group_mind), beta 13 | 14 | 15 | ///Table 3 16 | ///Hierarchical mutiple regression: temporal positive affect network density 17 | nestreg: reg t_pad (gc_smaas group) (mind) /// 18 | (smaas_mind group_mind), beta 19 | 20 | ///Hierarchical mutiple regression: temporal negative affect network density 21 | nestreg: reg t_nad (gc_smaas group) (mind) /// 22 | (smaas_mind group_mind), beta 23 | 24 | 25 | ///Figure 2 26 | regress t_oad c.gc_smaas i.group i.mind c.gc_smaas#i.mind i.group#i.mind, beta 27 | margins mind, at(gc_smaas=(-.879 .879)) 28 | marginsplot 29 | 30 | 31 | ///Supplementary material 32 | ///Table S1 33 | ///Intercorrelations 34 | pwcorr t_oad t_nad t_pad c_oad c_pad c_nad c_pa_nad, sig 35 | 36 | ///Table S2 37 | ///Hierarchical mutiple regression: contemporaneous overall affect network density 38 | nestreg: reg c_oad (gc_smaas group) (group mind) /// 39 | (smaas_mind group_mind), beta 40 | 41 | ///Table S3 42 | ///Hierarchical mutiple regression: contemporaneous positive affect network density 43 | nestreg: reg c_pad (gc_smaas group) (mind) /// 44 | (smaas_mind group_mind), beta 45 | 46 | ///Hierarchical mutiple regression: contemporaneous negative affect network density 47 | nestreg: reg c_nad (gc_smaas group) (mind) /// 48 | (smaas_mind group_mind), beta 49 | 50 | 51 | ///Table S4 52 | ///Hierarchical mutiple regressions: contemporaneous positive-negative affect network density 53 | nestreg: reg c_pa_nad (gc_smaas group) (mind) /// 54 | (smaas_mind group_mind), beta 55 | 56 | 57 | ///Table S5 58 | ///Alternative meditation variable 59 | nestreg: reg t_oad (group gc_smaas) (both_mind) /// 60 | (group_both_mind smaas_both_mind), beta 61 | 62 | ///Table S6 63 | ///Alternative meditation variable 64 | nestreg: reg t_pad (group gc_smaas) (both_mind) /// 65 | (group_both_mind smaas_both_mind ), beta 66 | 67 | nestreg: reg t_nad (group gc_smaas) (both_mind) /// 68 | (group_both_mind smaas_both_mind ), beta 69 | 70 | 71 | ///Table S7 72 | ///Hierarchical mutiple regressions: single-affect density 73 | nestreg: reg t_happy (group gc_smaas mind) (smaas_mind group_mind), beta 74 | nestreg: reg t_excited (group gc_smaas mind) (smaas_mind group_mind), beta 75 | nestreg: reg t_relaxed (group gc_smaas mind) (smaas_mind group_mind), beta 76 | nestreg: reg t_satisfied (group gc_smaas mind) (smaas_mind group_mind), beta 77 | nestreg: reg t_angry (group gc_smaas mind) (smaas_mind group_mind), beta 78 | nestreg: reg t_anxious (group gc_smaas mind) (smaas_mind group_mind), beta 79 | nestreg: reg t_depressed (group gc_smaas mind) (smaas_mind group_mind), beta 80 | nestreg: reg t_sad (group gc_smaas mind) (smaas_mind group_mind), beta 81 | 82 | 83 | 84 | 85 | -------------------------------------------------------------------------------- /DataFromAuthors/Rowland2020/betweennetworkOR.pdf: -------------------------------------------------------------------------------- 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Two randomized controlled trials. 10 | ### Evelien Snippe, Wolfgang Viechtbauer, Nicole Geschwind, Peter de Jonge, Marieke Wichers 11 | ### 12 | ###Code was adapted by Charlotte Vrijen for the present study. 13 | 14 | ########PERMUTATION TEST 1######## 15 | 16 | ############################################################################ 17 | 18 | rm(list=ls()) 19 | library(lme4) 20 | library(parallel) 21 | 22 | ############################################################################ 23 | 24 | ### select study to analyze 25 | study <- "nfng" 26 | 27 | ### number of permutation iterations 28 | perms <- 10000 29 | 30 | ### number of cores to use for (multicore) processing 31 | ncpus <- 1 32 | 33 | ############################################################################ 34 | 35 | ### load data, sort data, and recode/rename variables so that the coding/naming is consistent across trials 36 | ### group: 1 = low happy bias, 2 = high happy bias 37 | 38 | source("r_prep_data.r") 39 | 40 | ############################################################################ 41 | 42 | ### permutation function 43 | 44 | permfunc <- function(iter, dat, nobs.per.person, group.per.person, outnames) { 45 | 46 | library(nlme) 47 | 48 | ### reshuffle group variable (but 1st iteration is always the original data) 49 | if (iter > 1) 50 | dat$group <- rep(sample(group.per.person), times=nobs.per.person) 51 | 52 | ### matrices for storing the coefficients 53 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 54 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 55 | 56 | for (outname in outnames) { 57 | 58 | dat$outcome <- dat[,outname] 59 | 60 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 61 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 62 | 63 | 64 | ### if one of the models doesn't converge, break out of loop 65 | if (inherits(res.G1, "try-error") | inherits(res.G2, "try-error")) 66 | break 67 | 68 | ### store coefficients 69 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 70 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 71 | } 72 | 73 | ### if one of the coefficients is NA, return NA; otherwise return the difference in the difference in mean connections from positive nodes JOY and POS to negative nodes SAD, IRR WOR and NEG. 74 | if (any(is.na(b.G1)) | any(is.na(b.G2))) { 75 | return(NA) 76 | } else { 77 | return((abs(b.G2[2,1])+abs(b.G2[2,2])+abs(b.G2[2,3])+abs(b.G2[2,4])+abs(b.G2[2,5])+abs(b.G2[2,6])+abs(b.G2[2,7])+abs(b.G2[6,1])+abs(b.G2[6,2])+abs(b.G2[6,3])+abs(b.G2[6,4])+abs(b.G2[6,5])+abs(b.G2[6,6])+abs(b.G2[6,7])) - (abs(b.G1[2,1])+abs(b.G1[2,2])+abs(b.G1[2,3])+abs(b.G1[2,4])+abs(b.G1[2,5])+abs(b.G1[2,6])+abs(b.G1[2,7])+abs(b.G1[6,1])+abs(b.G1[6,2])+abs(b.G1[6,3])+abs(b.G1[6,4])+abs(b.G1[6,5])+abs(b.G1[6,6])+abs(b.G1[6,7]))) 78 | 79 | } 80 | 81 | 82 | } 83 | 84 | ############################################################################ 85 | 86 | ### start local cluster for multicore processing 87 | if (ncpus > 1) 88 | cl <- makePSOCKcluster(ncpus) 89 | 90 | ### matrices for storing the coefficients 91 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 92 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 93 | 94 | for (outname in outnames) { 95 | 96 | cat("outcome: ", outname, "\n") 97 | 98 | dat$outcome <- dat[,outname] 99 | 100 | 101 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 102 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 103 | 104 | 105 | ### store coefficients 106 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 107 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 108 | 109 | } 110 | 111 | ### number of observations per person (pre and post together) 112 | nobs.per.person <- sapply(split(dat$group, dat$ID), length) 113 | 114 | ### group of each person (either high bias or low bias) 115 | group.per.person <- sapply(split(dat$group, dat$ID), function(x) x[1]) 116 | 117 | ### repeatedly apply permfunc() function 118 | time.start <- proc.time() 119 | if (ncpus == 1) { 120 | permres <- lapply(1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 121 | } else { 122 | permres <- parLapply(cl, 1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 123 | } 124 | time.end <- proc.time() 125 | cat("Minutes:", ((time.end - time.start)/60)[3], "\n") 126 | 127 | ### turn results into a matrix 128 | permres <- do.call(rbind, permres) 129 | 130 | ############################################################################ 131 | 132 | ### table with permutation based p-values (two definitions of the p-values) 133 | b.diff.obs <- (abs(b.G2[2,1])+abs(b.G2[2,2])+abs(b.G2[2,3])+abs(b.G2[2,4])+abs(b.G2[2,5])+abs(b.G2[2,6])+abs(b.G2[2,7])+abs(b.G2[6,1])+abs(b.G2[6,2])+abs(b.G2[6,3])+abs(b.G2[6,4])+abs(b.G2[6,5])+abs(b.G2[6,6])+abs(b.G2[6,7])) - (abs(b.G1[2,1])+abs(b.G1[2,2])+abs(b.G1[2,3])+abs(b.G1[2,4])+abs(b.G1[2,5])+abs(b.G1[2,6])+abs(b.G1[2,7])+abs(b.G1[6,1])+abs(b.G1[6,2])+abs(b.G1[6,3])+abs(b.G1[6,4])+abs(b.G1[6,5])+abs(b.G1[6,6])+abs(b.G1[6,7])) 134 | p.perm.def1 <- 2*min(mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE)) 135 | p.perm.def2 <- min(1, 2*ifelse(b.diff.obs > 0, mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE))) 136 | 137 | ### save results to file 138 | sav <- round(cbind(b.diff.obs, "p-perm.def1"=p.perm.def1, "p-perm.def2"=p.perm.def2, "conv"=sum(!is.na(permres[,1]))), 6) 139 | capture.output(sav, file=paste0("tabletestdiffjoyandposttoall.txt")) 140 | 141 | ### stop local cluster for multicore processing 142 | if (ncpus > 1) 143 | stopCluster(cl) 144 | 145 | ############################################################################ 146 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/data and syntax main analyses/permutationtest2.r: -------------------------------------------------------------------------------- 1 | ### Supplementary code for the paper: 2 | ### 3 | ###Spread the joy: How high and low bias for happy facial emotions translate into different daily life affect dynamics 4 | ###Charlotte Vrijen, Catharina Hartman, Eeske van Roekel, Peter de Jonge, & Albertine Oldehinkel 5 | 6 | ### Code for loading and preparing the data. 7 | 8 | ### Code was originally Written by Wolfgang Viechtbauer for the paper: 9 | ###The impact of treatments for depression on the dynamic network structure of mental states: Two randomized controlled trials. 10 | ### Evelien Snippe, Wolfgang Viechtbauer, Nicole Geschwind, Peter de Jonge, Marieke Wichers 11 | ### 12 | ###Code was adapted by Charlotte Vrijen for the present study. 