The Methylome in Female Adolescent Conduct Disorder: Neural Pathomechanisms and Environmental Risk Factors
Post-hoc Analyses
AG Chiocchetti
29 Dezember 2020
Last updated: 2021-08-04
Checks: 6 1
Knit directory: femNATCD_MethSeq/
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Sensitivity main hit
For the most significant tag of interest (5’ of the SLITRK5 gene), we tested if the group effect is stable if correcting for Ethnicity (PC1-PC4) or CD associated environmental risk factors.
tophit=which.min(results_Deseq$padj)
methdata=log2_cpm[tophit,]
Probdat=as.data.frame(colData(dds_filt))
Probdat$topHit=methdata[rownames(Probdat)]
model0=as.character(design(dds_filt))[2]
model0=as.formula(gsub("0", "topHit ~ 0 + (1|site)", model0))
lmres=lmer(model0, data=Probdat)boundary (singular) fit: see ?isSingularlmrescoeff = as.data.frame(coefficients(summary(lmres)))
lmrescoeff$pval = dt(lmrescoeff$"t value", df=length(Probdat)-1)
totestpar=c("PC_1", "PC_2", "PC_3", "PC_4", envFact)
ressens=data.frame(matrix(nrow = length(totestpar)+1, ncol=c(3)))
colnames(ressens) = c("beta", "se", "p.value")
rownames(ressens) = c("original", totestpar)
ressens["original",] = lmrescoeff["groupCD", c("Estimate", "Std. Error", "pval")]
for( parm in totestpar){
modelpar=as.character(design(dds_filt))[2]
modelpar=as.formula(gsub("0", paste0("topHit ~ 0 + (1|site) +",parm), modelpar))
lmres=lmer(modelpar, data=Probdat)
lmrescoeff = as.data.frame(coefficients(summary(lmres)))
lmrescoeff$pval = dt(lmrescoeff$"t value", df=length(Probdat)-1)
ressens[parm,] = lmrescoeff["groupCD", c("Estimate", "Std. Error", "pval")]
}boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingulara = barplot(height = ressens$beta,
ylim=rev(range(c(0,ressens$beta-ressens$se)))*1.3,
names.arg = rownames(ressens), col=Set3, border = NA, las=3,
ylab="beta[se]", main="Effect sensitvity analysis")
arrows(a,ressens$beta, a, ressens$beta+ressens$se, angle = 90, length = 0.1)
arrows(a,ressens$beta, a, ressens$beta-ressens$se, angle = 90, length = 0.1)
text(a, min(ressens$beta-ressens$se)*1.15,
formatC(ressens$p.value), cex=0.6, srt=90)
All models are corrected for:
site, Age, Pubstat, int_dis, medication, contraceptives, cigday_1,
site is included as random effect.
original: model defined as 0 + (1 | site) + Age + int_dis + medication + contraceptives + cigday_1 + V8 + group
all other models represent the original model + the variable of interest
Genomic location enrichment
Significant loci with a p-value <= 0.01 and a absolute log2 fold-change lager 0.5 were tested for enrichment in annotated genomic feature using fisher exact test.
Ranges=rowData(dds_filt)
TotTagsofInterest=sum(Ranges$WaldPvalue_groupCD<=thresholdp & abs(Ranges$groupCD)>thresholdLFC)
Resall=data.frame()
index = Ranges$WaldPvalue_groupCD<=thresholdp& abs(Ranges$groupCD)>thresholdLFC
for (feat in unique(Ranges$feature)){
tmp=table(Ranges$feature == feat, signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resall = rbind(Resall, res)
}
tmp=table(Ranges$tf_binding!="", signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resall = rbind(Resall, res)
tmp=table(Ranges$cpg=="cpg", signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resall = rbind(Resall, res)
colnames(Resall)=c("OR", "CI95L", "CI95U", "P")
rownames(Resall)=c(unique(Ranges$feature), "TF-binding", "CpG-island")
Resall$Beta = log(Resall$OR)
Resall$SE = (log(Resall$OR)-log(Resall$CI95L))/1.96
Resall$Padj=p.adjust(Resall$P, method = "bonferroni")
Resdown=data.frame()
index = Ranges$WaldPvalue_groupCD<=thresholdp & Ranges$groupCD<thresholdLFC
for (feat in unique(Ranges$feature)){
tmp=table(Ranges$feature == feat, signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resdown = rbind(Resdown, res)
