knitr::opts_chunk$set(echo = TRUE, warning=FALSE)
knitr::opts_knit$set(root.dir = "/Users/caitlinbrown/Desktop/RNT_R")
library(stargazer)##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.1. https://CRAN.R-project.org/package=stargazerlibrary(sjPlot)## Learn more about sjPlot with 'browseVignettes("sjPlot")'.library(sjmisc)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#rntwide <- read.csv("HealthyU_merged_totals_wide_rev1.csv")
rntwide <- read.csv("rntwide_full_withsubscales.csv")
colnames(rntwide)## [1] "ID" "Rtinhibit" "STRPacc"
## [4] "STRPiacc" "sw_costRT" "sw_avg_ac"
## [7] "sw_inc_ac" "sw_acc" "sw_iacc"
## [10] "nback_rt" "nback_D" "nback_ac"
## [13] "ASI" "ASI_soc" "ASI_ph"
## [16] "ASI_cog" "FSIS" "GAD7"
## [19] "IUS" "IUS_pa" "IUS_ia"
## [22] "OCIR" "OCIRchk" "OCIRord"
## [25] "OCIRwash" "OCIRobs" "PEPQ"
## [28] "PEPQavg" "PHQ9" "PSWQ"
## [31] "PSWQ_tot" "PTQ" "RFQ"
## [34] "RRS" "SHAPS" "SAD"
## [37] "SADavg" "SIAS" "SIR"
## [40] "SIR_d" "SIR_a" "URG"
## [43] "URGavg" "PU" "Puavg"
## [46] "ASRM" "binge" "purge"
## [49] "restric" "exerc" "IES"
## [52] "INQ" "time.x" "sASI2"
## [55] "sASI_soc2" "sASI_phy2" "sASI_cog2"
## [58] "FSIS2" "GAD7_2" "IUS2"
## [61] "IUS_pa2" "IUS_ia2" "OCIR2"
## [64] "OCIRchk2" "OCIRord2" "OCIRwsh2"
## [67] "OCIRobs2" "PEPQ2" "PEPQavg2"
## [70] "PHQ9_2" "PSWQ2" "PTQ2"
## [73] "RRS2" "SHAPS2" "SIAS2"
## [76] "SIR2" "SIR_d2" "SIR_a2"
## [79] "URG2" "URGavg2" "PU2"
## [82] "PUavg2" "ASRM_2" "binge_2"
## [85] "purge_2" "restric_2" "exerc_2"
## [88] "INQ_2" "ALC2" "time.y"
## [91] "time" "onset2" "LAIP2"
## [94] "rLAIP2" "RAIP2" "rRAIP2"
## [97] "mu2" "lambda2" "rho2"
## [100] "LL2" "BIC2" "pseudo2"
## [103] "onset4" "LAIP4" "rLAIP4"
## [106] "RAIP4" "rRAIP4" "mu4"
## [109] "lambda4" "rho4" "LL4"
## [112] "BIC4" "pseudo4" "onset3"
## [115] "LAIP3" "rLAIP3" "RAIP3"
## [118] "rRAIP3" "mu3" "lambda3"
## [121] "rho3" "LL3" "BIC3"
## [124] "pseudo3" "onset1" "LAIP1"
## [127] "rLAIP1" "RAIP1" "rRAIP1"
## [130] "mu1" "lambda1" "rho1"
## [133] "LL1" "BIC1" "pseudo1"
## [136] "ASI3" "ASI_soc3" "ASI_phy3"
## [139] "ASI_cog3" "FSIS3" "GAD7_3"
## [142] "IUS3" "IUS_pa3" "IUS_ia3"
## [145] "OCIR3" "OCIRchk3" "OCIRord3"
## [148] "OCIRwsh3" "OCIRobs3" "PEPQ3"
## [151] "PEPQavg3" "PHQ9_3" "PSWQ3"
## [154] "PTQ3" "RRS3" "SHAPS3"
## [157] "SIAS3" "SIR3" "SIR_d3"
## [160] "SIR_a3" "URG3" "URGavg3"
## [163] "PU3" "PUavg3" "ASRM_3"
## [166] "binge_3" "purge_3" "restric_3"
## [169] "exerc_3" "INQ_3" "ALC3"
## [172] "Drugs_3" "PTSD3" "RIQ3"
## [175] "r21dif" "l21dif" "r32dif"
## [178] "l32dif" "r31dif" "l31dif"
## [181] "pan_p1" "pan_n1" "pan_p2"
## [184] "pan_n2" "pan_p3" "pan_n3"
## [187] "pan_p4" "pan_n4" "pan_p5"
## [190] "pan_n5" "pan_p6" "pan_n6"
