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Oxytocin modulates the neurocomputational mechanisms engaged in learning rank relationships in social networks.

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  1. [1] § Materials and Methods › Behavioral Analysis. ↔ behavior data/oxtfmri.R, lines 564–625 · score 0.65 · linear mixed model, lme4, LMM, lmer, RT, Block

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R · 657 lines · 36 KB · no license · 1 match

  1. rm(list = ls())
  3. library(plyr)
  4. library(dplyr)
  5. library(tibble)
  6. library(tidyr)
  7. library(ggplot2)
  8. library(fpc)
  9. library(car)
  10. library(ez)
  11. library(HH)
  12. library(reshape)
  13. library(RColorBrewer)
  14. library(Rmisc)
  15. library(sjstats)
  16. library(gridExtra)
  17. library(lme4)
  18. library(nlme)
  19. library(utils)
  20. library(phia)
  21. library(pastecs)
  22. library(psych)
  23. library(ggpubr)
  24. library(ggsignif)
  25. library(emmeans)
  26. library(reshape)
  27. library(stringr)
  28. library(pbkrtest)
  29. library(EMAtools)
  30. library(lmerTest)
  31. library(remotes)
  32. library(parallel)
  33. library(broom)
  34. library(MuMIn)
  35. library(rlist)
  36. library(ggplot2)
  37. library(grImport)
  38. library(Rcpp)
  39. library(magick)
  40. library(grid)
  41. library(DMwR2)
  42. library(effsize)
  43. library(effects)
  44. library(report)
  45. library(TMB)
  46. library(sjPlot)
  47. library(sjmisc)
  48. library(psycho)
  49. library(parameters)
  50. library(performance)
  51. library(prediction)
  52. library(ggeffects)
  53. library(Cairo)
  54. library(xlsx)
  55. library(rJava)
  56. library(XLConnect)
  57. library(openxlsx)
  58. library(patchwork)
  59. library(scales)
  60. library(jsonlite)
  61. library(purrr)
  62. library(data.table)
  63. library(rio)
  64. library(cowplot)
  65. library(ggalt)
  66. library(tidyr)
  67. library(ggpattern)
  68. library(readxl)
  69. library(StanHeaders)
  70. library(qpcR)
  71. library(rlist)
  72. library(r2symbols)
  73. library(wesanderson)
  75. '%!in%' <- function(x,y)!('%in%'(x,y))
  78. # OT: sub
  79. # 3,4,5,6,8,10,14,15,19,[20],21,(22),25,29,30,31,32,33,37,38,39,40,41,43,46,50,51,53,56,60,61
  80. # Placebo: sub
  81. # 1,2,7,9,11,12,13,16,17,18,23,24,26,27,28,34,35,36,42,44,[45],47,48,49,52,54,55,57,58,59
  83. # sub47, has 4 runs, delete the 3rd
  84. # sub22---delete
  85. # sub20,45 --delete perfome
  88. ############ LEARN #############
  91. ####1. simple contrast coding ####
  92. #however,it's not work in some factor() related lmer.
  93. learndata_long3$Group = ordered(learndata_long3$Group, levels=c('Oxytocin','Placebo'))
  94. learndata_long3$S_O = ordered(learndata_long3$S_O, levels=c('Self','Other'))
  95. learndata_long3$Hierarchy1st = ordered(learndata_long3$Hierarchy1st, levels=c("Superior","Intermediate", "Inferior"))
  96. learndata_long3$Hierarchy2nd = ordered(learndata_long3$Hierarchy2nd, levels=c("Superior","Intermediate", "Inferior",'Unrelated'))
  97. learndata_long3$LR3rd = ordered(learndata_long3$LR3rd, levels=c('Greater than','Equal','Less than'))
  98. learndata_long3$Familiarity = ordered(learndata_long3$Familiarity, levels=c('Related','Unrelated'))
  100. #rename colname in contrast #!!!***name! not contrast it self!!!!2022
  101. colnames(attr(learndata_long3$Group, "contrasts")) <- c(" (PB)")
  102. colnames(attr(learndata_long3$S_O, "contrasts")) <- c(" (Others)")
  103. colnames(attr(learndata_long3$Hierarchy1st, "contrasts")) <- c(" (Sup > Inter)"," (Inf > Inter)")
  104. colnames(attr(learndata_long3$Hierarchy2nd, "contrasts")) <- c(" (Superior)"," (Inferior)"," (Unrelated)")
  105. colnames(attr(learndata_long3$LR3rd, "contrasts")) <- c(" (Greater)"," (Less)")
  106. colnames(attr(learndata_long3$Familiarity, "contrasts"))<- c(" (Related)")
  109. # 1.1 different with ramdon choice
  111. #block 4 starts to learn
  112. xtabs(~Block ,learndata_long33) #1390 1392 1390 1390 1392 1391 1392 1390 1391 [1390/2=695]
  113. try=xtabs(~(Block==7)+ ACC,learndata_long33)[2,] #true 0 1
  115. Trainramdon<-as.data.frame(matrix(nrow=9,ncol=2))
  116. for (i in 1:9)
  117. {Trainramdon[i,1]<- xtabs(~(Block==i)+ ACC,learndata_long33)[2,1]
  118. Trainramdon[i,2]<- xtabs(~(Block==i)+ ACC,learndata_long33)[2,2]}
  120. Trainramdonlist<-as.data.frame(matrix(nrow=length(unique(learndata_long33$Listname)),ncol=2))
  121. listnamenum=c('List23','List24','List25','List31','List32', 'List35','List36','List37','List43','List44',
  122. 'List26','List27','List38','List39',
  123. 'List28','List29','List30','List33', 'List34', 'List40','List41','List42','List45','List46') #unique(learndata_long33$Listname)
  125. for (i in 1:length(listnamenum))
  126. {Trainramdonlist[i,1]<- xtabs(~(Listname==listnamenum[i])+ ACC,learndata_long33)[2,1]
  127. Trainramdonlist[i,2]<- xtabs(~(Listname==listnamenum[i])+ ACC,learndata_long33)[2,2]}
  129. Trainchis<- list()
  130. for (ii in 1:length(listnamenum))
  131. {Trainchis$'X-squared'[ii]<- chisq.test(rbind(Trainramdonlist[ii,],c(522*2/3,522*1/3)))
  132. Trainchis$p[ii]<- chisq.test(rbind(Trainramdonlist[ii,],c(348,174)))[3] }
  135. ### ACC numeric ####
  137. # 2021.4--derivsFdrop2int2drop
  138. vNglmernew<-list()
  140. vNglmernew[["derivsF"]]<-glmer(ACC~Group*S_O*Block*Hierarchy1st+ (1| Subject),data = learndata_long33, glmerControl(calc.derivs = F,optCtrl = list(maxfun=1e5)),family="binomial")
  141. vNglmernew[["derivsFdrop2int"]] <-update(vNglmernew[["derivsF"]], .~. -Group:S_O:Block:Hierarchy1st- Group:S_O:Hierarchy1st -Group:S_O:Block -Group:Block:Hierarchy1st -S_O:Block:Hierarchy1st ) # -2*2