}
tmp=table(Ranges$tf_binding!="", signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resdown = rbind(Resdown, res)
tmp=table(Ranges$cpg=="cpg", signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resdown = rbind(Resdown, res)
colnames(Resdown)=c("OR", "CI95L", "CI95U", "P")
rownames(Resdown)=c(unique(Ranges$feature), "TF-binding", "CpG-island")
Resdown$Beta = log(Resdown$OR)
Resdown$SE = (log(Resdown$OR)-log(Resdown$CI95L))/1.96
Resdown$Padj=p.adjust(Resdown$P, method = "bonferroni")
Resup=data.frame()
index = Ranges$WaldPvalue_groupCD<=thresholdp & Ranges$groupCD>thresholdLFC
for (feat in unique(Ranges$feature)){
tmp=table(Ranges$feature == feat, signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resup = rbind(Resup, res)
}
tmp=table(Ranges$tf_binding!="", signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resup = rbind(Resup, res)
tmp=table(Ranges$cpg=="cpg", signif=index)
resfish=fisher.test(tmp)
res = c(resfish$estimate, unlist(resfish$conf.int), resfish$p.value)
Resup = rbind(Resup, res)
colnames(Resup)=c("OR", "CI95L", "CI95U", "P")
rownames(Resup)=c(unique(Ranges$feature), "TF-binding", "CpG-island")
Resup$Beta = log(Resup$OR)
Resup$SE = (log(Resup$OR)-log(Resup$CI95L))/1.96
Resup$Padj=p.adjust(Resup$P, method = "bonferroni")
multiORplot(Resall, Pval = "P", Padj = "Padj", beta="Beta",SE = "SE", pheno="All diff. methylated loci")
multiORplot(Resup, Pval = "P", Padj = "Padj", beta="Beta",SE = "SE", pheno="hypomethylated loci")
multiORplot(Resdown, Pval = "P", Padj = "Padj", beta="Beta",SE = "SE", pheno="Hypermethylated loci")
pdf(paste0(Home, "/output/functional_Enrichemnt.pdf"))
multiORplot(Resall, Pval = "P", Padj = "Padj", beta="Beta",SE = "SE", pheno="All diff. methylated loci")
multiORplot(Resup, Pval = "P", Padj = "Padj", beta="Beta",SE = "SE", pheno="hypomethylated loci")
multiORplot(Resdown, Pval = "P", Padj = "Padj", beta="Beta",SE = "SE", pheno="Hypermethylated loci")
dev.off()png
2 GO-term Enrichment
Significant loci and differentially methylated regions with a p-value <= 0.01 and an absolute log2 fold-change lager 0.5 were tested for enrichment among GO-terms Molecular Function, Cellular Compartment and Biological Processes, KEGG pathways, Transcription factor Binding sites, Human Protein Atlas Tissue Expression, Human Phenotypes.
getGOresults = function(geneset, genereference){
resgo = gost(geneset, organism = "hsapiens",
correction_method = "g_SCS",
domain_scope = "custom",
sources = c("GO:BP", "GO:MF", "GO:CC"),
custom_bg = genereference)
if(length(resgo) != 0){
return(resgo)
} else {
print("no significant results")
return(NULL)
}
}
gene_univers = getuniquegenes(as.data.frame(rowRanges(dds_filt))$gene)
idx = (results_Deseq$pvalue <= thresholdp &
(abs(results_Deseq$log2FoldChange) > thresholdLFC))
genes_reg = getuniquegenes(as.data.frame(rowRanges(dds_filt))$gene[idx])
dmr_genes = unique(resultsdmr_table$name[resultsdmr_table$p.value<=thresholdp &
abs(resultsdmr_table$value)>=thresholdLFC])
Genes_of_interset = list("01_dmregions" = dmr_genes,
"02_dmtag" = genes_reg
)
gostres = getGOresults(Genes_of_interset, gene_univers)
gostplot(gostres, capped = TRUE, interactive = T)p = gostplot(gostres, capped = TRUE, interactive = F)
toptab = gostres$result
pp = publish_gostplot(p, filename = paste0(Home,"/output/gostres.pdf"))The image is saved to C:/Users/chiocchetti/Projects/femNATCD_MethSeq/output/gostres.pdfwrite.xlsx2(toptab, file = paste0(Home,"/output/GOres.xlsx"), sheetName = "GO_enrichment")Kang Brain Module Enrichments
Gene sets identified to be deferentially methylated with a p-value <= 0.01 and an absolute log2 fold-change larger 0.5 were tested for enrichment among gene-modules coregulated during Brain expression.