## [193] "pan_p7" "pan_n7" "Idcheck"
## [196] "LaOcsLg0" "LaOcrLg0" "RhoOc0"
## [199] "RhoSqOc0" "LaOcsLg1" "LaOcrLg1"
## [202] "RhoOc1" "RhoSqOc1" "LaOcsLg2"
## [205] "LaOcrLg2" "RhoOc2" "RhoSqOc2"
## [208] "LaOcsLg3" "LaOcrLg3" "RhoOc3"
## [211] "RhoSqOc3" "La21dif" "La31dif"
## [214] "La32dif" "Rh21dif" "Rh31dif"
## [217] "Rh32dif" "PTQsplit" "pan_ndiff32"
## [220] "pan_ndiff43" "pan_ndiff54" "pan_ndiff65"
## [223] "pan_pdiff32" "pan_pdiff43" "pan_pdiff54"
## [226] "pan_pdiff65" "pan_ndiff42" "pan_pdiff42"
## [229] "Cortisol.mg.dl.0" "Cortisol.nmol.L.0" "Cortisol.mg.dl.1"
## [232] "Cortisol.nmol.L.1" "Cortisol.mg.dl.2" "Cortisol.nmol.L.2"
## [235] "Cortisol.mg.dl.3" "Cortisol.nmol.L.3" "Cortisol.mg.dl.4"
## [238] "Cortisol.nmol.L.4" "Cortisol.mg.dl.5" "Cortisol.nmol.L.5"
## [241] "time." "PTQ_core" "PTQ_unprod"
## [244] "PTQ_mental" "RRS_ref" "RRS_brood"
## [247] "RRS_dep" "time2" "PTQ_core2"
## [250] "PTQ_unprod2" "PTQ_mental2" "RRS_ref2"
## [253] "RRS_brood2" "RRS_dep2" "time3"
## [256] "PTQ_core3" "PTQ_unprod3" "PTQ_mental3"
## [259] "RRS_ref3" "RRS_brood3" "RRS_dep3"#Cortisol and RNT variables
cdf <- rntwide[c(1,150,153:155,242:247,181,183,185,187,189,191,193,182,184,186,188,190,192,194,229,231,233,235,237,239)]
colnames(cdf)## [1] "ID" "PEPQ3" "PSWQ3"
## [4] "PTQ3" "RRS3" "PTQ_core"
## [7] "PTQ_unprod" "PTQ_mental" "RRS_ref"
## [10] "RRS_brood" "RRS_dep" "pan_p1"
## [13] "pan_p2" "pan_p3" "pan_p4"
## [16] "pan_p5" "pan_p6" "pan_p7"
## [19] "pan_n1" "pan_n2" "pan_n3"
## [22] "pan_n4" "pan_n5" "pan_n6"
## [25] "pan_n7" "Cortisol.mg.dl.0" "Cortisol.mg.dl.1"
## [28] "Cortisol.mg.dl.2" "Cortisol.mg.dl.3" "Cortisol.mg.dl.4"
## [31] "Cortisol.mg.dl.5"colnames(cdf) <- c("ID","PEPQ3","PSWQ3","PTQ3","RRS3","PTQ_core","PTQ_unprod","PTQ_mental", "RRS_ref","RRS_brood", "RRS_dep","p1","p2", "p3", "p4", "p5", "p6", "p7","n1", "n2", "n3", "n4", "n5", "n6", "n7","cort1","cort2","cort3","cort4","cort5","cort6")
#include only those who did time 3
cdf <- cdf[complete.cases(cdf$cort1),]
cdf$cort7 <- NA
library(reshape2)
library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smiths## The following object is masked from 'package:sjmisc':
##
## replace_nacdflong <- reshape(cdf, varying = c("p1","p2", "p3", "p4", "p5", "p6", "p7","n1", "n2", "n3", "n4", "n5", "n6", "n7","cort1","cort2","cort3","cort4","cort5","cort6","cort7"), timevar = "time", idvar = "ID", direction="long", sep = "")
cdflong$ID <- as.factor(cdflong$ID)
cdflong$time <- cdflong$time - 1
str(cdflong)## 'data.frame': 448 obs. of 15 variables:
## $ ID : Factor w/ 64 levels "5001","5002",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ PEPQ3 : int 700 1025 548 598 13 560 34 604 375 180 ...