13 | 14 | ########PERMUTATION TEST 2######## 15 | 16 | ############################################################################ 17 | 18 | rm(list=ls()) 19 | library(lme4) 20 | library(parallel) 21 | 22 | ############################################################################ 23 | 24 | ### select study to analyze 25 | study <- "nfng" 26 | 27 | ### number of permutation iterations 28 | perms <- 10000 29 | 30 | ### number of cores to use for (multicore) processing 31 | ncpus <- 1 32 | 33 | ############################################################################ 34 | 35 | ### load data, sort data, and recode/rename variables so that the coding/naming is consistent across trials 36 | ### group: 1 = low happy bias, 2 = high happy bias 37 | 38 | source("r_prep_data.r") 39 | 40 | ############################################################################ 41 | 42 | ### permutation function 43 | 44 | permfunc <- function(iter, dat, nobs.per.person, group.per.person, outnames) { 45 | 46 | library(nlme) 47 | 48 | ### reshuffle group variable (but 1st iteration is always the original data) 49 | if (iter > 1) 50 | dat$group <- rep(sample(group.per.person), times=nobs.per.person) 51 | 52 | ### matrices for storing the coefficients 53 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 54 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 55 | 56 | for (outname in outnames) { 57 | 58 | dat$outcome <- dat[,outname] 59 | 60 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 61 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 62 | 63 | 64 | ### if one of the models doesn't converge, break out of loop 65 | if (inherits(res.G1, "try-error") | inherits(res.G2, "try-error")) 66 | break 67 | 68 | ### store coefficients 69 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 70 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 71 | } 72 | 73 | ### if one of the coefficients is NA, return NA; otherwise return the difference in the difference in mean connections from positive nodes JOY and POS to negative nodes SAD, IRR WOR and NEG. 74 | if (any(is.na(b.G1)) | any(is.na(b.G2))) { 75 | return(NA) 76 | } else { 77 | return((b.G2[2,2]+b.G2[2,6]+b.G2[6,2]+b.G2[6,6]) - (b.G1[2,2]+b.G1[2,6]+b.G1[6,2]+b.G1[6,6])) 78 | 79 | } 80 | 81 | 82 | 83 | } 84 | 85 | ############################################################################ 86 | 87 | ### start local cluster for multicore processing 88 | if (ncpus > 1) 89 | cl <- makePSOCKcluster(ncpus) 90 | 91 | ### matrices for storing the coefficients 92 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 93 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 94 | 95 | for (outname in outnames) { 96 | 97 | cat("outcome: ", outname, "\n") 98 | 99 | dat$outcome <- dat[,outname] 100 | 101 | 102 | 103 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 104 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 105 | 106 | 107 | 108 | ### store coefficients 109 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 110 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 111 | 112 | } 113 | 114 | ### number of observations per person (pre and post together) 115 | nobs.per.person <- sapply(split(dat$group, dat$ID), length) 116 | 117 | ### group of each person (either high bias or low bias) 118 | group.per.person <- sapply(split(dat$group, dat$ID), function(x) x[1]) 119 | 120 | ### repeatedly apply permfunc() function 121 | time.start <- proc.time() 122 | if (ncpus == 1) { 123 | permres <- lapply(1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 124 | } else { 125 | permres <- parLapply(cl, 1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 126 | } 127 | time.end <- proc.time() 128 | cat("Minutes:", ((time.end - time.start)/60)[3], "\n") 129 | 130 | ### turn results into a matrix 131 | permres <- do.call(rbind, permres) 132 | 133 | ############################################################################ 134 | 135 | ### table with permutation based p-values (two definitions of the p-values) 136 | b.diff.obs <- (b.G2[2,2]+b.G2[2,6]+b.G2[6,2]+b.G2[6,6]) - (b.G1[2,2]+b.G1[2,6]+b.G1[6,2]+b.G1[6,6]) 137 | p.perm.def1 <- 2*min(mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE)) 138 | p.perm.def2 <- min(1, 2*ifelse(b.diff.obs > 0, mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE))) 139 | 140 | ### save results to file 141 | sav <- round(cbind(b.diff.obs, "p-perm.def1"=p.perm.def1, "p-perm.def2"=p.perm.def2, "conv"=sum(!is.na(permres[,1]))), 6) 142 | capture.output(sav, file=paste0("tabletestdiffjoyandposttojoyandpos.txt")) 143 | 144 | ### stop local cluster for multicore processing 145 | if (ncpus > 1) 146 | stopCluster(cl) 147 | 148 | ############################################################################ 149 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/data and syntax main analyses/permutationtest3.r: -------------------------------------------------------------------------------- 1 | ### Supplementary code for the paper: 2 | ### 3 | ###Spread the joy: How high and low bias for happy facial emotions translate into different daily life affect dynamics 4 | ###Charlotte Vrijen, Catharina Hartman, Eeske van Roekel, Peter de Jonge, & Albertine Oldehinkel 5 | 6 | ### Code for loading and preparing the data. 7 | 8 | ### Code was originally Written by Wolfgang Viechtbauer for the paper: 9 | ###The impact of treatments for depression on the dynamic network structure of mental states: Two randomized controlled trials. 10 | ### Evelien Snippe, Wolfgang Viechtbauer, Nicole Geschwind, Peter de Jonge, Marieke Wichers 11 | ### 12 | ###Code was adapted by Charlotte Vrijen for the present study. 13 | 14 | ########PERMUTATION TEST 3######## 15 | 16 | ############################################################################ 17 | 18 | rm(list=ls()) 19 | library(lme4) 20 | library(parallel) 21 | 22 | ############################################################################ 23 | 24 | ### select study to analyze 25 | study <- "nfng" 26 | 27 | ### number of permutation iterations 28 | perms <- 10000 29 | 30 | ### number of cores to use for (multicore) processing 31 | ncpus <- 1 32 | 33 | ############################################################################ 34 | 35 | ### load data, sort data, and recode/rename variables so that the coding/naming is consistent across trials 36 | ### group: 1 = low happy bias, 2 = high happy bias 37 | 38 | source("r_prep_data.r") 39 | 40 | ############################################################################ 41 | 42 | ### permutation function 43 | 44 | permfunc <- function(iter, dat, nobs.per.person, group.per.person, outnames) { 45 | 46 | library(nlme) 47 | 48 | ### reshuffle group variable (but 1st iteration is always the original data) 49 | if (iter > 1) 50 | dat$group <- rep(sample(group.per.person), times=nobs.per.person) 51 | 52 | ### matrices for storing the coefficients 53 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 54 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 55 | 56 | for (outname in outnames) { 57 | 58 | dat$outcome <- dat[,outname] 59 | 60 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 61 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 62 | 63 | 64 | ### if one of the models doesn't converge, break out of loop 65 | if (inherits(res.G1, "try-error") | inherits(res.G2, "try-error")) 66 | break 67 | 68 | ### store coefficients 69 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 70 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 71 | } 72 | 73 | 74 | ### if one of the coefficients is NA, return NA; otherwise return the difference in the difference in mean connections from positive nodes JOY and POS to negative nodes SAD, IRR WOR and NEG. 75 | if (any(is.na(b.G1)) | any(is.na(b.G2))) { 76 | return(NA) 77 | } else { 78 | return((b.G2[2,3]+b.G2[2,4]+b.G2[2,5]+b.G2[2,7]+b.G2[6,3]+b.G2[6,4]+b.G2[6,5]+b.G2[6,7]) - (b.G1[2,3]+b.G1[2,4]+b.G1[2,5]+b.G1[2,7]+b.G1[6,3]+b.G1[6,4]+b.G1[6,5]+b.G1[6,7])) 79 | } 80 | 81 | 82 | } 83 | 84 | ############################################################################ 85 | 86 | ### start local cluster for multicore processing 87 | if (ncpus > 1) 88 | cl <- makePSOCKcluster(ncpus) 89 | 90 | ### matrices for storing the coefficients 91 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 92 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 93 | 94 | for (outname in outnames) { 95 | 96 | cat("outcome: ", outname, "\n") 97 | 98 | dat$outcome <- dat[,outname] 99 | 100 | 101 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 102 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 103 | 104 | 105 | 106 | ### store coefficients 107 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 108 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 109 | 110 | } 111 | 112 | ### number of observations per person (pre and post together) 113 | nobs.per.person <- sapply(split(dat$group, dat$ID), length) 114 | 115 | ### group of each person (either high bias or low bias) 116 | group.per.person <- sapply(split(dat$group, dat$ID), function(x) x[1]) 117 | 118 | ### repeatedly apply permfunc() function 119 | time.start <- proc.time() 120 | if (ncpus == 1) { 121 | permres <- lapply(1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 122 | } else { 123 | permres <- parLapply(cl, 1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 124 | } 125 | time.end <- proc.time() 126 | cat("Minutes:", ((time.end - time.start)/60)[3], "\n") 127 | 128 | ### turn results into a matrix 129 | permres <- do.call(rbind, permres) 130 | 131 | ############################################################################ 132 | 133 | ### table with permutation based p-values (two definitions of the p-values) 134 | b.diff.obs <- (b.G2[2,3]+b.G2[2,4]+b.G2[2,5]+b.G2[2,7]+b.G2[6,3]+b.G2[6,4]+b.G2[6,5]+b.G2[6,7]) - (b.G1[2,3]+b.G1[2,4]+b.G1[2,5]+b.G1[2,7]+b.G1[6,3]+b.G1[6,4]+b.G1[6,5]+b.G1[6,7]) 135 | p.perm.def1 <- 2*min(mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE)) 136 | p.perm.def2 <- min(1, 2*ifelse(b.diff.obs > 0, mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE))) 137 | 138 | ### save results to file 139 | sav <- round(cbind(b.diff.obs, "p-perm.def1"=p.perm.def1, "p-perm.def2"=p.perm.def2, "conv"=sum(!is.na(permres[,1]))), 6) 140 | capture.output(sav, file=paste0("tabletestdiffjoyandposttoneg.txt")) 141 | 142 | ### stop local cluster for multicore processing 143 | if (ncpus > 1) 144 | stopCluster(cl) 145 | 146 | ############################################################################ 147 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/data and syntax main analyses/permutationtest5.r: -------------------------------------------------------------------------------- 1 | ### Supplementary code for the paper: 2 | ### 3 | ###Spread the joy: How high and low bias for happy facial emotions translate into different daily life affect dynamics 4 | ###Charlotte Vrijen, Catharina Hartman, Eeske van Roekel, Peter de Jonge, & Albertine Oldehinkel 5 | 6 | ### Code for loading and preparing the data. 7 | 8 | ### Code was originally Written by Wolfgang Viechtbauer for the paper: 9 | ###The impact of treatments for depression on the dynamic network structure of mental states: Two randomized controlled trials. 