  142. vNglmernew[["derivsFdrop2int2drop"]] <-update(vNglmernew[["derivsFdrop2int"]], .~. -Group:S_O -Group:Hierarchy1st -S_O:Block) #final!!!***
  144. #without block 1 guess--
  145. vNglmernew[["derivsFnob1"]]<-glmer(ACC~Group*S_O*Block*Hierarchy1st+ (1| Subject),data = learndata_long33[which(learndata_long33$Block != 1 ),],
  146. glmerControl(calc.derivs = F,optCtrl = list(maxfun=1e5)),family="binomial")
  147. vNglmernew[["derivsFdrop2intnob1"]] <-update(vNglmernew[["derivsFnob1"]], .~. -Group:S_O:Block:Hierarchy1st- Group:S_O:Hierarchy1st -Group:S_O:Block -Group:Block:Hierarchy1st -S_O:Block:Hierarchy1st ) # -2*2
  148. vNglmernew[["derivsFdrop2int2dropnob1"]] <-update(vNglmernew[["derivsFdrop2intnob1"]], .~. -Group:S_O -Group:Hierarchy1st -S_O:Block) #final!!!***
  151. #session for emmeans
  152. vNglmernew[["derivsS"]]<-glmer(ACC~Group*S_O*Session*Hierarchy1st+ (1| Subject),data = learndata_long33, glmerControl(calc.derivs = F,optCtrl = list(maxfun=1e5)),family="binomial")
  153. vNglmernew[["derivsSd1"]]<- update(vNglmernew[["derivsS"]], .~. -Group:S_O:Session:Hierarchy1st- Group:S_O:Hierarchy1st -Group:S_O:Session -Group:Session:Hierarchy1st -S_O:Session:Hierarchy1st )
  154. vNglmernew[["derivsSd2"]]<- update(vNglmernew[["derivsSd1"]], .~. -Group:S_O -Group:Hierarchy1st -S_O:Session) #***
  157. summary(vNglmernew[["derivsFdrop2int2drop"]])
  159. ### results ACC Nglmernew ####
  161. vNglmernew=vNglmernew[sort(names(vNglmernew))]
  163. vNglmernewAICSIN=cbind(sapply(vNglmernew,AIC),sapply(vNglmernew,BIC),sapply(vNglmernew,isSingular)) %>% as.data.frame()
  164. vNglmernewAICSIN<- vNglmernewAICSIN[order(vNglmernewAICSIN$V1),] #blocksubl3 13505.00 1
  165. vNglmernewsum=lapply(vNglmernew,summary)
  166. vNglmernewAnova=lapply(within(vNglmernew, rm(Null)),Anova) #must fixed
  167. vNglmernewanova=lapply(vNglmernew,anova)
  168. vNglmernewPCA=lapply(vNglmernew,rePCA) # proportion of variance in subject or other random effects
  169. vNglmernewcorr=lapply(vNglmernew,VarCorr) #random effects variance in std.dev whose small~0 !same PCA
  170. vNglmernewcohend=lapply(vNglmernew,function(i) {rbind(c(0,0,0),lme.dscore(i,learndata_long33, type="lme4"))}) # effect size Cohen's D
  173. #odds ratios
  174. vNglmernewse=lapply(vNglmernew,function(i) {sqrt(diag(vcov(i)))}) # se
  175. vNglmernewconfint=list() # estimated beita~
  176. vNglmernewconfintodd=list() # odds ratios~
  177. Est=list()
  178. LL=list() #%95
  179. UL=list()
  180. for (i in 1:length(vNglmernew)){
  181. vNglmernewconfint[[i]]= cbind( Est=fixef(vNglmernew[[i]]),LL=fixef(vNglmernew[[i]]) -1.96 * vNglmernewse[[i]],UL=fixef(vNglmernew[[i]]) +1.96 * vNglmernewse[[i]]) %>%
  182. as.data.frame()
  183. names(vNglmernewconfint)[i]=names(vNglmernew)[i]
  184. vNglmernewconfintodd[[i]]= exp(vNglmernewconfint[[i]]) %>% plyr::rename(c("Est" = "Odds ratios", 'LL'='Odds LL', 'UL'='Odds UL'))
  185. names(vNglmernewconfintodd)[i]=names(vNglmernew)[i] } # odds ratios~
  187. #result combine
  188. #cat(paste("'",names(vNglmernewpara[[i]]),"'", sep = "", collapse = ", "))
  190. vNglmernewpara=list()
  191. vNglmernewperform=list()
  192. for (i in 1:length(vNglmernew)){
  193. vNglmernewpara[[i]]= cbind(model_parameters(vNglmernew[[i]],effects="fixed", exponentiate = T),vNglmernewconfintodd[[i]],vNglmernewcohend[[i]] ) %>% # b~p + odds +cohend
  194. mutate_if(is.numeric, round, digits=3)
  195. names(vNglmernewpara)[i]=names(vNglmernew)[i]
  197. for (j in 1:nrow(vNglmernewpara[[i]])) {
  198. if(vNglmernewpara[[i]]$p[j] < 0.001 ){vNglmernewpara[[i]]$Coefficient[j] <- paste(vNglmernewpara[[i]]$Coefficient[j], c('***'))}
  199. else if(0.001 <= vNglmernewpara[[i]]$p[j] & vNglmernewpara[[i]]$p[j] < 0.01 ){vNglmernewpara[[i]]$Coefficient[j] <- paste(vNglmernewpara[[i]]$Coefficient[j], c('**'))}
  200. else if(0.01 < vNglmernewpara[[i]]$p[j]& vNglmernewpara[[i]]$p[j] < 0.05 ){vNglmernewpara[[i]]$Coefficient[j] <- paste(vNglmernewpara[[i]]$Coefficient[j], c('*'))}
  201. # else {vNglmernewpara[[i]]$p[j] <- paste(vNglmernewpara[[i]]$p[j],c('')) }
  202. }
  204. vNglmernewpara[[i]]$Parameter<-str_replace_all(vNglmernewpara[[i]]$Parameter,c(":"=" × "))
  205. # vNglmernewpara[[i]]$Parameter<-str_replace_all(vNglmernewpara[[i]]$Parameter,c(
  206. # "Hierarchy1st1" = "Hierarchy: Superior","Hierarchy1st2" = "Hierarchy: Inferior","S_O1"="S_O: Self",
  207. # "Hierarchy2nd1" = "Hierarchy: Superior","Hierarchy2nd2" = "Hierarchy: Inferior","Hierarchy2nd3" = "Hierarchy: Unrelated",
  208. # "Familiarity1" = "Inner Group: Related","Group1" ="Group: Oxytocin"))
  209. vNglmernewpara[[i]]<-subset(vNglmernewpara[[i]], select=names(vNglmernewpara[[i]])!='df_error')
  211. vNglmernewperform[[i]]= model_performance(vNglmernew[[i]]) %>% #AIC~
  212. mutate_if(is.numeric, round, digits=3)
  213. names(vNglmernewperform)[i]=names(vNglmernew)[i]
  214. }
  216. listsumlearnACC <- do.call("rbind", lapply(vNglmernewpara[c('derivsFdrop2int2drop',"derivsFdrop2int", "derivsF")],
  217. function(i) {as.data.frame(i,col.names =names(vNglmernewpara$derivsFdrop))}))
  220. ### pair test marginal effect
  222. #odds ratios!!!
  223. #1. hierachy ----|2021.4--derivsFdrop2int2drop --no
  224. derivsFdropHier1=emmeans(vNglmernew$derivsFdrop2int2drop,pairwise ~ "Hierarchy1st", type = "response", reverse = TRUE)
  225. CIderivsFdropHier1= confint(derivsFdropHier1,level = .95, type = "response") #all
  226. hier1mainodds= cbind(as.data.frame(derivsFdropHier1$contrasts),CIderivsFdropHier1$contrasts[names(CIderivsFdropHier1$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  227. mutate_if(is.numeric, round, digits=3)