## $ PSWQ3 : int 79 57 60 73 66 63 54 61 78 61 ...
## $ PTQ3 : int 39 34 50 23 45 38 40 28 39 25 ...
## $ RRS3 : int 66 46 60 29 78 65 52 30 50 45 ...
## $ PTQ_core : int 25 28 28 23 17 28 23 25 24 17 ...
## $ PTQ_unprod: int 7 8 6 5 5 9 6 7 5 2 ...
## $ PTQ_mental: int 8 5 6 2 4 5 6 5 7 6 ...
## $ RRS_ref : int 8 14 13 7 14 14 12 8 10 10 ...
## $ RRS_brood : int 8 14 7 11 17 15 12 12 13 10 ...
## $ RRS_dep : int 18 34 33 16 38 33 26 24 30 23 ...
## $ time : num 0 0 0 0 0 0 0 0 0 0 ...
## $ p : int 2 8 18 4 29 19 7 26 18 23 ...
## $ n : int 2 13 19 2 16 12 19 7 12 8 ...
## $ cort : num 0.101 0.579 0.616 0.308 0.223 0.185 0.442 0.411 0.438 0.445 ...
## - attr(*, "reshapeLong")=List of 4
## ..$ varying:List of 3
## .. ..$ p : chr "p1" "p2" "p3" "p4" ...
## .. ..$ n : chr "n1" "n2" "n3" "n4" ...
## .. ..$ cort: chr "cort1" "cort2" "cort3" "cort4" ...
## .. ..- attr(*, "v.names")= chr "p" "n" "cort"
## .. ..- attr(*, "times")= num 1 2 3 4 5 6 7
## ..$ v.names: chr "p" "n" "cort"
## ..$ idvar : chr "ID"
## ..$ timevar: chr "time"cdflong$time <- as.numeric(cdflong$time)
library(lmerTest)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loading required package: lme4##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(lme4)
library(sjPlot)
library(doBy)#remove basline
cdfsub <- subset(cdflong, cdflong$time !=0 & cdflong$time!=6)
subject <- levels(cdfsub$ID)
print(subject)## [1] "5001" "5002" "5003" "5004" "5005" "5007" "5008" "5009" "5010" "5012"
## [11] "5015" "5016" "5017" "5021" "5022" "5024" "5026" "5028" "5029" "5030"
## [21] "5032" "5033" "5034" "5035" "5036" "5037" "5038" "5039" "5040" "5041"
## [31] "5042" "5043" "5044" "5045" "5047" "5049" "5050" "5053" "5054" "5055"
## [41] "5056" "5057" "5058" "5060" "5061" "5062" "5063" "5065" "5066" "5067"
## [51] "5069" "5071" "5072" "5073" "5074" "5075" "5078" "5079" "5080" "5081"
## [61] "5084" "5085" "5086" "5088"To identify nonresponders, we examined the four cortisol records after the two baseline samples. If there was a clear peak at the stressor (i.e., cortisol recording #4 of 6), they were flagged as a responder. A peak was defined as a spike in cortisol at time points 2, 3, or 4 (defined as being 30% higher than the preceding or subsequent time point). Note that someone could potentially have more than 1 peaks (i.e., 2 and 4).