10 | ### Evelien Snippe, Wolfgang Viechtbauer, Nicole Geschwind, Peter de Jonge, Marieke Wichers 11 | ### 12 | ###Code was adapted by Charlotte Vrijen for the present study. 13 | 14 | ########PERMUTATION TEST 5######## 15 | 16 | ############################################################################ 17 | 18 | rm(list=ls()) 19 | library(lme4) 20 | library(parallel) 21 | 22 | ############################################################################ 23 | 24 | ### select study to analyze 25 | study <- "nfng" 26 | 27 | ### number of permutation iterations 28 | perms <- 10000 29 | 30 | ### number of cores to use for (multicore) processing 31 | ncpus <- 1 32 | 33 | ############################################################################ 34 | 35 | ### load data, sort data, and recode/rename variables so that the coding/naming is consistent across trials 36 | ### group: 1 = low happy bias, 2 = high happy bias 37 | 38 | source("r_prep_data.r") 39 | 40 | ############################################################################ 41 | 42 | ### permutation function 43 | 44 | permfunc <- function(iter, dat, nobs.per.person, group.per.person, outnames) { 45 | 46 | library(nlme) 47 | 48 | ### reshuffle group variable (but 1st iteration is always the original data) 49 | if (iter > 1) 50 | dat$group <- rep(sample(group.per.person), times=nobs.per.person) 51 | 52 | ### matrices for storing the coefficients 53 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 54 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 55 | 56 | for (outname in outnames) { 57 | 58 | dat$outcome <- dat[,outname] 59 | 60 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 61 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 62 | 63 | 64 | ### if one of the models doesn't converge, break out of loop 65 | if (inherits(res.G1, "try-error") | inherits(res.G2, "try-error")) 66 | break 67 | 68 | ### store coefficients 69 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 70 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 71 | } 72 | 73 | ### if one of the coefficients is NA, return NA; otherwise return the difference in the difference in mean connections from positive nodes JOY and POS to negative nodes SAD, IRR WOR and NEG. 74 | if (any(is.na(b.G1)) | any(is.na(b.G2))) { 75 | return(NA) 76 | } else { 77 | return((b.G2[3,3]+b.G2[3,4]+b.G2[3,5]+b.G2[3,7]+b.G2[4,3]+b.G2[4,4]+b.G2[4,5]+b.G2[4,7]+b.G2[5,3]+b.G2[5,4]+b.G2[5,5]+b.G2[5,7]+b.G2[7,3]+b.G2[7,4]+b.G2[7,5]+b.G2[7,7]) - (b.G1[3,3]+b.G1[3,4]+b.G1[3,5]+b.G1[3,7]+b.G1[4,3]+b.G1[4,4]+b.G1[4,5]+b.G1[4,7]+b.G1[5,3]+b.G1[5,4]+b.G1[5,5]+b.G1[5,7]+b.G1[7,3]+b.G1[7,4]+b.G1[7,5]+b.G1[7,7])) 78 | 79 | } 80 | 81 | 82 | } 83 | 84 | ############################################################################ 85 | 86 | ### start local cluster for multicore processing 87 | if (ncpus > 1) 88 | cl <- makePSOCKcluster(ncpus) 89 | 90 | ### matrices for storing the coefficients 91 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 92 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 93 | 94 | for (outname in outnames) { 95 | 96 | cat("outcome: ", outname, "\n") 97 | 98 | dat$outcome <- dat[,outname] 99 | 100 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 101 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 102 | 103 | 104 | 105 | ### store coefficients 106 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 107 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 108 | 109 | } 110 | 111 | ### number of observations per person (pre and post together) 112 | nobs.per.person <- sapply(split(dat$group, dat$ID), length) 113 | 114 | ### group of each person (either high bias or low bias) 115 | group.per.person <- sapply(split(dat$group, dat$ID), function(x) x[1]) 116 | 117 | ### repeatedly apply permfunc() function 118 | time.start <- proc.time() 119 | if (ncpus == 1) { 120 | permres <- lapply(1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 121 | } else { 122 | permres <- parLapply(cl, 1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 123 | } 124 | time.end <- proc.time() 125 | cat("Minutes:", ((time.end - time.start)/60)[3], "\n") 126 | 127 | ### turn results into a matrix 128 | permres <- do.call(rbind, permres) 129 | 130 | ############################################################################ 131 | 132 | ### table with permutation based p-values (two definitions of the p-values) 133 | b.diff.obs <- (b.G2[3,3]+b.G2[3,4]+b.G2[3,5]+b.G2[3,7]+b.G2[4,3]+b.G2[4,4]+b.G2[4,5]+b.G2[4,7]+b.G2[5,3]+b.G2[5,4]+b.G2[5,5]+b.G2[5,7]+b.G2[7,3]+b.G2[7,4]+b.G2[7,5]+b.G2[7,7]) - (b.G1[3,3]+b.G1[3,4]+b.G1[3,5]+b.G1[3,7]+b.G1[4,3]+b.G1[4,4]+b.G1[4,5]+b.G1[4,7]+b.G1[5,3]+b.G1[5,4]+b.G1[5,5]+b.G1[5,7]+b.G1[7,3]+b.G1[7,4]+b.G1[7,5]+b.G1[7,7]) 134 | p.perm.def1 <- 2*min(mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE)) 135 | p.perm.def2 <- min(1, 2*ifelse(b.diff.obs > 0, mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE))) 136 | 137 | ### save results to file 138 | sav <- round(cbind(b.diff.obs, "p-perm.def1"=p.perm.def1, "p-perm.def2"=p.perm.def2, "conv"=sum(!is.na(permres[,1]))), 6) 139 | capture.output(sav, file=paste0("tabletestdiffnegtoneg.txt")) 140 | 141 | ### stop local cluster for multicore processing 142 | if (ncpus > 1) 143 | stopCluster(cl) 144 | 145 | ############################################################################ 146 | 147 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/data and syntax main analyses/permutationtest6.r: -------------------------------------------------------------------------------- 1 | ### Supplementary code for the paper: 2 | ### 3 | ###Spread the joy: How high and low bias for happy facial emotions translate into different daily life affect dynamics 4 | ###Charlotte Vrijen, Catharina Hartman, Eeske van Roekel, Peter de Jonge, & Albertine Oldehinkel 5 | 6 | ### Code for loading and preparing the data. 7 | 8 | ### Code was originally Written by Wolfgang Viechtbauer for the paper: 9 | ###The impact of treatments for depression on the dynamic network structure of mental states: Two randomized controlled trials. 10 | ### Evelien Snippe, Wolfgang Viechtbauer, Nicole Geschwind, Peter de Jonge, Marieke Wichers 11 | ### 12 | ###Code was adapted by Charlotte Vrijen for the present study. 13 | 14 | ########PERMUTATION TEST 6######## 15 | 16 | ############################################################################ 17 | 18 | rm(list=ls()) 19 | library(lme4) 20 | library(parallel) 21 | 22 | ############################################################################ 23 | 24 | ### select study to analyze 25 | study <- "nfng" 26 | 27 | ### number of permutation iterations 28 | perms <- 10000 29 | 30 | ### number of cores to use for (multicore) processing 31 | ncpus <- 1 32 | 33 | ############################################################################ 34 | 35 | ### load data, sort data, and recode/rename variables so that the coding/naming is consistent across trials 36 | ### group: 1 = low happy bias, 2 = high happy bias 37 | 38 | source("r_prep_data.r") 39 | 40 | ############################################################################ 41 | 42 | ### permutation function 43 | 44 | permfunc <- function(iter, dat, nobs.per.person, group.per.person, outnames) { 45 | 46 | library(nlme) 47 | 48 | ### reshuffle group variable (but 1st iteration is always the original data) 49 | if (iter > 1) 50 | dat$group <- rep(sample(group.per.person), times=nobs.per.person) 51 | 52 | ### matrices for storing the coefficients 53 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 54 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 55 | 56 | for (outname in outnames) { 57 | 58 | dat$outcome <- dat[,outname] 59 | 60 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 61 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 62 | 63 | 64 | ### if one of the models doesn't converge, break out of loop 65 | if (inherits(res.G1, "try-error") | inherits(res.G2, "try-error")) 66 | break 67 | 68 | ### store coefficients 69 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 70 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 71 | } 72 | ### if one of the coefficients is NA, return NA; otherwise return the difference in the difference in mean connections from positive nodes JOY and POS to negative nodes SAD, IRR WOR and NEG. 73 | if (any(is.na(b.G1)) | any(is.na(b.G2))) { 74 | return(NA) 75 | } else { 76 | return((b.G2[3,2]+b.G2[3,6]+b.G2[4,2]+b.G2[4,6]+b.G2[5,2]+b.G2[5,6]+b.G2[7,2]+b.G2[7,6]) - (b.G1[3,2]+b.G1[3,6]+b.G1[4,2]+b.G1[4,6]+b.G1[5,2]+b.G1[5,6]+b.G1[7,2]+b.G1[7,6])) 77 | 78 | } 79 | 80 | 81 | } 82 | 83 | ############################################################################ 84 | 85 | ### start local cluster for multicore processing 86 | if (ncpus > 1) 87 | cl <- makePSOCKcluster(ncpus) 88 | 89 | ### matrices for storing the coefficients 90 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 91 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 92 | 93 | for (outname in outnames) { 94 | 95 | cat("outcome: ", outname, "\n") 96 | 97 | dat$outcome <- dat[,outname] 98 | 99 | 100 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 101 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 102 | 103 | 104 | 105 | ### store coefficients 106 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 107 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 108 | 109 | } 110 | 111 | ### number of observations per person (pre and post together) 112 | nobs.per.person <- sapply(split(dat$group, dat$ID), length) 113 | 114 | ### group of each person (either high bias or low bias) 115 | group.per.person <- sapply(split(dat$group, dat$ID), function(x) x[1]) 116 | 117 | ### repeatedly apply permfunc() function 118 | time.start <- proc.time() 119 | if (ncpus == 1) { 120 | permres <- lapply(1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 121 | } else { 122 | permres <- parLapply(cl, 1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 123 | } 124 | time.end <- proc.time() 125 | cat("Minutes:", ((time.end - time.start)/60)[3], "\n") 126 | 127 | ### turn results into a matrix 128 | permres <- do.call(rbind, permres) 129 | 130 | ############################################################################ 131 | 132 | ### table with permutation based p-values (two definitions of the p-values) 133 | b.diff.obs <- (b.G2[3,2]+b.G2[3,6]+b.G2[4,2]+b.G2[4,6]+b.G2[5,2]+b.G2[5,6]+b.G2[7,2]+b.G2[7,6]) - (b.G1[3,2]+b.G1[3,6]+b.G1[4,2]+b.G1[4,6]+b.G1[5,2]+b.G1[5,6]+b.G1[7,2]+b.G1[7,6]) 134 | p.perm.def1 <- 2*min(mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE)) 135 | p.perm.def2 <- min(1, 2*ifelse(b.diff.obs > 0, mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE))) 136 | 137 | ### save results to file 138 | sav <- round(cbind(b.diff.obs, "p-perm.def1"=p.perm.def1, "p-perm.def2"=p.perm.def2, "conv"=sum(!is.na(permres[,1]))), 6) 139 | capture.output(sav, file=paste0("tabletestdiffnegtojoypos.txt")) 140 | 141 | ### stop local cluster for multicore processing 142 | if (ncpus > 1) 143 | stopCluster(cl) 144 | 145 | ############################################################################ 146 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/data and syntax main analyses/permutationtest7.r: -------------------------------------------------------------------------------- 1 | ### Supplementary code for the paper: 2 | ### 3 | ###Spread the joy: How high and low bias for happy facial emotions translate into different daily life affect dynamics 4 | ###Charlotte Vrijen, Catharina Hartman, Eeske van Roekel, Peter de Jonge, & Albertine Oldehinkel 5 | 6 | ### Code for loading and preparing the data. 7 | 8 | ### Code was originally Written by Wolfgang Viechtbauer for the paper: 9 | ###The impact of treatments for depression on the dynamic network structure of mental states: Two randomized controlled trials. 10 | ### Evelien Snippe, Wolfgang Viechtbauer, Nicole Geschwind, Peter de Jonge, Marieke Wichers 11 | ### 12 | ###Code was adapted by Charlotte Vrijen for the present study. 13 | 14 | ########PERMUTATION TEST 7######## 15 | 16 | ############################################################################ 17 | 18 | rm(list=ls()) 19 | library(lme4) 20 | library(parallel) 21 | 22 | ############################################################################ 23 | 24 | ### select study to analyze 25 | study <- "nfng" 26 | 27 | ### number of permutation iterations 28 | perms <- 10000 29 | 30 | ### number of cores to use for (multicore) processing 31 | ncpus <- 1 32 | 33 | ############################################################################ 34 | 35 | ### load data, sort data, and recode/rename variables so that the coding/naming is consistent across trials 36 | ### group: 1 = low happy bias, 2 = high happy bias 37 | 38 | source("r_prep_data.r") 39 | 40 | ############################################################################ 41 | 42 | ### permutation function 43 | 44 | permfunc <- function(iter, dat, nobs.per.person, group.per.person, outnames) { 45 | 46 | library(nlme) 47 | 48 | ### reshuffle group variable (but 1st iteration is always the original data) 49 | if (iter > 1) 50 | dat$group <- rep(sample(group.per.person), times=nobs.per.person) 51 | 52 | ### matrices for storing the coefficients 53 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 54 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 55 | 56 | for (outname in outnames) { 57 | 58 | dat$outcome <- dat[,outname] 59 | 60 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 61 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 62 | 63 | 64 | ### if one of the models doesn't converge, break out of loop 65 | if (inherits(res.G1, "try-error") | inherits(res.G2, "try-error")) 66 | break 67 | 68 | ### store coefficients 69 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 70 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 71 | } 72 | ### if one of the coefficients is NA, return NA; otherwise return the difference in the difference in mean connections from positive nodes JOY and POS to negative nodes SAD, IRR WOR and NEG. 73 | if (any(is.na(b.G1)) | any(is.na(b.G2))) { 74 | return(NA) 75 | } else { 76 | return((b.G2[2,1]+b.G2[6,1]) - (b.G1[2,1]+b.G1[6,1])) 77 | 78 | } 79 | 80 | 81 | } 82 | 83 | ############################################################################ 84 | 85 | ### start local cluster for multicore processing 86 | if (ncpus > 1) 87 | cl <- makePSOCKcluster(ncpus) 88 | 89 | ### matrices for storing the coefficients 90 | b.G1 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 91 | b.G2 <- matrix(NA, nrow=length(outnames), ncol=length(outnames), dimnames=list(outnames,outnames)) 92 | 93 | for (outname in outnames) { 94 | 95 | cat("outcome: ", outname, "\n") 96 | 97 | dat$outcome <- dat[,outname] 98 | 99 | 100 | res.G1 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==1), silent=TRUE) 101 | res.G2 <- try(lmer(outcome ~ ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 + (ZINT_1 + ZJOY_1 + ZSAD_1 + ZIRR_1 + ZWOR_1 + ZPOS_1 + ZNEG_1 || ID), data=dat, subset=group==2), silent=TRUE) 102 | 103 | 104 | 105 | ### store coefficients 106 | b.G1[,which(outname == outnames)] <- fixef(res.G1)[2:(length(outnames)+1)] 107 | b.G2[,which(outname == outnames)] <- fixef(res.G2)[2:(length(outnames)+1)] 108 | 109 | } 110 | 111 | ### number of observations per person (pre and post together) 112 | nobs.per.person <- sapply(split(dat$group, dat$ID), length) 113 | 114 | ### group of each person (either high bias or low bias) 115 | group.per.person <- sapply(split(dat$group, dat$ID), function(x) x[1]) 116 | 117 | ### repeatedly apply permfunc() function 118 | time.start <- proc.time() 119 | if (ncpus == 1) { 120 | permres <- lapply(1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 121 | } else { 122 | permres <- parLapply(cl, 1:perms, permfunc, dat=dat, nobs.per.person=nobs.per.person, group.per.person=group.per.person, outnames=outnames) 123 | } 124 | time.end <- proc.time() 125 | cat("Minutes:", ((time.end - time.start)/60)[3], "\n") 126 | 127 | ### turn results into a matrix 128 | permres <- do.call(rbind, permres) 129 | 130 | ############################################################################ 131 | 132 | ### table with permutation based p-values (two definitions of the p-values) 133 | b.diff.obs <- (b.G2[2,1]+b.G2[6,1]) - (b.G1[2,1]+b.G1[6,1]) 134 | p.perm.def1 <- 2*min(mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE)) 135 | p.perm.def2 <- min(1, 2*ifelse(b.diff.obs > 0, mean(permres >= b.diff.obs, na.rm=TRUE), mean(permres <= b.diff.obs, na.rm=TRUE))) 136 | 137 | ### save results to file 138 | sav <- round(cbind(b.diff.obs, "p-perm.def1"=p.perm.def1, "p-perm.def2"=p.perm.def2, "conv"=sum(!is.na(permres[,1]))), 6) 139 | capture.output(sav, file=paste0("tabletestdiffjoyandposttoint.txt")) 140 | 141 | ### stop local cluster for multicore processing 142 | if (ncpus > 1) 143 | stopCluster(cl) 144 | 145 | ############################################################################ 146 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/data and syntax main analyses/r_prep_data.r: -------------------------------------------------------------------------------- 1 | ### Supplementary code for the paper: 2 | ### 3 | ###Spread the joy: How high and low bias for happy facial emotions translate into different daily life affect dynamics 4 | ###Charlotte Vrijen, Catharina Hartman, Eeske van Roekel, Peter de Jonge, & Albertine Oldehinkel 5 | 6 | ### Code for loading and preparing the data. 7 | 8 | ### Code was originally Written by Wolfgang Viechtbauer for the paper: 9 | ###The impact of treatments for depression on the dynamic network structure of mental states: Two randomized controlled trials. 10 | ### Evelien Snippe, Wolfgang Viechtbauer, Nicole Geschwind, Peter de Jonge, Marieke Wichers 11 | ### 12 | ###Code was adapted by Charlotte Vrijen for the present study. 