  229. #1. hierachy ----|2022.7--derivsSd2
  230. derivsFdropHierDB1=emmeans(vNglmernew$derivsSd2,pairwise ~ "Hierarchy1st", type = "response", reverse = TRUE)
  231. CIderivsFdropHierDB1= confint(derivsFdropHierDB1,level = .95, type = "response") #all
  232. hier1mainoddsDB1= cbind(as.data.frame(derivsFdropHierDB1$contrasts),CIderivsFdropHierDB1$contrasts[names(CIderivsFdropHierDB1$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  233. mutate_if(is.numeric, round, digits=3)
  235. #1.1 SO
  236. derivsFdropSO1=emmeans(vNglmernew$derivsFdrop2int2drop,pairwise ~ "S_O", type = "response", reverse = TRUE) #odds ratios!!!
  237. CIderivsFdropSO1= confint(derivsFdropSO1,level = .95, type = "response") #all
  238. SO1mainodds= cbind(as.data.frame(derivsFdropSO1$contrasts),CIderivsFdropSO1$contrasts[names(CIderivsFdropSO1$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  239. mutate_if(is.numeric, round, digits=3)
  241. emmeans(vNglmernew$derivsFdrop2int2drop,pairwise ~ Group|S_O, type = "response", reverse = TRUE) #no
  242. emmeans(vNglmernew$derivsFdrop2int2drop,pairwise ~ S_O|Group, type = "response", reverse = TRUE) #** both
  244. #2. group*block
  245. derivsFdropgrpbloc1=emmeans(vNglmernew$derivsFdrop2int2drop, pairwise~ Group|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  246. CIderivsFdropgrpbloc1= confint(derivsFdropgrpbloc1,level = .95, type = "response") #no!
  247. grpblo1odds= cbind(as.data.frame(derivsFdropgrpbloc1$contrasts),CIderivsFdropgrpbloc1$contrasts[names(CIderivsFdropgrpbloc1$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  248. mutate_if(is.numeric, round, digits=3)
  250. emmeans(vNglmernew$derivsFdrop2int2drop, pairwise~ Group:Block, at = list(Block = c(-16)), type = "response", reverse = TRUE) # *?
  251. emmeans(vNglmernew$derivsFdrop2int2drop,pairwise ~ Group:Block, type = "response", reverse = TRUE) #
  254. #3. SO*hier ----|2022.7--derivsSd2
  255. derivsFdropSohierDB1=emmeans(vNglmernew$derivsSd2, pairwise~ S_O|Hierarchy1st, type = "response", reverse = TRUE)
  256. CIderivsFdropSohierDB1= confint(derivsFdropSohierDB1,level = .95, type = "response") #intermediate
  257. Sohier1oddsDB= cbind(as.data.frame(derivsFdropSohierDB1$contrasts),CIderivsFdropSohierDB1$contrasts[names(CIderivsFdropSohierDB1$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  258. mutate_if(is.numeric, round, digits=3)
  261. #4. hierachy*block
  262. derivsFdrophierbloc1=emmeans(vNglmernew$derivsFdrop2int2drop, pairwise~ Hierarchy1st|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  263. CIderivsFdrophierbloc1= confint(derivsFdrophierbloc1,level = .95, type = "response") # except blo1 super&infer + blo8/9 inter&inferior
  264. hierblo1odds= cbind(as.data.frame(derivsFdrophierbloc1$contrasts),CIderivsFdrophierbloc1$contrasts[names(CIderivsFdrophierbloc1$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  265. mutate_if(is.numeric, round, digits=3)
  267. #5.all in block
  268. derivsFdropbloc1=emmeans(vNglmernew$derivsFdrop2int2drop, pairwise~ Block, type = "response", at = list(Block = c(1:9))) #every all~
  269. CIderivsFdropbloc1= confint(derivsFdropbloc1,level = .95, type = "response")
  271. GlmerpairACCtrain= rbind.fill(hier1mainodds,SO1mainodds,grpblo1odds,Sohier1odds,hierblo1odds)
  273. for (j in 1:nrow(GlmerpairACCtrain)) {
  274. if(GlmerpairACCtrain$p.value[j] < 0.001 ){GlmerpairACCtrain$odds.ratio[j] <- paste(GlmerpairACCtrain$odds.ratio[j], c('***'))}
  275. else if(0.001 <= GlmerpairACCtrain$p.value[j] & GlmerpairACCtrain$p.value[j] < 0.01 ){GlmerpairACCtrain$odds.ratio[j] <- paste(GlmerpairACCtrain$odds.ratio[j], c('**'))}
  276. else if(0.01 < GlmerpairACCtrain$p.value[j]& GlmerpairACCtrain$p.value[j] < 0.05 ){GlmerpairACCtrain$odds.ratio[j] <- paste(GlmerpairACCtrain$odds.ratio[j], c('*'))}
  277. }
  280. ## main effect -A:B1/B2-
  281. convNglmernew= vector("list",length(vNglmernew))
  282. convFac= vector("list",length(names(ref_grid(vNglmernew[[length(vNglmernew)]])@levels)))
  284. for (i in 1:length(vNglmernew)){
  285. for (j in 1:length(names(ref_grid(vNglmernew[[i]])@levels))) {
  286. convFac[[j]]=emmeans(vNglmernew[[i]], as.formula(paste("pairwise ~",names(ref_grid(vNglmernew[[i]])@levels)[j])), adjust ="bonf")
  287. names(convFac)[j]=names(ref_grid(vNglmernew[[i]])@levels)[j]} #onelmer1[j]~ update the emmeans name!
  288. convNglmernew[[i]]=convFac
  289. names(convNglmernew)[i]=names(vNglmernew)[i] }
  292. ## interaction effect: simple effects-- A1/A2:B1/B2
  293. intervNglmernew= vector("list",length(vNglmernew))
  294. intervFac= vector("list",length(names(ref_grid(vNglmernew[[length(vNglmernew)]])@levels)))
  296. for (i in 1:length(vNglmernew)){
  297. for (j in 1:length(names(ref_grid(vNglmernew[[i]])@levels))) {
  298. intervFac[[j]]=joint_tests(vNglmernew[[i]], as.formula(paste("by='",names(ref_grid(vNglmernew[[i]])@levels)[j],"'", sep = "")))
  299. names(intervFac)[j]=names(ref_grid(vNglmernew[[i]])@levels)[j]}
  300. intervNglmernew[[i]]=intervFac
  301. names(intervNglmernew)[i]=names(vNglmernew)[i]}
  304. ####RT numeric####
  306. vNlmerRT<- list()
  308. vNlmerRT[["derivsF"]]<-lmerTest::lmer(RT~Group*S_O*Block*Hierarchy1st+ (1| Subject),data = learndata_long33,REML = F)
  309. vNlmerRT[["derivsFdrop2int"]]<-update(vNlmerRT[["derivsF"]], .~. -Group:S_O:Block:Hierarchy1st - S_O:Hierarchy1st:Block - Group:S_O:Hierarchy1st- Group:Block:Hierarchy1st -Group:S_O:Block) # but no main effect, the interaction is weird
  310. vNlmerRT[["derivsFdrop2int2drop"]]<-update(vNlmerRT[["derivsFdrop2int"]], .~. -Group:Hierarchy1st-Group:S_O ) # final!*** and group show marginal