# define Reference Universe
KangUnivers<- read.table(paste0(Home,"/data/KangUnivers.txt"), sep="\t", header=T)
colnames(KangUnivers)<-c("EntrezId","Symbol")
Kang_genes<-read.table(paste0(Home,"/data/Kang_dataset_genesMod_version2.txt"),sep="\t",header=TRUE)
#3)Generate Gene universe to be used for single gene lists
tmp=merge(KangUnivers,Kang_genes,by.y="EntrezGene",by.x="EntrezId",all=TRUE) #18826
KangUni_Final<-tmp[duplicated(tmp$EntrezId)==FALSE,] #18675
# Local analysis gene universe
Annotation_list<-data.frame(Symbol = gene_univers)
# match modules
Annotation_list$Module = Kang_genes$Module[match(Annotation_list$Symbol,Kang_genes$symbol)]
# check if overlapping in gene universes
Annotation_list$univers = Annotation_list$Symbol %in% KangUni_Final$Symbol
# drop duplicates
Annotation_list = Annotation_list[duplicated(Annotation_list$Symbol)==FALSE,]
# selct only genes that have been detected on both datasets
Annotation_list = Annotation_list[Annotation_list$univers==T,]
# final reference
UniversalGeneset=Annotation_list$Symbol
# define Gene lists to test
# sort and order Modules to be tested
Modules=unique(Annotation_list$Module)
Modules = Modules[! Modules %in% c(NA, "")]
Modules = Modules[order(as.numeric(gsub("M","",Modules)))]
GL_all=list()
for(i in Modules){
GL_all[[i]]=Annotation_list$Symbol[Annotation_list$Module%in%i]
}
GL_all[["M_all"]]=Kang_genes$symbol[Kang_genes$Module %in% Modules]
GOI1 = Genes_of_interset
Resultsall=list()
for(j in names(GOI1)){
Res = data.frame()
for(i in names(GL_all)){
Modulegene=GL_all[[i]]
Factorgene=GOI1[[j]]
Testframe<-fisher.test(table(factor(UniversalGeneset %in% Factorgene,levels=c("TRUE","FALSE")),
factor(UniversalGeneset %in% Modulegene,levels=c("TRUE","FALSE"))))
beta=log(Testframe$estimate)
Res[i, "beta"] =beta
Res[i, "SE"]=abs(beta-log(Testframe$conf.int[1]))/1.96
Res[i, "Pval"]=Testframe$p.value
Res[i, "OR"]=(Testframe$estimate)
Res[i, "ORL"]=(Testframe$conf.int[1])
Res[i, "ORU"]=(Testframe$conf.int[2])
}
Res$Padj = p.adjust(Res$Pval, method = "bonferroni")
Resultsall[[j]] = Res
}
par(mfrow = c(2,1))
for (i in names(Resultsall)){
multiORplot(datatoplot = Resultsall[[i]], pheno=i)
}
par(mfrow = c(1,1))
pdf(paste0(Home, "/output/BrainMod_Enrichemnt.pdf"))
for (i in names(Resultsall)){
multiORplot(datatoplot = Resultsall[[i]], pheno=i)
}
dev.off()png
2 Modsig = c()
for(r in names(Resultsall)){
a=rownames(Resultsall[[r]])[Resultsall[[r]]$Padj<=0.05]
Modsig = c(Modsig,a)
}# show brains and expression
Modsig2=unique(Modsig[Modsig!="M_all"])
load(paste0(Home,"/data/Kang_DataPreprocessing.RData")) #Load the Kang expression data of all genes
datExprPlot=matriz #Expression data of Kang loaded as Rdata object DataPreprocessing.RData
Genes = GL_all[names(GL_all)!="M_all"]
Genes_expression<-list()
pcatest<-list()
for (i in names(Genes)){
Genes_expression[[i]]<-matriz[,which(colnames(matriz) %in% Genes[[i]])]
pcatest[[i]]=prcomp(t(as.matrix(Genes_expression[[i]])),retx=TRUE)
}
# PCA test
PCA<-data.frame(pcatest[[1]]$rotation)
PCA$donor_name<-rownames(PCA)
PC1<-data.frame(PCA[,c(1,ncol(PCA))])
#Combining the age with expression data
list <- strsplit(sampleInfo$age, " ")
library("plyr")------------------------------------------------------------------------------You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)------------------------------------------------------------------------------
Attaching package: 'plyr'The following object is masked from 'package:matrixStats':
countThe following object is masked from 'package:IRanges':
descThe following object is masked from 'package:S4Vectors':
renameThe following objects are masked from 'package:dplyr':
arrange, count, desc, failwith, id, mutate, rename, summarise,
summarizeThe following object is masked from 'package:purrr':
compactdf <- ldply(list)
colnames(df) <- c("Age", "time")
sampleInfo<-cbind(sampleInfo[,1:9],df)
sampleInfo$Age<-as.numeric(sampleInfo$Age)
sampleInfo$period<-ifelse(sampleInfo$time=="pcw",sampleInfo$Age*7,ifelse(sampleInfo$time=="yrs",sampleInfo$Age*365+270,ifelse(sampleInfo$time=="mos",sampleInfo$Age*30+270,NA)))