13 | 14 | ############################################################################ 15 | 16 | ### load data, sort data, and recode/rename variables so that the coding/naming is consistent across trials 17 | ### group: 1 = low happy bias, 2 = high happy bias 18 | 19 | ### outcome names 20 | outnames <- c("ZINT", "ZJOY", "ZSAD", "ZIRR", "ZWOR", "ZPOS", "ZNEG") 21 | 22 | if (study == "nfng") { 23 | dat <- read.table("happybias data for permutation main.csv", sep=",", header=TRUE, na.strings="") 24 | dat <- dat[order(dat$ID, dat$group, dat$Time),] 25 | 26 | } 27 | 28 | ########for the other permutation tests for different group sizes, use "happybias data for permutation 20.csv", "happybias data for permutation 30.csv", "happybias data for permutation 35.csv" and "happybias data for permutation 40.csv"### 29 | 30 | ############################################################################ 31 | 32 | ### to avoid captured output being split across lines and to avoid scientific notation 33 | options(width=1000, scipen=100) 34 | 35 | ############################################################################ 36 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/dataprep_detrending/detrending momentary assessments.do: -------------------------------------------------------------------------------- 1 | * DETRENDING 2 | 3 | * First detrend all momentary assessment measures a La Rovine & Walls 4 | * This is detrending with OLS regressions per individual and then adding the PersonMeans to the residuals: 5 | 6 | * use centered version of Time: Time_cent 7 | * centered at middle measure 8 | 9 | * use for ID a variable with ascending numbers 10 | 11 | * Open file: rawdata.dta 12 | 13 | summarize Time 14 | summarize Time_cen 15 | 16 | 17 | tsset ID Time 18 | tsset ID Time_cen 19 | 20 | gen INT_DMDT = . 21 | tsset ID Time_cen 22 | forv i = 1/`=r(imax)' { 23 | reg INTraw Time_cen afternoo evening if ID == `i' 24 | predict res if e(sample), res 25 | replace INT_DMDT = res if ID == `i' 26 | drop res 27 | } 28 | 29 | gen JOY_DMDT = . 30 | tsset ID Time_cen 31 | forv i = 1/`=r(imax)' { 32 | reg JOYraw Time_cen afternoo evening if ID == `i' 33 | predict res if e(sample), res 34 | replace JOY_DMDT = res if ID == `i' 35 | drop res 36 | } 37 | 38 | 39 | gen SAD_DMDT = . 40 | tsset ID Time_cen 41 | forv i = 1/`=r(imax)' { 42 | reg SADraw Time_cen afternoo evening if ID == `i' 43 | predict res if e(sample), res 44 | replace SAD_DMDT = res if ID == `i' 45 | drop res 46 | } 47 | 48 | 49 | gen IRR_DMDT = . 50 | tsset ID Time_cen 51 | forv i = 1/`=r(imax)' { 52 | reg IRRraw Time_cen afternoo evening if ID == `i' 53 | predict res if e(sample), res 54 | replace IRR_DMDT = res if ID == `i' 55 | drop res 56 | } 57 | 58 | 59 | gen WOR_DMDT = . 60 | tsset ID Time_cen 61 | forv i = 1/`=r(imax)' { 62 | reg WORraw Time_cen afternoo evening if ID == `i' 63 | predict res if e(sample), res 64 | replace WOR_DMDT = res if ID == `i' 65 | drop res 66 | } 67 | 68 | gen POS_DMDT = . 69 | tsset ID Time_cen 70 | forv i = 1/`=r(imax)' { 71 | reg POSraw Time_cen afternoo evening if ID == `i' 72 | predict res if e(sample), res 73 | replace POS_DMDT = res if ID == `i' 74 | drop res 75 | } 76 | 77 | gen NEG_DMDT = . 78 | tsset ID Time_cen 79 | forv i = 1/`=r(imax)' { 80 | reg NEGraw Time_cen afternoo evening if ID == `i' 81 | predict res if e(sample), res 82 | replace NEG_DMDT = res if ID == `i' 83 | drop res 84 | } 85 | 86 | *add the person mean 87 | 88 | 89 | by ID: egen INT_mean = mean(INTraw) 90 | gen INT = INT_DMDT+INT_mean 91 | 92 | by ID: egen JOY_mean = mean(JOYraw) 93 | gen JOY = JOY_DMDT+JOY_mean 94 | 95 | by ID: egen SAD_mean = mean(SADraw) 96 | gen SAD = SAD_DMDT+SAD_mean 97 | 98 | by ID: egen IRR_mean = mean(IRRraw) 99 | gen IRR = IRR_DMDT+IRR_mean 100 | 101 | by ID: egen WOR_mean = mean(WORraw) 102 | gen WOR = WOR_DMDT+WOR_mean 103 | 104 | by ID: egen POS_mean = mean(POSraw) 105 | gen POS = POS_DMDT+POS_mean 106 | 107 | by ID: egen NEG_mean = mean(NEGraw) 108 | gen NEG = NEG_DMDT+NEG_mean 109 | 110 | 111 | * save as spss file 112 | *download package: http://www.radyakin.org/transfer/savespss/savespss.htm 113 | 114 | findit savespss 115 | 116 | savespss "raw and detrended data.sav" 117 | -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/dataprep_detrending/raw and detrended data.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Vrijen2018/dataprep_detrending/raw and detrended data.sav -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/dataprep_detrending/rawdata.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Vrijen2018/dataprep_detrending/rawdata.dta -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/general/2018 01 15 Detailed description facial emotion identification morph task.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Vrijen2018/general/2018 01 15 Detailed description facial emotion identification morph task.docx -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/general/Codebook variables.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Vrijen2018/general/Codebook variables.docx -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/general/Data for descriptives.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Vrijen2018/general/Data for descriptives.sav -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/general/Dataset happy bias selection.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Vrijen2018/general/Dataset happy bias selection.sav -------------------------------------------------------------------------------- /DataFromAuthors/Vrijen2018/general/Flowchart.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Vrijen2018/general/Flowchart.tif -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Wright2017/.DS_Store -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/data/ffm2.csv: -------------------------------------------------------------------------------- 1 | opene;consc;extra;agree;neuro;id 2 | 1.1;2;0.5;2.666666667;3.083333333;1 3 | 2.2;2.833333333;3;2.833333333;1.666666667;2 4 | 2;3.25;2.416666667;2.666666667;1.666666667;3 5 | 1.6;3.090909091;1.833333333;2.25;2.916666667;4 6 | 2.3;1.75;1.416666667;1.75;3.083333333;5 7 | 2.9;0.5;0.833333333;2.25;3.5;6 8 | 3.5;2.75;1.333333333;1.916666667;2.916666667;9 9 | 1.2;2.833333333;1.583333333;2.75;2.083333333;10 10 | 2.4;2.5;2;2.333333333;2.083333333;11 11 | 2.8;2.666666667;1.75;2;1.833333333;12 12 | 3;1.583333333;1.75;3;2.25;13 13 | 3;3.416666667;3.25;3.916666667;1.25;14 14 | 2.1;3;1.833333333;2.333333333;1.833333333;15 15 | 3.3;3.583333333;2.5;2.25;2.181818182;16 16 | 2.4;0.5;2.166666667;3.083333333;3.083333333;17 17 | 2.2;2.083333333;2;2.166666667;3;18 18 | 2;3;2.181818182;3.416666667;2.916666667;19 19 | 3.2;3.583333333;3.083333333;2.666666667;1.25;20 20 | 2.7;2.25;2.333333333;1.916666667;3.5;21 21 | 2;2;1.909090909;2;1.75;22 22 | 1.9;2.333333333;2.583333333;2.545454545;2.416666667;23 23 | 1.6;2.363636364;2;2.166666667;2;24 24 | 2.3;3;1.833333333;2.166666667;1.583333333;26 25 | 2.6;1.333333333;1.5;3;2.25;28 26 | 2.9;2.416666667;1.916666667;2.833333333;1.916666667;29 27 | 2.3;2.5;1.666666667;2.666666667;1.916666667;30 28 | 2.5;1.25;0.583333333;2.083333333;2;31 29 | 2;2.333333333;1.166666667;2.833333333;3.25;32 30 | 2.4;1.833333333;1.5;2.583333333;2.666666667;33 31 | 2.3;3.25;1.166666667;3.25;2.5;34 32 | 2.2;2.833333333;2.5;3.25;2.166666667;35 33 | 1.6;3;1.333333333;2.833333333;2;36 34 | 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3.1;3.833333333;2.25;3.416666667;1;110 95 | 3.2;2.5;1.083333333;3.583333333;1.5;111 96 | 1.8;1.6;1.666666667;2.090909091;3.090909091;112 97 | 2.8;2.5;2.666666667;2.083333333;2.166666667;113 98 | 2.3;2.166666667;1.416666667;2.333333333;3.166666667;114 99 | 2;3.583333333;1.833333333;2.909090909;2.25;115 100 | 3.5;1.416666667;1;2.75;3.363636364;116 101 | -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/data/ffm3.csv: -------------------------------------------------------------------------------- 1 | id;neuro;extra;opene;agree;consc 2 | 80000;83;96;102;99;92 3 | 80010;113;62;116;102;87 4 | 80011;91;91;98;91;94 5 | 80030;52;98;147;124;101 6 | 80031;89;78;130;98;119 7 | 80040;120;85;125;96;82 8 | 80041;136;90;137;136;52 9 | 80050;92;61;124;124;100 10 | 80051;116;86;113;124;80 11 | 80060;79;105;126;137;118 12 | 80061;118;91;111;104;80 13 | 80070;87;90;103;95;88 14 | 80071;94;109;128;104;118 15 | 80081;128;97;123;102;98 16 | 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118 | 80820;58;76;81;123;101 119 | 80821;89;105;119;119;129 120 | 80830;83;95;100;95;96 121 | 80831;102;88;95;80;86 122 | 80840;64;108;114;105;109 123 | 80841;106;89;97;101;103 124 | 80861;72;104;117;127;127 125 | 80870;105;104;124;100;98 126 | 80871;114;81;126;100;113 127 | 80880;67;89;105;72;126 128 | 80881;126;97;97;119;121 129 | 80910;84;98;142;130;106 130 | 80911;88;115;133;142;77 131 | 80920;89;89;99;92;97 132 | 80921;82;93;94;104;108 133 | 80930;85;116;107;123;113 134 | 80931;101;117;153;124;140 135 | 80940;68;96;99;120;112 136 | 80941;82;76;97;89;84 137 | 80951;107;95;87;116;107 138 | 80960;87;100;126;98;132 139 | 80961;81;101;105;121;99 140 | 80981;83;86;102;101;83 141 | 80991;56;107;133;126;124 142 | 81000;55;102;116;127;151 143 | 81010;63;93;113;109;120 144 | 81011;93;106;144;128;116 145 | 81021;87;89;96;95;85 146 | 81030;90;86;96;92;88 147 | 81031;90;86;96;92;88 148 | 81040;66;105;91;98;127 149 | 81041;103;114;121;110;79 150 | 81060;73;81;88;94;104 151 | 81070;67;111;108;113;109 152 | 81071;108;100;105;124;105 153 | 81080;88;111;141;131;87 154 | 81081;79;96;143;120;96 155 | 81090;118;91;127;133;95 156 | 81091;113;94;108;116;108 157 | 81100;58;111;153;94;116 158 | 81110;107;105;136;123;93 159 | 81111;113;68;123;99;71 160 | 81121;89;95;99;88;84 161 | 81131;94;81;124;111;97 162 | 81140;96;94;128;112;104 163 | 81141;95;92;113;109;126 164 | 81150;104;100;104;101;109 165 | 81151;119;71;118;118;92 166 | 81160;66;91;103;87;116 167 | 81161;119;131;138;127;35 168 | 81170;74;98;107;115;110 169 | 81171;113;100;97;103;128 170 | 81180;66;66;123;89;123 