  311. vNlmerRT[["derivsFdrop2int2drop1"]]<-update(vNlmerRT[["derivsFdrop2int2drop"]], .~. -Block:Hierarchy1st) # final!*** and group show marginal
  314. vNlmerRT[["derivsF"]]<-lmerTest::lmer(RT~Group*S_O*Block*Hierarchy1st+ (1| Subject),data = learndata_long33,REML = F)
  315. vNlmerRT[["derivsFdrop2int"]]<-update(vNlmerRT[["derivsF"]], .~. -Group:S_O:Block:Hierarchy1st - S_O:Hierarchy1st:Block - Group:S_O:Hierarchy1st- Group:Block:Hierarchy1st -Group:S_O:Block) # but no main effect, the interaction is weird
  316. vNlmerRT[["derivsFdrop2int2drop"]]<-update(vNlmerRT[["derivsFdrop2int"]], .~. -Group:Hierarchy1st-Group:S_O ) # final!*** and group show marginal
  317. vNlmerRT[["derivsFdrop2int2drop1"]]<-update(vNlmerRT[["derivsFdrop2int2drop"]], .~. -Block:Hierarchy1st) # final!*** and group show marginal
  320. ### results RT Nlmer ####
  321. vNlmerRT=vNlmerRT[sort(names(vNlmerRT))]
  323. vNlmerRTAICSIN=cbind(sapply(vNlmerRT,AIC),sapply(vNlmerRT,BIC),sapply(vNlmerRT,isSingular)) %>% as.data.frame()
  324. vNlmerRTAICSIN<- vNlmerRTAICSIN[order(vNlmerRTAICSIN$V1),]
  325. vNlmerRTsum=lapply(vNlmerRT,summary)
  326. vNlmerRTAnova=lapply(vNlmerRT,Anova)
  327. vNlmerRTanova=lapply(vNlmerRT,anova)
  328. vNlmerRTPCA=lapply(vNlmerRT,rePCA) # proportion of variance in subject or other random effects
  329. vNlmerRTcorr=lapply(vNlmerRT,VarCorr) # small~0 !same PCA
  330. vNlmerRTcohend=lapply(vNlmerRT,function(i) {rbind(c(0,0,0),lme.dscore(i,learndata_long33, type="lme4"))}) # effect size cohend
  333. #ODDS
  334. vNglmernewseRT=lapply(vNlmerRT,function(i) {sqrt(diag(vcov(i)))}) # se
  335. vNglmernewconfintRT=list() # estimated beita~
  336. vNglmernewconfintoddRT=list() # odds ratios~
  337. Est=list()
  338. LL=list() #%95
  339. UL=list()
  340. for (i in 1:length(vNlmerRT)){
  341. vNglmernewconfintRT[[i]]= cbind( Est=fixef(vNlmerRT[[i]]),LL=fixef(vNlmerRT[[i]]) -1.96 * vNglmernewseRT[[i]],UL=fixef(vNlmerRT[[i]]) +1.96 * vNglmernewseRT[[i]]) %>%
  342. as.data.frame()
  343. names(vNglmernewconfintRT)[i]=names(vNlmerRT)[i]
  344. vNglmernewconfintoddRT[[i]]= exp(vNglmernewconfintRT[[i]]) %>% plyr::rename(c("Est" = "Odds ratios", 'LL'='Odds LL', 'UL'='Odds UL'))
  345. names(vNglmernewconfintoddRT)[i]=names(vNlmerRT)[i]
  346. vNlmerRTcohend[[i]]=plyr::rename(vNlmerRTcohend[[i]],c("t" = "cohedt")) #double t in cohed so rename
  347. } # odds ratios~
  350. vNglmernewRTpara=list()
  351. vNglmernewperformRT=list()
  352. for (i in 1:length(vNlmerRT)){
  353. vNglmernewRTpara[[i]]= cbind(model_parameters(vNlmerRT[[i]],effects="fixed"),vNglmernewconfintoddRT[[i]],vNlmerRTcohend[[i]] )%>%
  354. mutate_if(is.numeric, round, digits=3)
  355. names(vNglmernewRTpara)[i]=names(vNlmerRT)[i]
  357. for (j in 1:nrow(vNglmernewRTpara[[i]])) {
  358. if(vNglmernewRTpara[[i]]$p[j] < 0.001 ){vNglmernewRTpara[[i]]$Coefficient[j] <- paste(vNglmernewRTpara[[i]]$Coefficient[j], c('***'))}
  359. else if(0.001 <= vNglmernewRTpara[[i]]$p[j] & vNglmernewRTpara[[i]]$p[j] < 0.01 ){vNglmernewRTpara[[i]]$Coefficient[j] <- paste(vNglmernewRTpara[[i]]$Coefficient[j], c('**'))}
  360. else if(0.01 < vNglmernewRTpara[[i]]$p[j]& vNglmernewRTpara[[i]]$p[j] < 0.05 ){vNglmernewRTpara[[i]]$Coefficient[j] <- paste(vNglmernewRTpara[[i]]$Coefficient[j], c('*'))}
  361. else {vNglmernewRTpara[[i]]$p[j] <- paste(vNglmernewRTpara[[i]]$p[j],c('')) }}
  363. vNglmernewRTpara[[i]]$Parameter<-str_replace_all(vNglmernewRTpara[[i]]$Parameter,c(":"=" × "))
  364. vNglmernewRTpara[[i]]<-subset(vNglmernewRTpara[[i]], select=names(vNglmernewRTpara[[i]])!='df_error')
  366. vNglmernewperformRT[[i]]= model_performance(vNlmerRT[[i]]) %>% #AIC~
  367. mutate_if(is.numeric, round, digits=3)
  368. names(vNglmernewperformRT)[i]=names(vNlmerRT)[i]}
  372. ### pair test marginal effect |2021.10--derivsF no
  373. emm_options((pbkrtest.limit = 30000))
  374. #1. hierachy ,pbkrtest.limit = 12518 ajust='bonferroni',
  375. derivsFdropHier1RT=emmeans(vNlmerRT$derivsFdrop2int2drop1,pairwise ~ "Hierarchy1st", type = "response", reverse = TRUE) #ESTIMATE
  376. CIderivsFdropHier1RT= confint(derivsFdropHier1RT,level = .95, type = "response") #all
  377. hier1mainoddsRT= cbind(as.data.frame(derivsFdropHier1RT$contrasts),CIderivsFdropHier1RT$contrasts[names(CIderivsFdropHier1RT$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  378. mutate_if(is.numeric, round, digits=3)
  380. #2. group*block
  381. derivsFdropgrpbloc1RT=emmeans(vNlmerRT$derivsFdrop2int2drop1, pairwise~ Group|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  382. CIderivsFdropgrpbloc1RT= confint(derivsFdropgrpbloc1RT,level = .95, type = "response") #no!
  383. grpblo1oddsRT= cbind(as.data.frame(derivsFdropgrpbloc1RT$contrasts),CIderivsFdropgrpbloc1RT$contrasts[names(CIderivsFdropgrpbloc1RT$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  384. mutate_if(is.numeric, round, digits=3)
  386. #3. SO*group
  387. derivsFdropSogrp1RT=emmeans(vNlmerRT$derivsFdrop2int2drop1, pairwise~ S_O|Group, type = "response", reverse = TRUE)
  388. CIderivsFdropSogrp1RT= confint(derivsFdropSogrp1RT,level = .95, type = "response") #intermediate
  389. Sogrp1oddsRT= cbind(as.data.frame(derivsFdropSogrp1RT$contrasts),CIderivsFdropSogrp1RT$contrasts[names(CIderivsFdropSogrp1RT$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  390. mutate_if(is.numeric, round, digits=3)
  392. #3.1 SO*hierachy
  393. derivsFdropSohier1RT=emmeans(vNlmerRT$derivsFdrop2int2drop1, pairwise~ S_O|Hierarchy1st, type = "response", reverse = TRUE)
  394. CIderivsFdropSohier1RT= confint(derivsFdropSohier1RT,level = .95, type = "response") #intermediate