cdflong$responder_13 <- NA
for (i in 1:64) {
subdata <- subset(cdfsub, cdfsub$ID == subject[i])
subdata$subcheckstr <- NA
for (j in 2:4) {
subdata$subcheckstr[j] <- ifelse ((subdata$cort[j] >= (1.3*subdata$cort[j-1])) | (subdata$cort[j] >= (1.3*subdata$cort[j+1])), 1, 0)
}
rowmatch <- which(cdflong$ID == subject[i])
true <- which(subdata$subcheckstr==1)
cdflong$responder_13[rowmatch] <- ifelse(length(true) >=1, 1, 0)
}
responder_13_summary <- summaryBy(responder_13 ~ ID, data=cdflong)
responder_13_summary## ID responder_13.mean
## 1 5001 1
## 2 5002 1
## 3 5003 1
## 4 5004 1
## 5 5005 1
## 6 5007 1
## 7 5008 1
## 8 5009 1
## 9 5010 0
## 10 5012 1
## 11 5015 0
## 12 5016 1
## 13 5017 1
## 14 5021 1
## 15 5022 0
## 16 5024 0
## 17 5026 1
## 18 5028 0
## 19 5029 1
## 20 5030 1
## 21 5032 1
## 22 5033 1
## 23 5034 1
## 24 5035 1
## 25 5036 0
## 26 5037 1
## 27 5038 1
## 28 5039 1
## 29 5040 0
## 30 5041 0
## 31 5042 0
## 32 5043 1
## 33 5044 1
## 34 5045 1
## 35 5047 1
## 36 5049 1
## 37 5050 0
## 38 5053 1
## 39 5054 0
## 40 5055 0
## 41 5056 1
## 42 5057 0
## 43 5058 1
## 44 5060 1
## 45 5061 1
## 46 5062 1
## 47 5063 1
## 48 5065 1
## 49 5066 1
## 50 5067 0
## 51 5069 0
## 52 5071 0
## 53 5072 1
## 54 5073 1
## 55 5074 1
## 56 5075 1
## 57 5078 0
## 58 5079 1
## 59 5080 0
## 60 5081 1
## 61 5084 0
## 62 5085 1
## 63 5086 1
## 64 5088 1num_responder_13 <- which(responder_13_summary==1)
length(num_responder_13)## [1] 45cdflong$responder_13## [1] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1
## [36] 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1
## [71] 1 1 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 0 1
## [106] 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1
## [141] 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1
## [176] 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0
## [211] 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1
## [246] 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1 1
## [281] 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0
## [316] 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0
## [351] 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1
## [386] 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1
## [421] 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1rntwidefinal <- read.csv("RNT_wide_final_2.csv")
rntwidefinal$ID <- as.factor(rntwidefinal$ID)
rntwide_withresponders <- full_join(rntwidefinal, responder_13_summary, by="ID")
str(rntwide_withresponders)## 'data.frame': 90 obs. of 444 variables:
## $ X.1 : int 1 2 3 4 5 6 7 8 9 10 ...
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ ID : chr "5000" "5001" "5002" "5003" ...