171 | 81181;129;86;113;90;99 172 | 81210;109;79;89;112;80 173 | 81211;93;66;111;135;134 174 | 81220;81;91;115;75;118 175 | 81221;54;106;129;96;121 176 | 81240;72;96;107;113;118 177 | 81241;91;98;116;118;123 178 | 81260;40;105;97;133;133 179 | 81261;51;93;112;103;122 180 | 81270;85;76;105;86;82 181 | 81271;81;127;120;116;106 182 | 81280;66;95;119;107;106 183 | 81281;61;102;112;114;128 184 | 81290;88;81;92;87;101 185 | 81291;108;88;110;106;109 186 | 81300;48;67;124;132;140 187 | 81301;103;65;127;113;127 188 | 81310;77;89;103;111;136 189 | 81311;117;114;112;130;89 190 | 81320;110;83;107;117;129 191 | 81330;90;86;96;92;88 192 | 81331;90;86;97;91;87 193 | 81340;97;132;102;93;73 194 | 81341;106;100;107;107;101 195 | 81351;108;84;111;86;118 196 | 81360;83;78;105;106;90 197 | 81361;90;101;105;112;123 198 | 81370;91;88;100;91;92 199 | 81371;87;87;98;93;87 200 | 81380;101;91;124;133;98 201 | 81381;126;74;121;120;115 202 | 81401;123;93;126;112;86 203 | 81420;83;95;115;100;91 204 | 81421;123;76;88;106;85 205 | 81431;67;79;106;134;139 206 | 81441;78;122;124;106;119 207 | 81450;82;105;134;127;118 208 | 81451;134;69;144;99;79 209 | 81460;88;95;100;110;120 210 | 81461;106;115;105;85;99 211 | 81470;60;100;132;135;123 212 | 81471;82;79;110;117;100 213 | 81480;84;103;102;91;102 214 | 81481;90;86;96;92;88 215 | 81490;70;94;99;124;120 216 | 81491;63;123;107;127;119 217 | 81501;93;83;100;93;97 218 | 81520;79;103;119;90;102 219 | 81530;106;62;137;92;73 220 | 81531;132;83;127;105;118 221 | 81540;79;96;107;119;123 222 | 81541;82;85;103;107;103 223 | -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/data/tbuch_days_items.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Wright2017/data/tbuch_days_items.sav -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/data/tbuch_days_scales.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Wright2017/data/tbuch_days_scales.sav -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/data/tbuch_scales.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Wright2017/data/tbuch_scales.sav -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/per.2277.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Wright2017/per.2277.pdf -------------------------------------------------------------------------------- /DataFromAuthors/Wright2017/wrightetal2017.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jmbh/EmotionTimeSeries/98043b51a350d4b72152d7c77f4fb44d4821368d/DataFromAuthors/Wright2017/wrightetal2017.pdf -------------------------------------------------------------------------------- /EmotionTimeSeries.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: knitr 13 | LaTeX: pdfLaTeX 14 | -------------------------------------------------------------------------------- /ProcessBetween.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 9, 2022 2 | 3 | # ------------------------------------------------------ 4 | # -------- What is Happening here? ------------------------ 5 | # ------------------------------------------------------ 6 | 7 | # Process between person datasets into a list for use in later analysis 8 | 9 | # Code below from Oisin Ryan 10 | 11 | # ------------------------------------------------------ 12 | # -------- Specify Data Loading ------------------------ 13 | # ------------------------------------------------------ 14 | 15 | baseDir <- "DataClean/" 16 | 17 | 18 | data_files <- c("data_Rowland2020.RDS", 19 | "data_Bringmann2016.RDS", 20 | "data_Vrijen2018_between.RDS", 21 | "data_Fisher2017_between.RDS", 22 | "data_Bringmann2013_between.RDS", 23 | "data_Fried2021_between.RDS", 24 | "data_Wendt2020_between.RDS") 25 | 26 | # Specification of EMOTION variables 27 | l_ind_vars <- list("Rowland2020" = 5:12, # 0-100 scale 28 | "Bringmann2016" = 2:7, # 0-100 29 | "Vrijen2018" = c(5,6,7,8), # 0-100 30 | "Fisher2017" = 2:20, # 0-100 31 | "Bringmann2013" = c(4,6:9), # 1-7 32 | "Fried2021" = c(2:8,10:11), # 1-5 33 | "Wendt2020" = 9:39) # 1-5 scale 34 | 35 | 36 | datal <- list() 37 | for(v in 1:7){ 38 | datal[[v]] <- readRDS(paste0(baseDir, names(l_ind_vars)[v], "/", data_files[v])) 39 | } 40 | 41 | # Make storage for the list file output 42 | data_bet_l <- list() 43 | 44 | # ------------------------------------------------------ 45 | # ------ Load all datasets, get into same format ------- 46 | # ------------------------------------------------------ 47 | 48 | 49 | # ---------------- Rowland 2022 ------------------------ 50 | 51 | 52 | u_id <- unique(datal[[1]]$subj_id) 53 | databet <- matrix(NA, nrow = length(u_id), ncol = 2, dimnames = list(NULL, c("subj_id","group"))) 54 | for(i in 1:length(u_id)){ 55 | bet <- unique(datal[[1]][datal[[1]][,"subj_id"] == u_id[i],"group"]) 56 | if(length(bet) != 1){ stop(paste0("non-unique between person", i)) 57 | }else{ 58 | # recode for dummies 59 | databet[i,2] <- bet -1 60 | } 61 | 62 | databet[i,1] <- u_id[i] 63 | } 64 | 65 | data_bet_l[[1]] <- databet 66 | 67 | 68 | 69 | # -------------- Bringmann 2016 ------------------------ 70 | 71 | 72 | # extremely weird format, see following 73 | databet <- cbind(unique(datal[[2]]$subj_id) ,datal[[2]]$Neuroticism[1:95]) 74 | colnames(databet) = c("subj_id","Neuroticism") 75 | data_bet_l[[2]] <- databet 76 | 77 | 78 | # ----------- Vrijen et al 2018 ------------------------ 79 | 80 | 81 | u_id <- unique(datal[[3]]$subj_id) 82 | databet <- matrix(NA, nrow = length(u_id), ncol = 2, dimnames = list(NULL, c("subj_id","happybias"))) 83 | for(i in 1:length(u_id)){ 84 | d <- as.data.frame(datal[[3]]) 85 | bet <- unique(d[d[,"subj_id"] == u_id[i],"happybias"]) 86 | if(length(bet) != 1){ stop(paste0("non-unique between person", i)) 87 | }else{ 88 | # recode for dummies, Low happybias =1 and high happybias = 2. Low happybias is a sign of "unhealthy" 89 | # so recode such that lowhappybias = 1 and high = 0 (so we always have 0 as healthy) 90 | bet <- as.matrix(bet) 91 | databet[i,2] <- ifelse(bet == 2, 0, bet) 92 | } 93 | 94 | databet[i,1] <- u_id[i] 95 | } 96 | 97 | data_bet_l[[3]] <- databet 98 | 99 | 100 | # ----------------- Fisher 2017 ------------------------ 101 | 102 | 103 | colnames(datal[[4]])[1] <- "subj_id" 104 | data_bet_l[[4]] <- datal[[4]] 105 | 106 | 107 | 108 | # -------------- Bringmann 2013 ------------------------ 109 | 110 | 111 | u_id <- unique(datal[[5]]$subj_id) 112 | databet <- matrix(NA, nrow = length(u_id), ncol = 2, dimnames = list(NULL, c("subj_id","Neurotic"))) 113 | for(i in 1:length(u_id)){ 114 | bet <- unique(datal[[5]][datal[[5]][,"subj_id"] == u_id[i],"Neurotic"]) 115 | bet <- bet[!is.na(bet)] 116 | if(length(bet) ==0){ databet[i,2] <- NA}else{ 117 | # recode for dummies 118 | databet[i,2] <- bet 119 | } 120 | 121 | databet[i,1] <- u_id[i] 122 | } 123 | data_bet_l[[5]] <- databet 124 | 125 | 126 | # ------------------- Fried 2021------------------------ 127 | 128 | data_bet_l[[6]] <- datal[[6]] 129 | 130 | # ------------------- Wendt 2020------------------------ 131 | 132 | data_bet_l[[7]] <- datal[[7]] 133 | 134 | 135 | # ------------------------------------------------------ 136 | # -------------------- Save ---------------------------- 137 | # ------------------------------------------------------ 138 | 139 | lapply(data_bet_l, function(l)head(l)) 140 | saveRDS(data_bet_l, file = "DataClean/BetweenData.RDS") 141 | 142 | -------------------------------------------------------------------------------- /ProcessData.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 5, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load Packages -------------------------------------- 5 | # ------------------------------------------------------------------ 6 | 7 | library(tidyverse) 8 | library(haven) 9 | library(plyr) 10 | library(dplyr) 11 | 12 | # ------------------------------------------------------------------ 13 | # ------------- Run all Scripts ------------------------------------ 14 | # ------------------------------------------------------------------ 15 | 16 | # Get all preprocessing scripts 17 | v_files <- list.files("ProcessingFiles/") 18 | n_files <- length(v_files) 19 | 20 | # Run all scripts 21 | for(DF in 1:n_files) { 22 | source(paste0("ProcessingFiles/", v_files[DF])) 23 | print(paste0(v_files[DF], " [Processed]")) 24 | } 25 | 26 | 27 | # ------------------------------------------------------------------ 28 | # ------------- Create List of Between-person datasets ------------- 29 | # ------------------------------------------------------------------ 30 | 31 | source("ProcessBetween.R") 32 | 33 | 34 | -------------------------------------------------------------------------------- /ProcessingFiles/Bringmann2013.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 5, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load data ------------------------------------------ 5 | # ------------------------------------------------------------------ 6 | 7 | data <- read.table("DataFromAuthors/Bringmann2013/pone.0060188.s004.txt", 8 | header=TRUE, sep=",") 9 | 10 | 11 | # ------------------------------------------------------------------ 12 | # ------------- Process -------------------------------------------- 13 | # ------------------------------------------------------------------ 14 | 15 | colnames(data) <- c("subj_id", "dayno", "beepno", "informat", "period", "Excited", "PlstEvent", "Worried", "Anxious", "Sad", "Relaxed", "Neurotic") 16 | 17 | # Take out between-measurements 18 | out <- ddply(data, .