  395. Sohier1oddsRT= cbind(as.data.frame(derivsFdropSohier1RT$contrasts),CIderivsFdropSohier1RT$contrasts[names(CIderivsFdropSohier1RT$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  396. mutate_if(is.numeric, round, digits=3)
  398. #4. SO*block
  399. derivsFdropSObloc1RT=emmeans(vNlmerRT$derivsFdrop2int2drop1, pairwise~ S_O|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  400. CIderivsFdropSObloc1RT= confint(derivsFdropSObloc1RT,level = .95, type = "response") # except blo1 super&infer + blo8/9 inter&inferior
  401. SOblo1oddsRT= cbind(as.data.frame(derivsFdropSObloc1RT$contrasts),CIderivsFdropSObloc1RT$contrasts[names(CIderivsFdropSObloc1RT$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  402. mutate_if(is.numeric, round, digits=3) #blo5
  404. #5. group*block*SO
  405. derivsFdropgrpSOgrpbloc1RT=emmeans(vNlmerRT$derivsFdrop2int2drop1, pairwise~ S_O|Group|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  406. CIderivsFdropSOgrpbloc1RT= confint(derivsFdropgrpSOgrpbloc1RT,level = .95, type = "response") #no!
  407. SOgrpblo1oddsRT= cbind(as.data.frame(derivsFdropgrpSOgrpbloc1RT$contrasts),CIderivsFdropSOgrpbloc1RT$contrasts[names(CIderivsFdropSOgrpbloc1RT$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  408. mutate_if(is.numeric, round, digits=3)
  410. GlmerpairRTtrain= rbind.fill(hier1mainoddsRT,grpblo1oddsRT,Sogrp1oddsRT,SOblo1oddsRT,SOgrpblo1oddsRT,Sohier1oddsRT)
  412. for (j in 1:nrow(GlmerpairRTtrain)) {
  413. if(GlmerpairRTtrain$p.value[j] < 0.001 ){GlmerpairRTtrain$estimate[j] <- paste(GlmerpairRTtrain$estimate[j], c('***'))}
  414. else if(0.001 <= GlmerpairRTtrain$p.value[j] & GlmerpairRTtrain$p.value[j] < 0.01 ){GlmerpairRTtrain$estimate[j] <- paste(GlmerpairRTtrain$estimate[j], c('**'))}
  415. else if(0.01 < GlmerpairRTtrain$p.value[j]& GlmerpairRTtrain$p.value[j] < 0.05 ){GlmerpairRTtrain$estimate[j] <- paste(GlmerpairRTtrain$estimate[j], c('*'))}
  416. }
  419. #####…………………………##########
  420. ############ TEST #############
  423. ####1. simple contrast coding ####
  425. testdata_long3$Group = ordered(testdata_long3$Group, levels=c('Oxytocin','Placebo'))
  426. testdata_long3$S_O = ordered(testdata_long3$S_O, levels=c('Self','Other'))
  427. testdata_long3$THierarchy1st = ordered(testdata_long3$THierarchy1st, levels=c('Intermediate','1RankInterval', '2RankInterval', '3RankInterval'))
  428. testdata_long3$THierarchy2nd = ordered(testdata_long3$THierarchy2nd, levels=c('Superior', 'Intermediate', 'Inferior', 'Mix') )
  429. testdata_long3$TR3rd = ordered(testdata_long3$TR3rd, levels=c('Greater than','Equal','Less than'))
  431. contrasts(testdata_long3$Group)=solve(t(matrix(c(1/2,1/2, 1,-1), ncol=2)))[,2] # Oxytocin -0.5 Placebo 0.5 #lib{YawMMF}contr.simple(2)
  432. contrasts(testdata_long3$S_O)=solve(t(matrix(c(1/2,1/2, 1,-1), ncol=2)))[,2] # Self -0.5 Other 0.5 #
  433. contrasts(testdata_long3$Block)=contr.simple(9) #2-1 ~
  434. contrasts(testdata_long3$THierarchy1st)=solve(t(matrix(c(1/4,1/4,1/4,1/4, -1,1,0,0, -1,0,1,0, -1,0,0,1), ncol=4)))[,2:4]
  435. contrasts(testdata_long3$THierarchy2nd)=solve(t(matrix(c(1/4,1/4,1/4,1/4, 1,-1,0,0, 0,-1,1,0, 0,-1,0,1), ncol=4)))[,2:4]
  436. contrasts(testdata_long3$TR3rd)=solve(t(matrix(c(1/3,1/3,1/3, 1,-1,0, 0,-1,1), ncol=3)))[,2:3]
  438. #rename colname in contrast
  439. colnames(attr(testdata_long3$Group, "contrasts")) <- c(" (PB)")
  440. colnames(attr(testdata_long3$S_O, "contrasts")) <- c(" (Others)")
  441. colnames(attr(testdata_long3$THierarchy1st, "contrasts")) <- c(" (1RankInterval)"," (2RankInterval)"," (3RankInterval)")
  442. colnames(attr(testdata_long3$THierarchy2nd, "contrasts")) <- c(" (Superior)"," (Inferior)"," (Mix)")
  443. colnames(attr(testdata_long3$TR3rd, "contrasts")) <- c(" (Greater)"," (Less)")
  445. testdata_long3[c("Block","Group","S_O","THierarchy1st","THierarchy2nd","TR3rd")]=
  446. lapply(testdata_long3[c("Block","Group","S_O","THierarchy1st","THierarchy2nd","TR3rd")], as.factor)
  449. #### Treat as numeric####
  451. testdata_long33=testdata_long3
  452. testdata_long33$Block = as.numeric(testdata_long33$Block)
  454. ###ACC numeric####
  456. #
  457. vNglmertest<- list()
  458. #only self--Grop*block*self | 2021.4 final**
  459. vNglmertest[["derivsFS"]]<-glmer(ACC~Group*S_O*Block+ (1| Subject),data = testdata_long33, glmerControl(calc.derivs = F,optCtrl = list(maxfun=1e5)),family="binomial")
  460. vNglmertest[["derivsFSdrop"]]<-update(vNglmertest[["derivsFS"]], .~.- Group:S_O:Block-S_O:Block) #good!
  461. vNglmertest[["Tinterval1st"]]<-glmer(ACC~Group*S_O*Block*TRANK1st+ (1| Subject),data = testdata_long33, glmerControl(calc.derivs = F,optCtrl = list(maxfun=1e5)),family="binomial")
  462. vNglmertest[["Tinterval1stdrop"]]<-update(vNglmertest[["Tinterval1st"]], .~. -Group:S_O -S_O:TRANK1st
  463. -Group:S_O:Block -Group:S_O:TRANK1st - Group:Block:TRANK1st -S_O:Block:TRANK1st - Group:S_O:Block:TRANK1st) #
  465. ### results ACC test Nglmer report ####
  467. vNglmertest=vNglmertest[sort(names(vNglmertest))]
  469. vNglmertestAICSIN=cbind(sapply(vNglmertest,AIC),sapply(vNglmertest,BIC),sapply(vNglmertest,isSingular)) %>% as.data.frame()
  470. vNglmertestAICSIN<- vNglmertestAICSIN[order(vNglmertestAICSIN$V1),] #blocksubl3 13505.00 1
  471. vNglmertestsum=lapply(vNglmertest,summary)
  472. vNglmertestAnova=lapply(within(vNglmertest, rm(Null)),Anova) #must fixed
  473. vNglmertestanova=lapply(vNglmertest,anova)
  474. vNglmertestPCA=lapply(vNglmertest,rePCA) # proportion of variance in subject or other random effects
  475. vNglmertestcorr=lapply(vNglmertest,VarCorr) #random effects variance in std.dev whose small~0 !same PCA