#We need it just for the donor names
PCA_matrix<-merge.with.order(PC1,sampleInfo,by.y="SampleID",by.x="donor_name",keep_order=1)
#Select which have phenotype info present
matriz2<-matriz[which(rownames(matriz) %in% PCA_matrix$donor_name),]
FactorGenes_expression<-list()
#Factors here mean modules
for (i in names(Genes)){
FactorGenes_expression[[i]]<-matriz2[,which(colnames(matriz2) %in% Genes[[i]])]
}
FactorseGE<-list()
for (i in names(Genes)){
FactorseGE[[i]]<-FactorGenes_expression[[i]]
}
allModgenes=NULL
colors=vector()
for ( i in names(Genes)){
allModgenes=cbind(allModgenes,FactorseGE[[i]])
colors=c(colors, rep(i, ncol(FactorseGE[[i]])))
}
lengths=unlist(lapply(FactorGenes_expression, ncol), use.names = F)
MEorig=moduleEigengenes(allModgenes, colors)
PCA_matrixfreeze=PCA_matrix
index=!PCA_matrix$structure_acronym %in% c("URL", "DTH", "CGE","LGE", "MGE", "Ocx", "PCx", "M1C-S1C","DIE", "TCx", "CB")
PCA_matrix=PCA_matrix[index,]
ME = MEorig$eigengenes[index,]
matsel = matriz2[index,]
colnames(ME) = gsub("ME", "", colnames(ME))
timepoints=seq(56,15000, length.out=1000)
matrix(c("CB", "THA", "CBC", "MD"), ncol=2 ) -> cnm
brainheatmap=function(Module){
MEmod=ME[,Module]
toplot=data.frame(matrix(NA, nrow=length(table(PCA_matrix$structure_acronym)), ncol=998))
rownames(toplot)=unique(PCA_matrix$structure_acronym)
target <- c("OFC", "DFC", "VFC", "MFC","M1C","S1C","IPC","A1C","STC","ITC","V1C","HIP","AMY","STR","MD","CBC")
toplot<-toplot[c(6,2,8,5,11,12,10,9,7,4,14,3,1,13,16,15),]
for ( i in unique(PCA_matrix$structure_acronym)){
index=PCA_matrix$structure_acronym==i
LOESS=loess(MEmod[index]~PCA_matrix$period[index])
toplot[i,]=predict(LOESS,newdata = round(exp(seq(log(56),log(15000), length.out=998)),2))
colnames(toplot)[c(1,77,282,392,640,803,996)]<-
c("1pcw","21pcw","Birth","1.3years","5.4years","13.6years","40.7years")
}
cols=viridis(100)
labvec <- c(rep(NA, 1000))
labvec[c(1,77,282,392,640,803,996)] <- c("1pcw","21pcw","Birth","1.3years","5.4years","13.6years","40.7years")
toplot<-toplot[,1:998]
date<-c(1:998)
dateY<-paste0(round(date/365,2),"_Years")
names(toplot)<-dateY
par(xpd=FALSE)
heatmap.2(as.matrix(toplot), col = cols,
main=Module,
trace = "none",
na.color = "grey",
Colv = F, Rowv = F,
labCol = labvec,
#breaks = seq(-0.1,0.1, length.out=101),
symkey = T,
scale = "row",
key.title = "",
dendrogram = "none",
key.xlab = "eigengene",
density.info = "none",
#main=paste("Module",1),
srtCol=90,
tracecol = "none",
cexRow = 1,
add.expr=eval.parent(abline(v=282),
axis(1,at=c(1,77,282,392,640,803,996),
labels =FALSE)),cexCol = 1)
}
brainheatmap_gene=function(Genename){
MEmod=matsel[,Genename]
toplot=data.frame(matrix(NA, nrow=length(table(PCA_matrix$structure_acronym)), ncol=998))
rownames(toplot)=unique(PCA_matrix$structure_acronym)
target <- c("OFC", "DFC", "VFC", "MFC","M1C","S1C","IPC","A1C","STC","ITC","V1C","HIP","AMY","STR","MD","CBC")
toplot<-toplot[c(6,2,8,5,11,12,10,9,7,4,14,3,1,13,16,15),]
for ( i in unique(PCA_matrix$structure_acronym)){
index=PCA_matrix$structure_acronym==i
LOESS=loess(MEmod[index]~PCA_matrix$period[index])
toplot[i,]=predict(LOESS,newdata = round(exp(seq(log(56),log(15000), length.out=998)),2))
colnames(toplot)[c(1,77,282,392,640,803,996)]<-
c("1pcw","21pcw","Birth","1.3years","5.4years","13.6years","40.7years")
}
cols=viridis(100)
labvec <- c(rep(NA, 1000))
labvec[c(1,77,282,392,640,803,996)] <- c("1pcw","21pcw","Birth","1.3years","5.4years","13.6years","40.7years")
toplot<-toplot[,1:998]
date<-c(1:998)
dateY<-paste0(round(date/365,2),"_Years")
names(toplot)<-dateY
par(xpd=FALSE)
heatmap.2(as.matrix(toplot), col = cols,
main=Genename,
trace = "none",
na.color = "grey",
Colv = F, Rowv = F,
labCol = labvec,
#breaks = seq(-0.1,0.1, length.out=101),
symkey = F,
scale = "none",
key.title = "",
dendrogram = "none",
key.xlab = "eigengene",
density.info = "none",
#main=paste("Module",1),
#srtCol=90,
tracecol = "none",
cexRow = 1,
add.expr=eval.parent(abline(v=282),
axis(1,at=c(1,77,282,392,640,803,996),
labels =FALSE))
,cexCol = 1)
}
brainheatmap_gene("SLITRK5")