(subj_id), function(x) x$Neurotic[1] ) 19 | colnames(out)[2] <- "Neurotic" 20 | 21 | # Delete Neurotic from within-data 22 | data2 <- data[, -12] 23 | head(data2) 24 | 25 | # ------------------------------------------------------------------ 26 | # ------------- Save -------------------------------------------- 27 | # ------------------------------------------------------------------ 28 | 29 | # Within 30 | saveRDS(data2, file ="DataClean/Bringmann2013/data_Bringmann2013.RDS") 31 | 32 | # Between 33 | saveRDS(out, file ="DataClean/Bringmann2013/data_Bringmann2013_between.RDS") -------------------------------------------------------------------------------- /ProcessingFiles/Bringmann2016.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 5, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load data ------------------------------------------ 5 | # ------------------------------------------------------------------ 6 | 7 | data <- read.table("DataFromAuthors/Bringmann2016/Data95.csv", header=TRUE, sep=";") 8 | 9 | # ------------------------------------------------------------------ 10 | # ------------- Process -------------------------------------------- 11 | # ------------------------------------------------------------------ 12 | 13 | head(data) 14 | 15 | # Replace 999 with NA 16 | data[data==9999] <- NA 17 | 18 | # Remove lagged variables 19 | data <- data[, -c(3, 5, 7, 9, 11, 13, 14)] 20 | 21 | # Rename to English from paper 22 | colnames(data) <- c("subj_id", "Angry", "Depressed", "Dysphoric", "Anxious", "Relaxed", "Happy", "Neuroticism") 23 | 24 | head(data) 25 | 26 | # ------------------------------------------------------------------ 27 | # ------------- Save -------------------------------------------- 28 | # ------------------------------------------------------------------ 29 | 30 | saveRDS(data, file = "DataClean/Bringmann2016/data_Bringmann2016.RDS") 31 | -------------------------------------------------------------------------------- /ProcessingFiles/Fisher2017.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 5, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load data ------------------------------------------ 5 | # ------------------------------------------------------------------ 6 | 7 | # Fisher Reanalysis 8 | folder <- "DataFromAuthors/Fisher2017/fisher2017/R Data/" 9 | files <- list.files(folder) 10 | n_files <- length(files) 11 | 12 | datlist <- list() 13 | 14 | for (i in seq(length(files))) { 15 | load(paste0(folder, files[i])) 16 | datlist[[i]] <- data 17 | } 18 | 19 | # Turn this into a single data frame 20 | l_newformat <- list() 21 | for(i in 1:n_files) l_newformat[[i]] <- cbind(i, datlist[[i]][, 3:27]) 22 | 23 | 24 | # ------------------------------------------------------------------ 25 | # ------------- Process -------------------------------------------- 26 | # ------------------------------------------------------------------ 27 | 28 | # Collapse 29 | data <- do.call(rbind, l_newformat) 30 | colnames(data) <- c("subj_id", colnames(datlist[[1]][, 3:27])) 31 | head(data) 32 | 33 | 34 | # ------------------------------------------------------------------ 35 | # ------------- Save -------------------------------------------- 36 | # ------------------------------------------------------------------ 37 | 38 | # Within 39 | saveRDS(data, file = "DataClean/Fisher2017/data_Fisher2017.RDS") 40 | 41 | # Between 42 | # manually enter table of between-person characteristics into R 43 | # Based on Table 1 of Fisher et al 2017 44 | 45 | 46 | 47 | between <- rbind( 48 | c( 23, 27 ), 49 | c( 16,15 ), 50 | c( 16,33 ), 51 | c( 13,13 ), 52 | c( 11,17 ), 53 | c( 19,15 ), 54 | c( 17,9 ), 55 | c( 22,22 ), 56 | c( 9,13 ), 57 | c( 14,19 ), 58 | c( 10,12 ), 59 | c( 10,10 ), 60 | c( 15,16 ), 61 | c( 8,7 ), 62 | c( 15,14 ), 63 | c( 8,14 ), 64 | c( 12,23 ), 65 | c( 21,41 ), 66 | c( 14,17 ), 67 | c( 11,14 ), 68 | c( 15,13 ), 69 | c( 12,10 ), 70 | c( 18,23 ), 71 | c( 7,14 ), 72 | c( 18,15 ), 73 | c( 4,15 ), 74 | c( 18,19 ), 75 | c( 12,18 ), 76 | c( 9,13 ), 77 | c( 16,15 ), 78 | c( 14,12 ), 79 | c( 21,30 ), 80 | c( 13,11 ), 81 | c( 16,16 ), 82 | c( 13,15 ), 83 | c( 10,11 ), 84 | c( 18,20 ), 85 | c( 12,16 ), 86 | c( 17,23 ), 87 | c( 17,14 ) 88 | ) 89 | 90 | id <- 1:40 91 | 92 | out <- cbind(id, between) 93 | colnames(out) <- c("id", "dep","anx") 94 | 95 | saveRDS(out, "DataClean/Fisher2017/data_Fisher2017_between.RDS") 96 | 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /ProcessingFiles/Fried2021.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 5, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load data ------------------------------------------ 5 | # ------------------------------------------------------------------ 6 | 7 | load("DataFromAuthors/Fried2021/4.Data/clean_ema.RData") # within-person measurements 8 | load("DataFromAuthors/Fried2021/4.Data/clean_prepost.RData") # between-person measurements 9 | 10 | # ------------------------------------------------------------------ 11 | # ------------- Process -------------------------------------------- 12 | # ------------------------------------------------------------------ 13 | 14 | # ----- Within Data ----- 15 | 16 | data <- rawdata[, -c(2:5, 24)] # delete unnecessary columns 17 | 18 | # Turn id into integer 19 | data$ID <- as.character(data$ID) 20 | u_id <- unique(data$ID) 21 | for(i in 1:length(u_id)) data$ID[data$ID == u_id[i]] <- i 22 | table(data$ID) 23 | 24 | # Turn Day into Integer 25 | data$Day <- as.character(data$Day) 26 | u_day <- unique(data$Day) 27 | for(i in 1:length(u_day)) data$Day[data$Day == u_day[i]] <- i 28 | table(data$Day) 29 | 30 | # Add Variable Names 31 | 32 | colnames(data) <- c("subj_id", "Relax", "Irritable", "Worry", "Nervous", "Future", "Anhedonia", 33 | "Tired", "Hunger", "Alone", "Angry", "Social_Offline", "Social_Online", 34 | "Music", "Procrast", "Outdoors", "C19_Occupied", 35 | "C19_Worry", "Home", 36 | "dayvar", "beepvar") 37 | head(data) 38 | 39 | data$dayvar <- as.numeric(data$dayvar) 40 | data$beepvar <- as.numeric(data$beepvar) 41 | 42 | # ----- Between Data ----- 43 | # Oisin added 30 March 2022 - add between person information 44 | 45 | df <- rawdata_full 46 | DASS <- data.frame("id"= df[,1], "DASSpre"= rowSums(df[,c(14:34)])) 47 | 48 | # drop people who are not in the original dataset 49 | u_id2 <- as.character(DASS[,1]) 50 | DASS1 <- DASS[(u_id2 %in% u_id),] 51 | DASS1 52 | 53 | # check for people who are in the time series dataset but not the betweenperson 54 | u_id[!u_id %in% u_id2] 55 | all(as.character(DASS1[,1]) == u_id[-45]) 56 | 57 | # add new 58 | DASS_pre <- c(DASS1[1:44, 2], NA, DASS1[45:nrow(DASS1),2]) 59 | 60 | id <- as.numeric(unique(data$subj_id)) 61 | databetween <- cbind(id, DASS_pre) 62 | 63 | 64 | # ------------------------------------------------------------------ 65 | # ------------- Save -------------------------------------------- 66 | # ------------------------------------------------------------------ 67 | 68 | # within 69 | saveRDS(data, file = "DataClean/Fried2021/data_Fried2021.RDS") 70 | 71 | # between 72 | saveRDS(databetween, file = "DataClean/Fried2021/data_Fried2021_between.RDS") 73 | 74 | 75 | -------------------------------------------------------------------------------- /ProcessingFiles/Rowland2020.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 5, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load data ------------------------------------------ 5 | # ------------------------------------------------------------------ 6 | 7 | dat <- read.table("DataFromAuthors/Rowland2020/data_affect.csv", sep=",", header=TRUE, na.strings="") 8 | 9 | # ------------------------------------------------------------------ 10 | # ------------- Process -------------------------------------------- 11 | # ------------------------------------------------------------------ 12 | 13 | # Var names from paper 14 | outnames <- c("happy", "excited", "relaxed", "satisfied", "angry","anxious", "depressed", "sad") 15 | 16 | # Sort data & add item names 17 | dat <- dat[order(dat$subjno, dat$group, dat$dayno, dat$beep),] 18 | names(dat)[pmatch(c("emo1_m", "emo2_m", "emo3_m", "emo4_m", "emo5_m", "emo6_m", "emo7_m", "emo8_m"), 19 | names(dat))] <- outnames 20 | data <- dat 21 | colnames(data)[1] <- "subj_id" 22 | 23 | head(data) 24 | 25 | 26 | # ------------------------------------------------------------------ 27 | # ------------- Save -------------------------------------------- 28 | # ------------------------------------------------------------------ 29 | 30 | saveRDS(data, file = "DataClean/Rowland2020/data_Rowland2020.RDS") 31 | 32 | 33 | -------------------------------------------------------------------------------- /ProcessingFiles/Vrijen2018.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; May 5, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load data ------------------------------------------ 5 | # ------------------------------------------------------------------ 6 | 7 | data <- read_dta(file = "DataFromAuthors/Vrijen2018/dataprep_detrending/rawdata.dta") 8 | 9 | 10 | 11 | # ------------------------------------------------------------------ 12 | # ------------- Process -------------------------------------------- 13 | # ------------------------------------------------------------------ 14 | 15 | # ----- Within Data ----- 16 | 17 | data <- as.data.frame(data) 18 | 19 | # Remove cases in which time of day indicator is missing 20 | kill_id <- rowSums(data[, 8:10])!=0 21 | table(kill_id) 22 | data_new <- data[kill_id, ] 23 | 24 | # Create a beepvar variable 25 | beepvar <- unlist(apply(data[, 8:10], 1, function(x) which(x==1))) 26 | 27 | data_new2 <- cbind(data_new[, 18], beepvar, data_new[, c(12, 1:7)]) 28 | data_newbp <- data_new[,"happybias"] 29 | 30 | colnames(data_new2) <- c("subj_id", "beepvar", "dayvar", 31 | "Feel_Inter", "Joy", "Sad", "Irritated", 32 | "Worried", "Pleas_Exp", "Unpleas_Exp") 33 | 34 | data <- data_new2 35 | 36 | # ----- Between Data ----- 37 | databet <- cbind(data_new2,data_newbp) 38 | out <- ddply(databet, .