## $ Rtinhibit : num NA NA 208.04 -1.15 186.45 ...
## $ STRPacc : num NA NA 0.973 0.959 0.932 ...
## $ STRPiacc : num NA NA 0.957 0.928 0.943 ...
## $ sw_costRT : num NA 179.9 301.6 48.1 221.3 ...
## $ sw_avg_ac : num NA 0.756 0.956 0.917 0.972 ...
## $ sw_inc_ac : num NA 0.736 0.921 0.911 0.967 ...
## $ sw_acc : num NA 0.747 0.934 0.923 0.967 ...
## $ sw_iacc : num NA 0.705 0.884 0.907 0.933 ...
## $ nback_rt : num 648 758 597 1320 625 ...
## $ nback_D : num 5.2 4.05 13.25 2.66 5.51 ...
## $ nback_ac : num 0.931 0.845 0.991 0.784 0.966 ...
## $ stress_2 : int 54 30 34 NA 66 112 NA NA NA NA ...
## $ NA_2 : num 24 36 63.75 NA 3.75 ...
## $ PA_2 : num 39.5 41.5 43.5 NA 75.2 ...
## $ Soc_2 : int 108 64 58 NA 116 188 NA NA NA NA ...
## $ PASSD_2 : logi NA NA NA NA NA NA ...
## $ NASSD_2 : logi NA NA NA NA NA NA ...
## $ stress_3 : int 62 91 89 92 28 104 15 100 70 NA ...
## $ NA_3 : num 25.8 29 47.8 51.8 2.5 ...
## $ PA_3 : num 50.2 56.5 41.2 71.5 75 ...
## $ Soc_3 : int 99 136 92 136 25 164 16 178 94 NA ...
## $ PASSD_3 : num 115.5625 225 5.0625 NA 0.0625 ...
## $ NASSD_3 : num 3.06 49 256 NA 1.56 ...
## $ stress_4 : int 53 58 63 11 48 52 12 96 75 22 ...
## $ NA_4 : num 55.5 46 51.2 24.5 15 ...
## $ PA_4 : num 36.5 69.8 50.2 51 67.2 ...
## $ Soc_4 : int 66 98 97 4 44 72 8 171 102 0 ...
## $ PASSD_4 : num 189.1 175.6 81 420.2 60.1 ...
## $ NASSD_4 : num 885.1 289 12.2 742.6 156.2 ...
## $ stress_5 : int NA 49 50 27 NA 117 NA 74 67 34 ...
## $ NA_5 : num NA 35.5 45.5 62 NA ...
## $ PA_5 : num NA 37.5 33 56.8 NA ...
## $ Soc_5 : int NA 80 68 51 NA 181 NA 85 104 28 ...
## $ PASSD_5 : num NA 1040.1 297.6 33.1 NA ...
## $ NASSD_5 : num NA 110.2 33.1 1406.2 NA ...
## $ stress_6 : int 44 63 96 30 63 59 26 86 68 18 ...
## $ NA_6 : num 63 50.8 16.2 59.8 23.2 ...
## $ PA_6 : num 28.2 55 69.5 74 57.8 ...
## $ Soc_6 : int 76 98 159 100 81 65 18 155 104 4 ...
## $ PASSD_6 : num NA 306 1332 298 NA ...
## $ NASSD_6 : num NA 232.56 855.56 5.06 NA ...
## $ stress_7 : int 42 73 36 86 10 42 37 33 68 25 ...
## $ NA_7 : num 36.2 21.2 69.2 33.5 58 ...
## $ PA_7 : num 33 36.8 31.8 91.5 35.8 ...
## $ Soc_7 : int 63 119 39 112 1 51 31 39 104 29 ...
## $ PASSD_7 : num 22.6 333.1 1425.1 306.2 484 ...
## $ NASSD_7 : num 716 870 2809 689 1208 ...
## $ stress_8 : int 51 44 56 82 12 68 9 33 66 107 ...
## $ NA_8 : num 39.2 41 43.5 40 70.8 ...
## $ PA_8 : num 47 44.2 45 81 19.5 ...
## $ Soc_8 : int 99 45 85 148 6 125 2 49 96 156 ...
## $ PASSD_8 : num 196 56.2 175.6 110.2 264.1 ...
## $ NASSD_8 : num 9 390.1 663.1 42.2 162.6 ...
## $ stress_9 : int 27 20 62 92 21 61 36 75 66 63 ...
## $ NA_9 : num 54 14.8 39.2 80.8 22.8 ...
## $ PA_9 : num 34.5 23 66.2 37.8 39.8 ...
## $ Soc_9 : int 31 18 124 150 17 91 58 84 104 80 ...
## $ PASSD_9 : num 156 452 452 1871 410 ...
## $ NASSD_9 : num 217.6 689.1 18.1 1660.6 2304 ...
## $ stress_10 : int 62 99 81 80 22 NA NA 88 67 115 ...
## $ NA_10 : num 27.2 17.8 64.2 18.5 14 ...