for(Module in Modsig2){
brainheatmap(Module)
}
pdf(paste0(Home, "/output/Brain_Module_Heatmap.pdf"))
brainheatmap_gene("SLITRK5")
for(Module in Modsig2){
brainheatmap(Module)
}
dev.off()png
2 SEM
dropfact=c("site", "0", "group")
modelFact=strsplit(as.character(design(dds_filt))[2], " \\+ ")[[1]]
Patdata=as.data.frame(colData(dds_filt))
load(paste0(Home, "/output/envFact.RData"))
envFact=envFact[!envFact %in% dropfact]
modelFact=modelFact[!modelFact %in% dropfact]
EpiMarker = c()
# TopHit
Patdata$Epi_TopHit=log2_cpm[base::which.min(results_Deseq$pvalue),]
# 1PC of all diff met
tmp=glmpca(log2_cpm[base::which(results_Deseq$pvalue<=thresholdp),], 1)
Patdata$Epi_all= tmp$factors$dim1
EpiMarker = c(EpiMarker, "Epi_TopHit", "Epi_all")
#Brain Modules
Epitestset=GL_all[Modsig]
for(n in names(Epitestset)){
index=gettaglistforgenelist(genelist = Epitestset[[n]], dds_filt)
index = base::intersect(index, base::which(results_Deseq$pvalue<=thresholdp))
# get eigenvalue
epiname=paste0("Epi_",n)
tmp=glmpca(log2_cpm[index,], 1)
Patdata[,epiname]= tmp$factors$dim1
EpiMarker = c(EpiMarker, epiname)
}
cormat = cor(apply(Patdata[,c("group", envFact, modelFact, EpiMarker)] %>% mutate_all(as.numeric), 2, minmax_scaling),
use = "pairwise.complete.obs")
par(mfrow=c(1,2))
corrplot(cormat, main="correlations")
corrplot(cormat, order = "hclust", main="scaled correlations")
fullmodEnv=paste(unique(envFact,modelFact), sep = "+", collapse = "+")
Dataset = Patdata[,c("group", envFact,modelFact,EpiMarker)]
model = "
Epi~1+a*Matsmk+b*Matagg+c*FamScore+d*EduPar+e*n_trauma+Age+int_dis+medication+contraceptives+cigday_1+V8
group~1+f*Matsmk+g*Matagg+h*FamScore+i*EduPar+j*n_trauma+Age+int_dis+medication+contraceptives+cigday_1+V8+z*Epi
#direct
directMatsmk := f
directMatagg := g
directFamScore := h
directEduPar := i
directn_trauma := j
#indirect
EpiMatsmk := a*z
EpiMatagg := b*z
EpiFamScore := c*z
EpiEduPar := d*z
Epin_trauma := e*z
total := f + g + h + i + j + (a*z)+(b*z)+(c*z)+(d*z)+(e*z)
Epi~~Epi
group~~group
"
Netlist = list()
for (marker in EpiMarker) {
Dataset$Epi = Dataset[,marker]
Datasetscaled = Dataset %>% mutate_if(is.numeric, minmax_scaling)
Datasetscaled = Datasetscaled %>% mutate_if(is.factor, ~ as.numeric(.)-1)
fit<-lavaan(model,data=Datasetscaled)
sink(paste0(Home,"/output/SEM_summary_group",marker,".txt"))
summary(fit)
print(fitMeasures(fit))
print(parameterEstimates(fit))
sink()
cat("############################\n")
cat("############################\n")
cat(marker, "\n")
cat("############################\n")
cat("############################\n")
summary(fit)
cat("\n")
print(fitMeasures(fit))
cat("\n")
print(parameterEstimates(fit))
cat("\n")
#SOURCE FOR PLOT https://stackoverflow.com/questions/51270032/how-can-i-display-only-significant-path-lines-on-a-path-diagram-r-lavaan-sem
restab=lavaan::standardizedSolution(fit) %>% dplyr::filter(!is.na(pvalue)) %>%
arrange(desc(pvalue)) %>% mutate_if("is.numeric","round",3) %>%
dplyr::select(-ci.lower,-ci.upper,-z)
pvalue_cutoff <- 0.05
obj <- semPlot:::semPlotModel(fit)
original_Pars <- obj@Pars
check_Pars <- obj@Pars %>% dplyr:::filter(!(edge %in% c("int","<->") | lhs == rhs)) # this is the list of parameter to sift thru
keep_Pars <- obj@Pars %>% dplyr:::filter(edge %in% c("int","<->") | lhs == rhs) # this is the list of parameter to keep asis
test_against <- lavaan::standardizedSolution(fit) %>% dplyr::filter(pvalue < pvalue_cutoff, rhs != lhs)
# for some reason, the rhs and lhs are reversed in the standardizedSolution() output, for some of the values
# I'll have to reverse it myself, and test against both orders
test_against_rev <- test_against %>% dplyr::rename(rhs2 = lhs, lhs = rhs) %>% dplyr::rename(rhs = rhs2)
checked_Pars <-
check_Pars %>% semi_join(test_against, by = c("lhs", "rhs")) %>% bind_rows(
check_Pars %>% semi_join(test_against_rev, by = c("lhs", "rhs"))
)
obj@Pars <- keep_Pars %>% bind_rows(checked_Pars) %>%
mutate_if("is.numeric","round",3) %>%
mutate_at(c("lhs","rhs"),~gsub("Epi", marker,.))
obj@Vars = obj@Vars %>% mutate_at(c("name"),~gsub("Epi", marker,.))