(subj_id), function(x) x$data_newbp[1] ) 39 | colnames(out)[2] <- "happybias" 40 | head(out) 41 | 42 | 43 | # ------------------------------------------------------------------ 44 | # ------------- Save -------------------------------------------- 45 | # ------------------------------------------------------------------ 46 | 47 | # Within 48 | saveRDS(data, file = "DataClean/Vrijen2018/data_Vrijen2018.RDS") 49 | 50 | # Between 51 | saveRDS(out, file = "DataClean/Vrijen2018/data_Vrijen2018_between.RDS") 52 | 53 | 54 | -------------------------------------------------------------------------------- /ProcessingFiles/Wright2017.R: -------------------------------------------------------------------------------- 1 | # jonashaslbeck@gmail.com; Oct 18, 2022 2 | 3 | # ------------------------------------------------------------------ 4 | # ------------- Load data ------------------------------------------ 5 | # ------------------------------------------------------------------ 6 | 7 | # Fisher Reanalysis 8 | data <- read.table("DataFromAuthors/Wright2017/data/iads_sample3_ild.csv", 9 | header=TRUE, sep=";") 10 | 11 | datab <- read.table("DataFromAuthors/Wright2017/data/ffm3.csv", 12 | header=TRUE, sep=";") 13 | 14 | 15 | # ------------------------------------------------------------------ 16 | # ------------- Process -------------------------------------------- 17 | # ------------------------------------------------------------------ 18 | 19 | 20 | # -------- Within-person data -------- 21 | 22 | #### From here: code from Wendt 2020 23 | dat <- data 24 | 25 | dummy2 <- data.frame(ddply( dat, .(UsrID), function(x) length(x$UsrID) )) 26 | 27 | which.na <- which(dat$tmstmp1 == 2) 28 | dat1 <- dat[-which.na,] 29 | dat1 <- unique(dat1) 30 | dat1$na.sum <- rowSums(is.na(dat1)) 31 | 32 | which.na.sum <- which(dat1$na.sum > 4) 33 | dat1 <- dat1[-which.na.sum ,] 34 | 35 | dat1 <- dat1[ 36 | with(dat1, order(UsrID, Date, tmstmp1)), 37 | ] 38 | 39 | 40 | trial <- 0 41 | id <- 0 42 | i <- 1 43 | date <- 0 44 | # if (dat1$date[i]==id) { 45 | 46 | for (i in 1:nrow(dat1)){ 47 | if (dat1$UsrID[i]==id) { 48 | trial <- trial+1 49 | } else { 50 | trial <- 0 51 | } 52 | id <- dat1$UsrID[i] 53 | dat1$trial[i] <- trial+1 54 | } 55 | 56 | date <- 1160 57 | id <- 80000 58 | trial <- 1 59 | 60 | for (i in 1:nrow(dat1)){ 61 | 62 | if (dat1$UsrID[i]==id) { 63 | 64 | if (dat1$Date[i]==date) {trial <- trial+1} else {trial <- trial+2} 65 | 66 | dat1$trial[i] <- trial 67 | date <- dat1$Date[i] 68 | 69 | } else 70 | 71 | { 72 | trial <- 1 73 | date <- dat1$Date[i] 74 | id <- dat1$UsrID[i] 75 | } 76 | 77 | } 78 | 79 | min.occ = 20 #minimum of occasions per subject (=/= subsequent occasions) 80 | max.occ = 600 #maximum of occasions per subject (here = 30) 81 | 82 | 83 | # Daten ordnen nach vp_id und tbday 84 | dat1 <- dat1[with(dat1, order(UsrID, trial)),] 85 | 86 | table <- table(dat1$UsrID) # Occasions pro Subject anzeigen 87 | which1 <- names(table[table < min.occ]) # Subjects mit weniger als x occasions ausschlie?en 88 | which2 <- names(table[table > max.occ]) # Subjects mit mehr als x occasions ausschlie?en 89 | dat2 <- dat1[ ! dat1$UsrID %in% which1, ] # anwenden 90 | dat2 <- dat2[ ! dat2$UsrID %in% which2, ] # anwenden 91 | 92 | dat <- dat2 93 | 94 | colnames(dat) <- c("tmstmp1", "UsrID", "PTNUM", "Dyad", "Date", "Afraid", "Ashamed", 95 | "Distressed", "Guilty", "Hostile", "Irritable", "Jittery", 96 | "Nervous", "Scared", "Upset", "Frightened", "Shaky", "Angry", 97 | "Scornful", "Disgusted", "Loathing", "Sad", "Blue", "Downhearted", 98 | "Alone", "Lonely", "Active", "Alert", "Attentive", "Determined", 99 | "Enthusiastic", "Excited", "Inspired", "Interested", "Proud", 100 | "Strong","na.sum" , "trial") 101 | dat <- dat[c(38,1:36)] 102 | 103 | daily_trial <- 0 104 | Date <- 0 105 | i <- 1 106 | 107 | for (i in 1:nrow(dat)){ 108 | if (dat$Date[i]==Date) { 109 | daily_trial <- daily_trial+1 110 | } else { 111 | daily_trial <- 0 112 | } 113 | Date <- dat$Date[i] 114 | dat$daily_trial[i] <- daily_trial+1 115 | } 116 | 117 | 118 | day <- 1 119 | Date <- 0 120 | i <- 1 121 | UsrID <- 0 122 | 123 | for (i in 1:nrow(dat)){ 124 | 125 | if (dat$UsrID[i]==UsrID) { 126 | } else { 127 | day <- 0 128 | } 129 | 130 | if (dat$Date[i]==Date) { 131 | } else { 132 | day <- day+1 133 | } 134 | Date <- dat$Date[i] 135 | UsrID <- dat$UsrID[i] 136 | dat$day[i] <- day 137 | } 138 | 139 | dat <- dat[c(39,38,1:37)] 140 | colnames(dat) <- c("day", "beep", "trial", "tmstmp1", "id", "PTNUM", "Dyad","Date", 141 | "Afraid","Ashamed", "Distressed", 142 | "Guilty" ,"Hostile","Irritable","Jittery","Nervous","Scared", 143 | "Upset","Frightened","Shaky","Angry","Scornful", "Disgusted","Loathing", 144 | "Sad","Blue", 145 | "Downhearted", "Alone", "Lonely", "Active", "Alert","Attentive" , 146 | "Determined", "Enthusiastic", "Excited","Inspired","Interested", 147 | "Proud", "Strong") 148 | 149 | 150 | #### Until here: code from Wendt 2020 151 | 152 | dim(dat) 153 | head(dat) 154 | 155 | tb <- table(dat$id) 156 | tb 157 | range(tb) 158 | length(tb) 159 | 160 | data <- dat 161 | 162 | colnames(data)[5] <- "subj_id" 163 | 164 | 165 | # Transform 3:7 scale into 1:5 scale 166 | table(unlist(data[, 9:39])) 167 | data_15 <- data 168 | data_15[, 9:39] <- data[, 9:39] - 2 169 | table(unlist(data_15[, 9:39])) 170 | 171 | 172 | # -------- Between-person data -------- 173 | # Code: Oisin 174 | 175 | datab <- datab[,1:2] 176 | length(unique(datab[,1])) 177 | length(unique(data$subj_id)) 178 | 179 | u_id <- unique(data$subj_id) 180 | u_id2 <- unique(datab[,1]) 181 | 182 | # first, cut from the between data those participants who are not in the original data 183 | u_id3 <- u_id2[(u_id2 %in% u_id)] 184 | 185 | # drop rows which aren't in the esm data 186 | datab1 <- datab[(u_id2 %in% u_id),] 187 | 188 | # add NAs for people who are in the ESM data 189 | u_id[(u_id %in% u_id3)] 190 | 191 | new <- matrix(NA, nrow = length(u_id), ncol = 2) 192 | new[,1] <- u_id 193 | for(i in 1:length(u_id)){ 194 | if(u_id[i] %in% u_id3){ 195 | new[i,2] <- datab1[which(u_id3 == u_id[i]),2] 196 | } else{ 197 | new[i,2] <- NA 198 | } 199 | } 200 | 201 | colnames(new) = c("subj_id","neuroticism") 202 | 203 | # ------------------------------------------------------------------ 204 | # ------------- Save -------------------------------------------- 205 | # ------------------------------------------------------------------ 206 | 207 | # Within 208 | saveRDS(data_15, file = "DataClean/Wendt2020/data_Wendt2020.RDS") 209 | # Between 210 | saveRDS(new, file = "DataClean/Wendt2020/data_Wendt2020_between.RDS") 211 | 212 | 213 | 214 | 215 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## README 2 | This is an archive including the data of currently seven studies measuring emotions in daily life using the Experience Sampling Methodology (ESM). 3 | 4 | ### The included studies are: 5 | 6 | 1. Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., ... & Tuerlinckx, F. (2013). A network approach to psychopathology: new insights into clinical longitudinal data. PloS one, 8(4), e60188. 7 | 2. Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., ... & Kuppens, P. (2016). Assessing temporal emotion dynamics using networks. Assessment, 23(4), 425-435. 8 | 3. Rowland, Z., & Wenzel, M. (2020). Mindfulness and affect-network density: Does mindfulness facilitate disengagement from affective experiences in daily life?. Mindfulness, 11(5), 1253-1266. 9 | 4. Fried, E. I., Papanikolaou, F., & Epskamp, S. (2021). Mental health and social contact during the COVID-19 pandemic: an ecological momentary assessment study. Clinical Psychological Science, 10(2), 340-354. 10 | 5. Vrijen, C., Hartman, C. A., Van Roekel, E., De Jonge, P., & Oldehinkel, A. J. (2018). Spread the joy: How high and low bias for happy facial emotions translate into different daily life affect dynamics. Complexity, 2018. 11 | 6. Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D., & Rubel, J. A. (2017). Exploring the idiographic dynamics of mood and anxiety via network analysis. Journal of abnormal psychology, 126(8), 1044. 12 | 7. Wright, A. G., Stepp, S. D., Scott, L. N., Hallquist, M. N., Beeney, J. E., Lazarus, S. A., & Pilkonis, P. A. (2017). The effect of pathological narcissism on interpersonal and affective processes in social interactions. Journal of abnormal psychology, 126(7), 898. 13 | 14 | Note 1: Vrijen et. al (2018) ask for a request to reuse their data. For more info see [https://osf.io/4czv3/](https://osf.io/4czv3/). 15 | 16 | Note 2: The data of Wright et al. (2017) are shared in the publication: Wendt, L. P., Wright, A. G., Pilkonis, P. A., Woods, W. C., Denissen, J. J., Kühnel, A., & Zimmermann, J. (2020). Indicators of affect dynamics: Structure, reliability, and personality correlates. European Journal of Personality, 34(6), 1060-1072. 17 | 18 | We would like to thank these authors for making their data openly available. 19 | 20 | 21 | ### Organization of this archive 22 | 23 | The organization of this archive is as follows: 24 | 25 | - `DataFromAuthors/`: Includes the data and associated material for each study. 26 | - `ProcessingFiles/`: Includes R files for each original data set and processes them into a standard format; these scripts are called by `ProcessData.R` 27 | - `DataClean/`: Includes the pre-processed data for each study. 28 | - `ProcessData.R`: Runs the processing scripts for each study, generating the cleaned data. 29 | - `ProcessBetween.R`: Uses the cleaned between-person data to create a list of datasets with columns id and between person characteristic for each dataset; this script is called by `ProcessData.R` 30 | 31 | --------------------------------------------------------------------------------