  476. vNglmertestcohend=lapply(vNglmertest,function(i) {rbind(c(0,0,0),lme.dscore(i,testdata_long33, type="lme4"))}) # effect size cohend
  478. #vNglmertestACC= lapply(vNglmertest,model_performance)
  480. #odds ratios
  481. vNglmertestse=lapply(vNglmertest,function(i) {sqrt(diag(vcov(i)))}) # se
  482. vNglmertestconfint=list() # estimated beita~
  483. vNglmertestconfintodd=list() # odds ratios~
  484. Est=list()
  485. LL=list() #%95
  486. UL=list()
  487. for (i in 1:length(vNglmertest)){
  488. vNglmertestconfint[[i]]= cbind( Est=fixef(vNglmertest[[i]]),LL=fixef(vNglmertest[[i]]) -1.96 * vNglmertestse[[i]],UL=fixef(vNglmertest[[i]]) +1.96 * vNglmertestse[[i]]) %>%
  489. as.data.frame()
  490. names(vNglmertestconfint)[i]=names(vNglmertest)[i]
  491. vNglmertestconfintodd[[i]]= exp(vNglmertestconfint[[i]]) %>% plyr::rename(c("Est" = "Odds ratios", 'LL'='Odds LL', 'UL'='Odds UL'))
  492. names(vNglmertestconfintodd)[i]=names(vNglmertest)[i] } # odds ratios~95
  495. vNglmertestpara=list()
  496. vNglmertestperform=list()
  497. for (i in 1:length(vNglmertest)){
  498. vNglmertestpara[[i]]= cbind(model_parameters(vNglmertest[[i]],effects="fixed", exponentiate = T),vNglmertestconfintodd[[i]],vNglmertestcohend[[i]] ) %>% # b~p + odds +cohend
  499. mutate_if(is.numeric, round, digits=3)
  500. names(vNglmertestpara)[i]=names(vNglmertest)[i]
  502. for (j in 1:nrow(vNglmertestpara[[i]])) {
  503. if(vNglmertestpara[[i]]$p[j] < 0.001 ){vNglmertestpara[[i]]$Coefficient[j] <- paste(vNglmertestpara[[i]]$Coefficient[j], c('***'))}
  504. else if(0.001 <= vNglmertestpara[[i]]$p[j] & vNglmertestpara[[i]]$p[j] < 0.01 ){vNglmertestpara[[i]]$Coefficient[j] <- paste(vNglmertestpara[[i]]$Coefficient[j], c('**'))}
  505. else if(0.01 < vNglmertestpara[[i]]$p[j]& vNglmertestpara[[i]]$p[j] < 0.05 ){vNglmertestpara[[i]]$Coefficient[j] <- paste(vNglmertestpara[[i]]$Coefficient[j], c('*'))}
  506. else {vNglmertestpara[[i]]$p[j] <- paste(vNglmertestpara[[i]]$p[j],c('')) }}
  508. vNglmertestpara[[i]]$Parameter<-str_replace_all(vNglmertestpara[[i]]$Parameter,c(":"=" × "))
  509. vNglmertestpara[[i]]<-subset(vNglmertestpara[[i]], select=names(vNglmertestpara[[i]])!='df_error')
  511. vNglmertestperform[[i]]= model_performance(vNglmertest[[i]]) %>% #AIC~
  512. mutate_if(is.numeric, round, digits=3)
  513. names(vNglmertestperform)[i]=names(vNglmertest)[i]
  514. }
  517. listsumtestACC <- do.call("rbind", vNglmertestpara)
  519. listAICtestACC <-rbind.fill(vNglmertestperform)
  520. row.names(listAICtestACC)<- names(vNglmertestperform)
  523. ### pair test marginal
  525. ##odds ratio !!
  526. #1. group*block |derivsFSdrop---2021.10
  527. derivsFdropgrpbloctest1=emmeans(vNglmertest$derivsFSdrop, pairwise~ Group|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  528. CIderivsFdropgrpbloctest1= confint(derivsFdropgrpbloctest1,level = .95, type = "response") #no!
  529. grpblo1oddstest= cbind(as.data.frame(derivsFdropgrpbloctest1$contrasts),CIderivsFdropgrpbloctest1$contrasts[names(CIderivsFdropgrpbloctest1$contrasts) %in% c("asymp.LCL", "asymp.UCL")]) %>%
  530. mutate_if(is.numeric, round, digits=3)
  532. #2.group*SO
  533. derivsFdropSogrptest1=emmeans(vNglmertest$derivsFSdrop, pairwise~ S_O|Group, type = "response", reverse = TRUE)
  534. CIderivsFdropSogrptest1= confint(derivsFdropSogrptest1,level = .95, type = "response") #intermediate
  535. Sogrp1oddstest= cbind(as.data.frame(derivsFdropSogrptest1$contrasts),CIderivsFdropSogrptest1$contrasts[names(CIderivsFdropSogrptest1$contrasts) %in% c("asymp.LCL", "asymp.UCL")]) %>%
  536. mutate_if(is.numeric, round, digits=3)
  538. GlmerpairACCtest= rbind.fill(grpblo1oddstest,Sogrp1oddstest)
  540. for (j in 1:nrow(GlmerpairACCtest)) {
  541. if(GlmerpairACCtest$p.value[j] < 0.001 ){GlmerpairACCtest$odds.ratio[j] <- paste(GlmerpairACCtest$odds.ratio[j], c('***'))}
  542. else if(0.001 <= GlmerpairACCtest$p.value[j] & GlmerpairACCtest$p.value[j] < 0.01 ){GlmerpairACCtest$odds.ratio[j] <- paste(GlmerpairACCtest$odds.ratio[j], c('**'))}
  543. else if(0.01 < GlmerpairACCtest$p.value[j]& GlmerpairACCtest$p.value[j] < 0.05 ){GlmerpairACCtest$odds.ratio[j] <- paste(GlmerpairACCtest$odds.ratio[j], c('*'))}
  544. }
  546. describetestACCRT=rBind(describeBy(testdata_long33~Group, mat=TRUE),describeBy(testdata_long33~S_O, mat=TRUE))
  548. set_theme(base = theme_classic(), axis.textsize = .9)
  549. PACCtest_LRSodds= plot_model(vNglmertest$derivsFSdrop, type = "est",wrap.labels = 50, axis.lim = c(0.1,5),show.values = TRUE, show.intercept = T, value.offset = .3,colors = c("blue","red"),title = '',
  550. axis.labels = rev(c("(Intercept)", "Group (PB)","S_O (Others)","Block" , "Group (PB) × S_O (Others)","Group (PB) × Block" ))) # value.size = 2,
  552. pACC_Test=c('PACCtest_LRSodds')
  553. lapply(pACC_Test,function(x){ggsave(file=paste("D:/fmriOT/faststone/",x,"png",sep="."),dpi = 300, width =14, height = 10, units = "cm",type="cairo",get(x))})
  555. #t1st interval
  556. plot_model(vNglmertest$Tinterval1stdrop, type = "est",wrap.labels = 50, axis.lim = c(0.1,5),show.values = TRUE, show.intercept = T, value.offset = .3,colors = c("blue","red"),title = '') # value.size = 2,
  558. plot_model(vNglmertest$Tinterval1st, type = "est",wrap.labels = 50, axis.lim = c(0.1,5),show.values = TRUE, show.intercept = T, value.offset = .3,colors = c("blue","red"),title = '') # value.size = 2,
  562. ####RT numeric####
  564. # LMM(Linear Mixed Model) with ML
  565. testdata_long33$RT=as.numeric(testdata_long33$RT)
  567. vNlmertestRT<- list()
  569. vNlmertestRT[["derivsFS"]]<-lmerTest::lmer(RT~Group*S_O*Block+ (1| Subject),data = testdata_long33, REML = F )