DF = obj@Pars
DF = DF[DF$lhs!=DF$rhs,]
DF = DF[abs(DF$std)>0.1,]
DF = DF[DF$edge == "~>",] # only include directly modelled effects in figure
nodes <- data.frame(id=obj@Vars$name, label = obj@Vars$name)
nodes$color<-Dark8[8]
nodes$color[nodes$label == "group"] = Dark8[3]
nodes$color[nodes$label == marker] = Dark8[4]
nodes$color[nodes$label %in% envFact] = Dark8[5]
edges <- data.frame(from = DF$lhs,
to = DF$rhs,
width=abs(DF$std),
arrows ="to")
edges$dashes = F
edges$label = DF$std
edges$color=c("firebrick", "forestgreen")[1:2][factor(sign(DF$std), levels=c(-1,0,1),labels=c(1,2,2))]
edges$width=2
cexlab = 18
plotnet<- visNetwork(nodes, edges,
main=list(text=marker,
style="font-family:arial;font-size:20px;text-align:center"),
submain=list(text="significant paths",
style="font-family:arial;text-align:center")) %>%
visEdges(arrows =list(to = list(enabled = TRUE, scaleFactor = 0.7)),
font=list(size=cexlab, style="font-family:arial;text-align:center")) %>%
visNodes(font=list(size=cexlab, style="font-family:arial;text-align:center")) %>%
visPhysics(enabled = T, solver = "forceAtlas2Based")
Netlist[[marker]] = plotnet
htmlfile = paste0(Home,"/output/SEMplot_",marker,".html")
visSave(plotnet, htmlfile)
webshot(paste0(Home,"/output/SEMplot_",marker,".html"), zoom = 1,
file = paste0(Home,"/output/SEMplot_",marker,".png"))
}############################
############################
Epi_TopHit
############################
############################
lavaan 0.6-7 ended normally after 45 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 27
Used Total
Number of observations 80 99
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) -0.033 0.045 -0.731 0.465
Matagg (b) -0.014 0.068 -0.213 0.831
FamScore (c) 0.056 0.064 0.887 0.375
EduPar (d) -0.038 0.082 -0.468 0.640
n_trauma (e) 0.085 0.088 0.964 0.335
Age -0.090 0.087 -1.036 0.300
int_dis -0.074 0.047 -1.580 0.114
medication -0.044 0.051 -0.876 0.381
contrcptvs -0.017 0.046 -0.361 0.718
cigday_1 -0.111 0.090 -1.228 0.219
V8 -0.089 0.262 -0.339 0.735
group ~
Matsmk (f) -0.002 0.080 -0.031 0.976
Matagg (g) 0.198 0.122 1.623 0.105
FamScore (h) 0.020 0.114 0.178 0.858
EduPar (i) -0.473 0.147 -3.213 0.001
n_trauma (j) 0.289 0.159 1.824 0.068
Age -0.346 0.157 -2.205 0.027
int_dis 0.170 0.086 1.977 0.048
medication 0.276 0.091 3.026 0.002
contrcptvs -0.023 0.083 -0.271 0.787
cigday_1 0.662 0.163 4.052 0.000
V8 0.319 0.470 0.679 0.497
Epi (z) -1.004 0.200 -5.015 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.843 0.159 5.320 0.000
.group 1.166 0.330 3.528 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.023 0.004 6.325 0.000
.group 0.075 0.012 6.325 0.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk -0.002 0.080 -0.031 0.976
directMatagg 0.198 0.122 1.623 0.105
directFamScore 0.020 0.114 0.178 0.858
directEduPar -0.473 0.147 -3.213 0.001
directn_trauma 0.289 0.159 1.824 0.068
EpiMatsmk 0.033 0.045 0.723 0.470
EpiMatagg 0.015 0.068 0.213 0.831
EpiFamScore -0.057 0.065 -0.874 0.382
EpiEduPar 0.039 0.083 0.466 0.641
Epin_trauma -0.085 0.090 -0.947 0.344
total -0.025 0.318 -0.077 0.938
npar fmin chisq df
27.000 0.000 0.000 0.000
pvalue baseline.chisq baseline.df baseline.pvalue
NA 106.412 23.000 0.000
cfi tli nnfi rfi
1.000 1.000 1.000 1.000
nfi pnfi ifi rni
1.000 0.000 1.000 1.000
logl unrestricted.logl aic bic
26.929 26.929 0.143 64.457
ntotal bic2 rmsea rmsea.ci.lower
80.000 -20.683 0.000 0.000
rmsea.ci.upper rmsea.pvalue rmr rmr_nomean
0.000 NA 0.000 0.000