## $ PA_10 : num 39 48.8 33.8 70.8 59.8 ...
## $ Soc_10 : int 104 159 141 145 33 NA NA 103 99 122 ...
## $ PASSD_10 : num 20.2 663.1 1056.2 1089 400 ...
## $ NASSD_10 : num 715.6 9 625 3875.1 76.6 ...
## $ stress_11 : int 37 19 78 59 11 67 7 77 58 85 ...
## $ NA_11 : num 32.5 10.2 59 58 12.2 ...
## $ PA_11 : num 41.2 34.8 62 48 59 ...
## $ Soc_11 : int 46 18 102 108 10 121 0 91 85 136 ...
## $ PASSD_11 : num 5.062 196 798.062 517.562 0.562 ...
## $ NASSD_11 : num 27.56 56.25 27.56 1560.25 3.06 ...
## $ stress_12 : int 66 69 NA 61 7 NA 16 96 67 62 ...
## $ NA_12 : num 23.2 12.2 NA 42 12.2 ...
## $ PA_12 : num 26.2 25.2 NA 55.8 44.8 ...
## $ Soc_12 : int 70 110 NA 109 0 NA 9 170 112 70 ...
## $ PASSD_12 : num 225 90.2 NA 60.1 203.1 ...
## $ NASSD_12 : num 85.6 4 NA 256 0 ...
## $ stress_13 : int 46 13 24 83 19 NA NA 86 63 103 ...
## $ NA_13 : num 24.2 29.2 27.2 40.5 21.5 ...
## $ PA_13 : num 34.8 18 50.5 57.5 57.8 ...
## $ Soc_13 : int 63 23 45 165 17 NA NA 159 104 154 ...
## $ PASSD_13 : num 72.25 52.56 NA 3.06 169 ...
## $ NASSD_13 : num 1 289 NA 2.25 85.56 ...
## $ stress_14 : int 15 63 21 58 8 57 NA 29 55 41 ...
## $ NA_14 : num 37 52.8 72.5 47 79.5 ...
## $ PA_14 : num 46.2 42 22.8 61.8 32.2 ...
## $ Soc_14 : int 35 95 25 106 0 72 NA 42 84 38 ...
## $ PASSD_14 : num 132.2 576 770.1 18.1 650.2 ...
## $ NASSD_14 : num 162.6 552.2 2047.6 42.2 3364 ...
## $ stress_15 : int 45 52 76 65 NA 67 28 92 82 91 ...
## $ NA_15 : num 34.2 56.2 27.5 19 NA ...
## $ PA_15 : num 34.2 34.2 71 61.8 NA ...
## $ Soc_15 : int 69 46 103 114 NA 116 17 102 131 148 ...
## $ PASSD_15 : num 144 60.1 2328.1 0 NA ...
## $ NASSD_15 : num 7.56 12.25 2025 784 NA ...
## $ stress_16 : int 46 49 77 50 10 31 9 62 64 33 ...
## [list output truncated]colnames(rntwide_withresponders)[444] <- "responder_1.3"
colnames(rntwide_withresponders)## [1] "X.1" "X"
## [3] "ID" "Rtinhibit"
## [5] "STRPacc" "STRPiacc"
## [7] "sw_costRT" "sw_avg_ac"
## [9] "sw_inc_ac" "sw_acc"
## [11] "sw_iacc" "nback_rt"
## [13] "nback_D" "nback_ac"
## [15] "stress_2" "NA_2"
## [17] "PA_2" "Soc_2"
## [19] "PASSD_2" "NASSD_2"
## [21] "stress_3" "NA_3"
## [23] "PA_3" "Soc_3"
## [25] "PASSD_3" "NASSD_3"
## [27] "stress_4" "NA_4"
## [29] "PA_4" "Soc_4"
## [31] "PASSD_4" "NASSD_4"
## [33] "stress_5" "NA_5"
## [35] "PA_5" "Soc_5"
## [37] "PASSD_5" "NASSD_5"
## [39] "stress_6" "NA_6"
## [41] "PA_6" "Soc_6"
## [43] "PASSD_6" "NASSD_6"
## [45] "stress_7" "NA_7"
## [47] "PA_7" "Soc_7"
## [49] "PASSD_7" "NASSD_7"
## [51] "stress_8" "NA_8"
## [53] "PA_8" "Soc_8"
## [55] "PASSD_8" "NASSD_8"
## [57] "stress_9" "NA_9"
## [59] "PA_9" "Soc_9"
## [61] "PASSD_9" "NASSD_9"
## [63] "stress_10" "NA_10"
## [65] "PA_10" "Soc_10"
## [67] "PASSD_10" "NASSD_10"
## [69] "stress_11" "NA_11"
## [71] "PA_11" "Soc_11"
## [73] "PASSD_11" "NASSD_11"
## [75] "stress_12" "NA_12"
## [77] "PA_12" "Soc_12"
## [79] "PASSD_12" "NASSD_12"
## [81] "stress_13" "NA_13"
## [83] "PA_13" "Soc_13"