  570. vNlmertestRT[["derivsFSdrop"]]<-update(vNlmertestRT[["derivsFS"]], .~.- Group:S_O:Block-S_O:Group) #good!
  573. ### RT Nlmer report results ####
  574. vNlmertestRT=vNlmertestRT[sort(names(vNlmertestRT))]
  576. vNlmertestRTAICSIN=cbind(sapply(vNlmertestRT,AIC),sapply(vNlmertestRT,BIC),sapply(vNlmertestRT,isSingular)) %>% as.data.frame()
  577. vNlmertestRTAICSIN<- vNlmertestRTAICSIN[order(vNlmertestRTAICSIN$V1),]
  578. vNlmertestRTsum=lapply(vNlmertestRT,summary)
  579. vNlmertestRTAnova=lapply(vNlmertestRT,Anova)
  580. vNlmertestRTanova=lapply(vNlmertestRT,anova)
  581. vNlmertestRTPCA=lapply(vNlmertestRT,rePCA) # proportion of variance in subject or other random effects
  582. vNlmertestRTcorr=lapply(vNlmertestRT,VarCorr) # small~0 !same PCA
  583. vNlmertestRTcohend=lapply(vNlmertestRT,function(i) {rbind(c(0,0,0),lme.dscore(i,testdata_long33, type="lme4"))}) # effect size cohend
  585. #ODDS
  586. vNglmertestseRT=lapply(vNlmertestRT,function(i) {sqrt(diag(vcov(i)))}) # se
  587. vNglmertestconfintRT=list() # estimated beita~
  588. vNglmertestconfintoddRT=list() # odds ratios~
  589. Est=list()
  590. LL=list() #%95
  591. UL=list()
  592. for (i in 1:length(vNlmertestRT)){
  593. vNglmertestconfintRT[[i]]= cbind( Est=fixef(vNlmertestRT[[i]]),LL=fixef(vNlmertestRT[[i]]) -1.96 * vNglmertestseRT[[i]],UL=fixef(vNlmertestRT[[i]]) +1.96 * vNglmertestseRT[[i]]) %>%
  594. as.data.frame()
  595. names(vNglmertestconfintRT)[i]=names(vNlmertestRT)[i]
  596. vNglmertestconfintoddRT[[i]]= exp(vNglmertestconfintRT[[i]]) %>% plyr::rename(c("Est" = "Odds ratios", 'LL'='Odds LL', 'UL'='Odds UL'))
  597. names(vNglmertestconfintoddRT)[i]=names(vNlmertestRT)[i]
  598. vNlmertestRTcohend[[i]]=plyr::rename(vNlmertestRTcohend[[i]],c("t" = "cohedt")) #double t in cohed so rename
  599. } # odds ratios~
  601. vNglmertestRTpara=list()
  602. vNglmertestperformRT=list()
  603. for (i in 1:length(vNlmertestRT)){
  604. vNglmertestRTpara[[i]]= cbind(model_parameters(vNlmertestRT[[i]],effects="fixed"),vNglmertestconfintoddRT[[i]],vNlmertestRTcohend[[i]] )%>%
  605. mutate_if(is.numeric, round, digits=3)
  606. names(vNglmertestRTpara)[i]=names(vNlmertestRT)[i]
  608. for (j in 1:nrow(vNglmertestRTpara[[i]])) {
  609. if(vNglmertestRTpara[[i]]$p[j] < 0.001 ){vNglmertestRTpara[[i]]$Coefficient[j] <- paste(vNglmertestRTpara[[i]]$Coefficient[j], c('***'))}
  610. else if(0.001 <= vNglmertestRTpara[[i]]$p[j] & vNglmertestRTpara[[i]]$p[j] < 0.01 ){vNglmertestRTpara[[i]]$Coefficient[j] <- paste(vNglmertestRTpara[[i]]$Coefficient[j], c('**'))}
  611. else if(0.01 < vNglmertestRTpara[[i]]$p[j]& vNglmertestRTpara[[i]]$p[j] < 0.05 ){vNglmertestRTpara[[i]]$Coefficient[j] <- paste(vNglmertestRTpara[[i]]$Coefficient[j], c('*'))}
  612. else {vNglmertestRTpara[[i]]$p[j] <- paste(vNglmertestRTpara[[i]]$p[j],c('')) }}
  614. vNglmertestRTpara[[i]]$Parameter<-str_replace_all(vNglmertestRTpara[[i]]$Parameter,c(":"=" × "))
  615. vNglmertestRTpara[[i]]<-subset(vNglmertestRTpara[[i]], select=names(vNglmertestRTpara[[i]])!='df_error')
  617. vNglmertestperformRT[[i]]= model_performance(vNlmertestRT[[i]]) %>% #AIC~
  618. mutate_if(is.numeric, round, digits=3)
  619. names(vNglmertestperformRT)[i]=names(vNlmertestRT)[i]
  620. }
  622. listsumtestRT <- do.call("rbind",vNglmertestRTpara)
  624. listAICtestRT <-rbind.fill(vNglmertestperformRT[c( "derivsFS","derivsFSdrop")])
  625. row.names(listAICtestRT)<-c( "derivsFS","derivsFSdrop")
  628. ### pair test marginal effect
  629. emm_options((pbkrtest.limit = 30000))
  631. #1. group*block
  632. #derivsFS --2021.10--no
  633. derivsFdropgrpbloc1RTtest=emmeans(vNlmertestRT$derivsFSdrop, pairwise~ Group|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  634. CIderivsFdropgrpbloc1RTtest= confint(derivsFdropgrpbloc1RTtest,level = .95, type = "response") #no!
  635. grpblo1oddsRTtest= cbind(as.data.frame(derivsFdropgrpbloc1RTtest$contrasts),CIderivsFdropgrpbloc1RTtest$contrasts[names(CIderivsFdropgrpbloc1RTtest$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  636. mutate_if(is.numeric, round, digits=3)
  638. #4. SO*block
  639. derivsFdropSObloc1RTtest=emmeans(vNlmertestRT$derivsFSdrop, pairwise~ S_O|Block, at = list(Block = c(1:9)), type = "response", reverse = TRUE) #every all~
  640. CIderivsFdropSObloc1RTtest= confint(derivsFdropSObloc1RTtest,level = .95, type = "response") #
  641. SOblo1oddsRTtest= cbind(as.data.frame(derivsFdropSObloc1RTtest$contrasts),CIderivsFdropSObloc1RTtest$contrasts[names(CIderivsFdropSObloc1RTtest$contrasts) %in% c( "asymp.LCL" , "asymp.UCL" )]) %>%
  642. mutate_if(is.numeric, round, digits=3) #blo5
  645. GlmerpairRTtest= rbind.fill(grpblo1oddsRTtest,SOblo1oddsRTtest)
  647. for (j in 1:nrow(GlmerpairRTtest)) {
  648. if(GlmerpairRTtest$p.value[j] < 0.001 ){GlmerpairRTtest$estimate[j] <- paste(GlmerpairRTtest$estimate[j], c('***'))}
  649. else if(0.001 <= GlmerpairRTtest$p.value[j] & GlmerpairRTtest$p.value[j] < 0.01 ){GlmerpairRTtest$estimate[j] <- paste(GlmerpairRTtest$estimate[j], c('**'))}
  650. else if(0.01 < GlmerpairRTtest$p.value[j]& GlmerpairRTtest$p.value[j] < 0.05 ){GlmerpairRTtest$estimate[j] <- paste(GlmerpairRTtest$estimate[j], c('*'))}
  651. }
  653. #
  654. set_theme(base = theme_classic(), axis.textsize = .9)
  655. PRT_TRFSodds=plot_model(vNlmertestRT$derivsFSdrop, type = "est",wrap.labels = 50,axis.lim = c(-1,3),value.size = 4, show.values = TRUE, show.intercept = T, value.offset = .3,colors = c("blue","red"),title = '',
  656. axis.labels = rev(c('(Intercept)','Group (PB)','S_O (Others)','Block', 'Group (PB) × Block', 'S_O (Others) × Block')))