srmr srmr_bentler srmr_bentler_nomean crmr
0.000 0.000 0.000 0.000
crmr_nomean srmr_mplus srmr_mplus_nomean cn_05
0.000 0.000 0.000 NA
cn_01 gfi agfi pgfi
NA 1.000 1.000 0.000
mfi ecvi
1.000 0.675
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~1
14 group ~ Matsmk f
15 group ~ Matagg g
16 group ~ FamScore h
17 group ~ EduPar i
18 group ~ n_trauma j
19 group ~ Age
20 group ~ int_dis
21 group ~ medication
22 group ~ contraceptives
23 group ~ cigday_1
24 group ~ V8
25 group ~ Epi z
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 Matsmk ~1
95 Matagg ~1
96 FamScore ~1
97 EduPar ~1
98 n_trauma ~1
99 Age ~1
100 int_dis ~1
101 medication ~1
102 contraceptives ~1
103 cigday_1 ~1
104 V8 ~1
105 directMatsmk := f directMatsmk
106 directMatagg := g directMatagg
107 directFamScore := h directFamScore
108 directEduPar := i directEduPar
109 directn_trauma := j directn_trauma
110 EpiMatsmk := a*z EpiMatsmk
111 EpiMatagg := b*z EpiMatagg
112 EpiFamScore := c*z EpiFamScore
113 EpiEduPar := d*z EpiEduPar
114 Epin_trauma := e*z Epin_trauma
115 total := f+g+h+i+j+(a*z)+(b*z)+(c*z)+(d*z)+(e*z) total
est se z pvalue ci.lower ci.upper
1 0.843 0.159 5.320 0.000 0.533 1.154
2 -0.033 0.045 -0.731 0.465 -0.120 0.055
3 -0.014 0.068 -0.213 0.831 -0.148 0.119
4 0.056 0.064 0.887 0.375 -0.068 0.181
5 -0.038 0.082 -0.468 0.640 -0.199 0.123
6 0.085 0.088 0.964 0.335 -0.088 0.257
7 -0.090 0.087 -1.036 0.300 -0.261 0.080
8 -0.074 0.047 -1.580 0.114 -0.167 0.018
9 -0.044 0.051 -0.876 0.381 -0.144 0.055
10 -0.017 0.046 -0.361 0.718 -0.108 0.074
11 -0.111 0.090 -1.228 0.219 -0.288 0.066
12 -0.089 0.262 -0.339 0.735 -0.603 0.425
13 1.166 0.330 3.528 0.000 0.518 1.814
14 -0.002 0.080 -0.031 0.976 -0.159 0.155
15 0.198 0.122 1.623 0.105 -0.041 0.436
16 0.020 0.114 0.178 0.858 -0.204 0.245
17 -0.473 0.147 -3.213 0.001 -0.762 -0.185
18 0.289 0.159 1.824 0.068 -0.021 0.600
19 -0.346 0.157 -2.205 0.027 -0.653 -0.039
20 0.170 0.086 1.977 0.048 0.001 0.338
21 0.276 0.091 3.026 0.002 0.097 0.455
22 -0.023 0.083 -0.271 0.787 -0.186 0.141
23 0.662 0.163 4.052 0.000 0.342 0.982
24 0.319 0.470 0.679 0.497 -0.602 1.240
25 -1.004 0.200 -5.015 0.000 -1.397 -0.612
26 0.023 0.004 6.325 0.000 0.016 0.031
27 0.075 0.012 6.325 0.000 0.052 0.098
28 0.194 0.000 NA NA 0.194 0.194
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.005 0.000 NA NA -0.005 -0.005
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.021 0.000 NA NA 0.021 0.021
35 0.004 0.000 NA NA 0.004 0.004
36 0.021 0.000 NA NA 0.021 0.021
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.090 0.000 NA NA 0.090 0.090
40 0.034 0.000 NA NA 0.034 0.034
41 -0.016 0.000 NA NA -0.016 -0.016
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.032 0.000 NA NA 0.032 0.032
45 0.007 0.000 NA NA 0.007 0.007
46 0.007 0.000 NA NA 0.007 0.007
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.131 0.000 NA NA 0.131 0.131
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.026 0.000 NA NA 0.026 0.026
52 0.004 0.000 NA NA 0.004 0.004
53 0.064 0.000 NA NA 0.064 0.064
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.043 0.000 NA NA 0.043 0.043
57 0.001 0.000 NA NA 0.001 0.001
58 0.053 0.000 NA NA 0.053 0.053
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.041 0.000 NA NA 0.041 0.041
69 0.017 0.000 NA NA 0.017 0.017
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.008 0.000 NA NA 0.008 0.008
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.210 0.000 NA NA 0.210 0.210