## [85] "PASSD_13" "NASSD_13"
## [87] "stress_14" "NA_14"
## [89] "PA_14" "Soc_14"
## [91] "PASSD_14" "NASSD_14"
## [93] "stress_15" "NA_15"
## [95] "PA_15" "Soc_15"
## [97] "PASSD_15" "NASSD_15"
## [99] "stress_16" "NA_16"
## [101] "PA_16" "Soc_16"
## [103] "PASSD_16" "NASSD_16"
## [105] "stress_17" "NA_17"
## [107] "PA_17" "Soc_17"
## [109] "PASSD_17" "NASSD_17"
## [111] "stress_18" "NA_18"
## [113] "PA_18" "Soc_18"
## [115] "PASSD_18" "NASSD_18"
## [117] "stress_19" "NA_19"
## [119] "PA_19" "Soc_19"
## [121] "PASSD_19" "NASSD_19"
## [123] "stress_20" "NA_20"
## [125] "PA_20" "Soc_20"
## [127] "PASSD_20" "NASSD_20"
## [129] "stress_21" "NA_21"
## [131] "PA_21" "Soc_21"
## [133] "PASSD_21" "NASSD_21"
## [135] "stress_22" "NA_22"
## [137] "PA_22" "Soc_22"
## [139] "PASSD_22" "NASSD_22"
## [141] "stress_23" "NA_23"
## [143] "PA_23" "Soc_23"
## [145] "PASSD_23" "NASSD_23"
## [147] "stress_24" "NA_24"
## [149] "PA_24" "Soc_24"
## [151] "PASSD_24" "NASSD_24"
## [153] "stress_25" "NA_25"
## [155] "PA_25" "Soc_25"
## [157] "PASSD_25" "NASSD_25"
## [159] "stress_26" "NA_26"
## [161] "PA_26" "Soc_26"
## [163] "PASSD_26" "NASSD_26"
## [165] "stress_27" "NA_27"
## [167] "PA_27" "Soc_27"
## [169] "PASSD_27" "NASSD_27"
## [171] "stress_28" "NA_28"
## [173] "PA_28" "Soc_28"
## [175] "PASSD_28" "NASSD_28"
## [177] "stress_29" "NA_29"
## [179] "PA_29" "Soc_29"
## [181] "PASSD_29" "NASSD_29"
## [183] "stress_30" "NA_30"
## [185] "PA_30" "Soc_30"
## [187] "PASSD_30" "NASSD_30"
## [189] "stress_31" "NA_31"
## [191] "PA_31" "Soc_31"
## [193] "PASSD_31" "NASSD_31"
## [195] "stress_32" "NA_32"
## [197] "PA_32" "Soc_32"
## [199] "PASSD_32" "NASSD_32"
## [201] "stress_33" "NA_33"
## [203] "PA_33" "Soc_33"
## [205] "PASSD_33" "NASSD_33"
## [207] "EMA_NAMSSD" "EMA_PAMSSD"
## [209] "EMA_NA_AVG" "EMA_PA_AVG"
## [211] "EMAStressReact_b" "EMAStressReact_se"
## [213] "ASI" "ASI_soc"
## [215] "ASI_ph" "ASI_cog"
## [217] "FSIS" "GAD7"
## [219] "IUS" "IUS_pa"
## [221] "IUS_ia" "OCIR"
## [223] "OCIRchk" "OCIRord"
## [225] "OCIRwash" "OCIRobs"
## [227] "PEPQ" "PEPQavg"
## [229] "PHQ9" "PSWQ"
## [231] "PSWQ_tot" "PTQ"
## [233] "RFQ" "RRS"
## [235] "SHAPS" "SAD"
## [237] "SADavg" "SIAS"
## [239] "SIR" "SIR_d"
## [241] "SIR_a" "URG"
## [243] "URGavg" "PU"
## [245] "Puavg" "ASRM"
## [247] "binge" "purge"
## [249] "restric" "exerc"
## [251] "IES" "INQ"
## [253] "sASI2" "sASI_soc2"
## [255] "sASI_phy2" "sASI_cog2"
## [257] "FSIS2" "GAD7_2"
## [259] "IUS2" "IUS_pa2"
## [261] "IUS_ia2" "OCIR2"
## [263] "OCIRchk2" "OCIRord2"
## [265] "OCIRwsh2" "OCIRobs2"
## [267] "PEPQ2" "PEPQavg2"
## [269] "PHQ9_2" "PSWQ2"
## [271] "PTQ2" "RRS2"
## [273] "SHAPS2" "SIAS2"
## [275] "SIR2" "SIR_d2"
## [277] "SIR_a2" "URG2"
## [279] "URGavg2" "PU2"
## [281] "PUavg2" "ASRM_2"
## [283] "binge_2" "purge_2"
## [285] "restric_2" "exerc_2"
## [287] "INQ_2" "ALC2"
## [289] "ASI3" "ASI_soc3"
## [291] "ASI_phy3" "ASI_cog3"
## [293] "FSIS3" "GAD7_3"