oxtfmri.R, no license · at the source

Overview

Authors: Jiawei Liu1,2,3,4, Chen Qu1,2, Rémi Philippe3,4, Siying Li1,2,5, Edmund Derrington3,4, Jean-Claude Dreher3,4
ORCID iDs: Rémi Philippe, Edmund Derrington, Jean-Claude Dreher
  1. Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China
  2. School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
  3. Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon 69675, France
  4. Université Claude Bernard Lyon 1, Lyon 69100, France
  5. Faculty of Education, Northeast Normal University, Changchun 130024, China
Institutions: Lyon 1 Université (France); South China Normal University (China); Institut des Sciences Cognitives Marc Jeannerod (France); Northeast Normal University (China)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 25, article e2606871123
Dates: received 25 February 2026; accepted 30 April 2026; published online 15 June 2026; in print 23 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2606871123 · PMID 42296342 · PMCID PMC13291526 · OpenAlex W7164835576
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Statistics, Preprocessing, fMRI & imaging
Keywords: oxytocin, social hierarchy, social memory, reinforcement learning
MeSH: Hierarchy, Social*, Learning*, Oxytocin*, Social Networking*, Adult, Amygdala, Female, Humans, Magnetic Resonance Imaging, Male, Memory, Prefrontal Cortex, Young Adult (* major topic)
Topic: Neuroendocrine regulation and behavior (Social Psychology, Psychology), according to OpenAlex
Funding: Agence Nationale de la Recherche (ANR-24-CE37-4261, ANR-21-CE37-0032, ANR-11-IDEX-007, ANR-11-LABX-0042); South China Normal University (22JJD19000)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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OSF wjbpz

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (6), R (1)
Size: 69 files, 7 scripts
Software Heritage: not checked
Found in: “Data, Materials, and Software Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (1 file), car (1 file), cowplot (1 file), data.table (1 file), easystats (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), nlme (1 file), patchwork (1 file), psych (1 file), reshape2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/wjbpz/overview?view_only=d9ed0a26788b45a98e51e367059e0588

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 7 scripts, each with its path and the digest of its content;
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Code and data availability statement

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Read it in the paper: doi.org/10.1073/pnas.2606871123.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 13 MeSH terms, 2 funders, 53 references.

Cite

This paper

Liu, J., Qu, C., Philippe, R., Li, S., Derrington, E., & Dreher, J.-C. (2026). Oxytocin modulates the neurocomputational mechanisms engaged in learning rank relationships in social networks. Proceedings of the National Academy of Sciences of the United States of America, 123(25), e2606871123. https://doi.org/10.1073/pnas.2606871123

BibTeX

@article{liu2026oxytocin,
author = {Liu, Jiawei and Qu, Chen and Philippe, Rémi and Li, Siying and Derrington, Edmund and Dreher, Jean-Claude},
title = {{Oxytocin modulates the neurocomputational mechanisms engaged in learning rank relationships in social networks}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = jun,
volume = {123},
number = {25},
pages = {e2606871123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2606871123},
url = {https://doi.org/10.1073/pnas.2606871123},
pmid = {42296342},
pmcid = {PMC13291526}
}

RIS

TY - JOUR
AU - Liu, Jiawei
AU - Qu, Chen
AU - Philippe, Rémi
AU - Li, Siying
AU - Derrington, Edmund
AU - Dreher, Jean-Claude
TI - Oxytocin modulates the neurocomputational mechanisms engaged in learning rank relationships in social networks
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/06/15
VL - 123
IS - 25
SP - e2606871123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2606871123
UR - https://doi.org/10.1073/pnas.2606871123
LA - en
ER -

CSL-JSON

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"id": "10.1073/pnas.2606871123",
"type": "article-journal",
"title": "Oxytocin modulates the neurocomputational mechanisms engaged in learning rank relationships in social networks",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
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"family": "Liu",
"given": "Jiawei"
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"PMID": "42296342",
"PMCID": "PMC13291526",
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"issued": {
"date-parts": [
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]
}
}

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