# hello, web3

**Published by:** [xiaoyuzeng_psychbrain](https://paragraph.com/@xiaoyuzeng-psychbrain/)
**Published on:** 2022-01-13
**URL:** https://paragraph.com/@xiaoyuzeng-psychbrain/hello-web3

## Content

title: "R_psychologicalTraits_xiaoyuzeng_211228"output: word_document: default html_document: default pdf_document: defaultrm(list = ls()) libraries#library(bruceR) #library(lmerTest) # Packages required for Chapter 10 #library(knitr) #library(gridExtra) #library(GGally) #library(mice) #library(nlme) #library(lme4) #library(mnormt) #library(boot) #library(HLMdiag) #library(kableExtra) #library(pander) library(tidyverse) #library(ggstatsplot) library(psych) library(GPArotation) library(nFactors) #library(caret) #library(Hmisc) library(ggthemes) library(factoextra) library(FactoMineR) #library(randomForest) library(reshape2) library("ggsci") library("ggpubr") source("D:/微云同步助手/754424340/kvip/03data analysis/batch/LA/mainfigure/geom_flat_violin.R") source("D:/微云同步助手/754424340/kvip/03data analysis/batch/LA/mainfigure/summarySE.R") source("D:/微云同步助手/754424340/kvip/03data analysis/batch/LA/mainfigure/R_rainclouds.R") data importpass_check_data<-read.csv('Experiment2_data.CSV',header=T)#n=2631 PCA principle factor analysiscols <- c("IRI2", "IRI3", "IRI4", "IRI6", "IRI8", "IRI9", "IRI10", "IRI11", "IRI13", "IRI14","IRI15", "IRI17", "IRI18","IRI19","IRI20","IRI21","IRI22", "IRI24","IRI25","IRI27","IRI28", "PSY1", "PSY2", "PSY3", "PSY4", "PSY5", "PSY6","PSY7", "PSY8", "PSY9", "PSY10", "PSY11", "PSY12","PSY13", "PSY14","PSY15", "PSY16", "PSY17","PSY18","PSY19","PSY20","PSY21","PSY22", "PSY23","PSY24","PSY25","PSY26","PSY27","PSY28","PSY29","PSY30", "GASP1", "GASP2", "GASP3", "GASP4", "GASP5", "GASP6", "GASP7", "GASP8", "GASP9", "GASP10", "GASP11", "GASP12","GASP13", "GASP14","GASP15", "GASP16", "SC1", "SC2", "SC3", "SC4", "SC5", "SC6", "SC7", "SC8", "SC9", "SC10", "SC11", "SC12","SC13", "SC14","SC15", "SC16", "SC17", "SC18","SC19","SC20","SC21","SC22", "SC23","SC24","SC25","SC26", "GBJWS1", "GBJWS2", "GBJWS3", "GBJWS4", "GBJWS5", "GBJWS6", "GBJWS7", "GBJWS8", "GBJWS9", "GBJWS10", "GBJWS11", "GBJWS12","GBJWS13", "AS1", "AS2", "AS3", "AS4", "AS5", "AS6", "AS7", "AS8", "AS9", "AS10", "AS11", "AS12","AS13", "AS14","AS15", "AS16", "AS17", "AS18","AS19","AS20") data_for_factor_analysis <- pass_check_data[,cols] # Pearson correlation corr_mat <- cor (data_for_factor_analysis) ev <- eigen(corr_mat) eva <- ev$values # Determine the number of factors numFact <- nCng(eva, model="factors", details=TRUE) plotuScree(eva, main=paste(numFact$nFactors, " factors retained by the CNG procedure", sep="")) # Factor analysis fa.new <- fa(r = corr_mat, nfactors = 3, n.obs = nrow(data_for_factor_analysis), rotate = "oblimin", fm = "ml", scores = "regression") fa.scores <- factor.scores(x=data_for_factor_analysis,f=fa.new) scores = data.frame(fa.scores$scores) loadings <- fa.new$loadings # add scores to all subs data pass_check_data$factor1_score <- scores$ML1 pass_check_data$factor2_score <- scores$ML2 pass_check_data$factor3_score <- scores$ML3 #output results write.csv(pass_check_data,'Experiment2_data.csv') write.csv(scores,'factor_scores_R.csv') write.csv(loadings,'factor_loading_R.csv') #allData$F1 <- scores$ML1 #allData$F2 <- scores$ML2 #allData$F3 <- scores$ML3 #1compute PCA res.pca <- prcomp(data_for_factor_analysis, scale = TRUE) #2Visualize eigenvalues fviz_eig(res.pca,addlabels=TRUE) fviz_pca_var(res.pca, col.var = "black") # Contributions of variables to PC1 fviz_contrib(res.pca, choice = "var", axes = 1, top = 30) # Contributions of variables to PC2 fviz_contrib(res.pca, choice = "var", axes = 2, top = 30) # Contributions of variables to PC3 fviz_contrib(res.pca, choice = "var", axes = 3, top = 30) # Contributions of variables to PC4 #fviz_contrib(res.pca, choice = "var", axes = 4, top = 10) #graph of individuals;individuals with a similar profile are grouped together. fviz_pca_ind(res.pca, label = "none", # hide individual labels col.ind = "cos2", # Color by the quality of representation gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), repel = TRUE # Avoid text overlapping #addEllipses = TRUE # Concentration ellipses ) clustering analysisdata for clusteringlibrary(dendextend) # for comparing two dendrograms ## cluster data_cluster <- subset(pass_check_data,select = c('factor1_score','factor2_score','factor3_score')) prep: scale/normalization### no need to normalize/scale #data_cluster_z <- base::scale(data_cluster) #data_cluster <- as.data.frame(data_cluster) clustering tendencymethod1 using PCs: hopkins stats;0.5=no cluster; 0=clusterablelibrary(clustertend) # Compute Hopkins statistic for iris dataset hopkins(data_cluster, n = nrow(data_cluster)-1) method2: RSA using PCsfviz_dist(dist(data_cluster), show_labels = FALSE,order=TRUE,gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))+ labs(title = "Dissimilarity matrix across all subs") determine cluster numberinternal to determine optimal cluster methodrequire(clValid) intern <- clValid(data_cluster, nClust = 2:10, maxitems=3000, clMethods = c("hierarchical","kmeans","pam"), validation = "internal")# Summary summary(intern) optimalScores(intern) plot(intern) stability to determine optimal cluster methodstability <- clValid(data_cluster, nClust = 2:10, maxitems=3000, clMethods = c("hierarchical","kmeans","pam"), validation = "stability")# Summary summary(stability) #optimalScores(stability) plot(stability) 30+indexes to determine optimal cluster numrequire(NbClust) res_hc_D2<-NbClust(data_cluster, distance = "euclidean", min.nc=2, max.nc=10, method = "ward.D2", index = "all") fviz_nbclust(res_hc_D2)

## Publication Information

- [xiaoyuzeng_psychbrain](https://paragraph.com/@xiaoyuzeng-psychbrain/): Publication homepage
- [All Posts](https://paragraph.com/@xiaoyuzeng-psychbrain/): More posts from this publication
- [RSS Feed](https://api.paragraph.com/blogs/rss/@xiaoyuzeng-psychbrain): Subscribe to updates