## [295] "IUS3" "IUS_pa3"
## [297] "IUS_ia3" "OCIR3"
## [299] "OCIRchk3" "OCIRord3"
## [301] "OCIRwsh3" "OCIRobs3"
## [303] "PEPQ3" "PEPQavg3"
## [305] "PHQ9_3" "PSWQ3"
## [307] "PTQ3" "RRS3"
## [309] "SHAPS3" "SIAS3"
## [311] "SIR3" "SIR_d3"
## [313] "SIR_a3" "URG3"
## [315] "URGavg3" "PU3"
## [317] "PUavg3" "ASRM_3"
## [319] "binge_3" "purge_3"
## [321] "restric_3" "exerc_3"
## [323] "INQ_3" "ALC3"
## [325] "Drugs_3" "PTSD3"
## [327] "RIQ3" "pan_p1"
## [329] "pan_n1" "pan_p2"
## [331] "pan_n2" "pan_p3"
## [333] "pan_n3" "pan_p4"
## [335] "pan_n4" "pan_p5"
## [337] "pan_n5" "pan_p6"
## [339] "pan_n6" "pan_p7"
## [341] "pan_n7" "pan_ndiff32"
## [343] "pan_ndiff43" "pan_ndiff54"
## [345] "pan_ndiff65" "pan_pdiff32"
## [347] "pan_pdiff43" "pan_pdiff54"
## [349] "pan_pdiff65" "pan_ndiff42"
## [351] "pan_pdiff42" "OC1_loglambda_strictOC_0"
## [353] "OC2_loglambda_relaxedOC_0" "OC2_Rho_OC_0"
## [355] "OC1_sqrtRho_OC_0" "OC1_loglambda_strictOC_1"
## [357] "OC2_loglambda_relaxedOC_1" "OC2_Rho_OC_1"
## [359] "OC1_sqrtRho_OC_1" "OC1_loglambda_strictOC_2"
## [361] "OC2_loglambda_relaxedOC_2" "OC2_Rho_OC_2"
## [363] "OC1_sqrtRho_OC_2" "OC1_loglambda_strictOC_3"
## [365] "OC2_loglambda_relaxedOC_3" "OC2_Rho_OC_3"
## [367] "OC1_sqrtRho_OC_3" "OC1_loglambda21"
## [369] "OC1_loglambda31" "OC1_loglambda32"
## [371] "OC1_rho21" "OC1_rho31"
## [373] "OC1_rho32" "OC2_loglambda21"
## [375] "OC2_loglambda31" "OC2_loglambda32"
## [377] "OC2_rho21" "OC2_rho31"
## [379] "OC2_rho32" "PTQ_core"
## [381] "PTQ_unprod" "PTQ_mental"
## [383] "RRS_ref" "RRS_brood"
## [385] "RRS_dep" "PTQ_core2"
## [387] "PTQ_unprod2" "PTQ_mental2"
## [389] "RRS_ref2" "RRS_brood2"
## [391] "RRS_dep2" "PTQ_core3"
## [393] "PTQ_unprod3" "PTQ_mental3"
## [395] "RRS_ref3" "RRS_brood3"
## [397] "RRS_dep3" "Cortisol.nmol.L.0"
## [399] "Cortisol.nmol.L.1" "Cortisol.nmol.L.2"
## [401] "Cortisol.nmol.L.3" "Cortisol.nmol.L.4"
## [403] "Cortisol.nmol.L.5" "Time.collected.0"
## [405] "Time.collected.1" "MinutesEllapsed_fromprevious.1"
## [407] "Cumulativeminutes.1" "Time.collected.2"
## [409] "MinutesEllapsed_fromprevious.2" "Cumulativeminutes.2"
## [411] "Time.collected.3" "MinutesEllapsed_fromprevious.3"
## [413] "Cumulativeminutes.3" "Time.collected.4"
## [415] "MinutesEllapsed_fromprevious.4" "Cumulativeminutes.4"
## [417] "Time.collected.5" "MinutesEllapsed_fromprevious.5"
## [419] "Cumulativeminutes.5" "OC3_lambda_log31"
## [421] "OC3_lambda_log32" "OC3_lambda_log21"
## [423] "OC3_rho31" "OC3_rho32"
## [425] "OC3_rho21" "cortresponders_OLDVARIABLE"
## [427] "AUCg1" "AUCg2"
## [429] "AUCg3" "AUCg4"
## [431] "AUCg5" "OC4_rho3"
## [433] "OC4_lambdalog3" "OC4_rho1"
## [435] "OC4_lambdalog1" "OC4_rho2"
## [437] "OC4_lambdalog2" "OC4_rho21"
## [439] "OC4_rho31" "OC4_rho32"
## [441] "OC4_loglambda21" "OC4_loglambda31"
## [443] "OC4_loglambda32" "responder_1.3"write.csv(rntwide_withresponders, file = "RNT_wide_final_responders.csv",row.names=FALSE)