80 0.060 0.000 NA NA 0.060 0.060
81 0.060 0.000 NA NA 0.060 0.060
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.144 0.000 NA NA 0.144 0.144
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.210 0.000 NA NA 0.210 0.210
89 0.048 0.000 NA NA 0.048 0.048
90 0.003 0.000 NA NA 0.003 0.003
91 0.061 0.000 NA NA 0.061 0.061
92 0.001 0.000 NA NA 0.001 0.001
93 0.005 0.000 NA NA 0.005 0.005
94 0.262 0.000 NA NA 0.262 0.262
95 0.100 0.000 NA NA 0.100 0.100
96 0.225 0.000 NA NA 0.225 0.225
97 0.606 0.000 NA NA 0.606 0.606
98 0.196 0.000 NA NA 0.196 0.196
99 0.562 0.000 NA NA 0.562 0.562
100 0.300 0.000 NA NA 0.300 0.300
101 0.175 0.000 NA NA 0.175 0.175
102 0.300 0.000 NA NA 0.300 0.300
103 0.124 0.000 NA NA 0.124 0.124
104 0.529 0.000 NA NA 0.529 0.529
105 -0.002 0.080 -0.031 0.976 -0.159 0.155
106 0.198 0.122 1.623 0.105 -0.041 0.436
107 0.020 0.114 0.178 0.858 -0.204 0.245
108 -0.473 0.147 -3.213 0.001 -0.762 -0.185
109 0.289 0.159 1.824 0.068 -0.021 0.600
110 0.033 0.045 0.723 0.470 -0.056 0.121
111 0.015 0.068 0.213 0.831 -0.119 0.148
112 -0.057 0.065 -0.874 0.382 -0.184 0.070
113 0.039 0.083 0.466 0.641 -0.124 0.201
114 -0.085 0.090 -0.947 0.344 -0.262 0.091
115 -0.025 0.318 -0.077 0.938 -0.648 0.599
############################
############################
Epi_all
############################
############################
lavaan 0.6-7 ended normally after 51 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 27
Used Total
Number of observations 80 99
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) 0.014 0.023 0.614 0.539
Matagg (b) 0.023 0.036 0.635 0.525
FamScore (c) -0.037 0.033 -1.093 0.274
EduPar (d) -0.090 0.043 -2.096 0.036
n_trauma (e) 0.025 0.046 0.542 0.588
Age 0.034 0.046 0.747 0.455
int_dis 0.023 0.025 0.923 0.356
medication 0.007 0.027 0.276 0.783
contrcptvs -0.016 0.024 -0.646 0.518
cigday_1 0.022 0.047 0.463 0.643
V8 0.060 0.138 0.437 0.662
group ~
Matsmk (f) -0.019 0.044 -0.430 0.667
Matagg (g) 0.134 0.067 1.998 0.046
FamScore (h) 0.089 0.063 1.404 0.160
EduPar (i) -0.125 0.083 -1.499 0.134
n_trauma (j) 0.118 0.087 1.356 0.175
Age -0.372 0.086 -4.318 0.000
int_dis 0.166 0.047 3.546 0.000
medication 0.296 0.050 5.896 0.000
contrcptvs 0.048 0.046 1.050 0.294
cigday_1 0.698 0.089 7.809 0.000
V8 0.202 0.259 0.779 0.436
Epi (z) 3.424 0.210 16.296 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.134 0.083 1.608 0.108
.group -0.140 0.159 -0.881 0.378
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.006 0.001 6.325 0.000
.group 0.023 0.004 6.325 0.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk -0.019 0.044 -0.430 0.667
directMatagg 0.134 0.067 1.998 0.046
directFamScore 0.089 0.063 1.404 0.160
directEduPar -0.125 0.083 -1.499 0.134
directn_trauma 0.118 0.087 1.356 0.175
EpiMatsmk 0.049 0.080 0.614 0.540
EpiMatagg 0.078 0.122 0.635 0.526
EpiFamScore -0.125 0.115 -1.091 0.275
EpiEduPar -0.310 0.149 -2.079 0.038
Epin_trauma 0.086 0.159 0.542 0.588
total -0.025 0.318 -0.077 0.938
npar fmin chisq df
27.000 0.000 0.000 0.000
pvalue baseline.chisq baseline.df baseline.pvalue
NA 199.783 23.000 0.000
cfi tli nnfi rfi
1.000 1.000 1.000 1.000
nfi pnfi ifi rni
1.000 0.000 1.000 1.000
logl unrestricted.logl aic bic
125.945 125.945 -197.890 -133.575
ntotal bic2 rmsea rmsea.ci.lower
80.000 -218.716 0.000 0.000
rmsea.ci.upper rmsea.pvalue rmr rmr_nomean
0.000 NA 0.000 0.000
srmr srmr_bentler srmr_bentler_nomean crmr
0.000 0.000 0.000 0.000
crmr_nomean srmr_mplus srmr_mplus_nomean cn_05
0.000 0.000 0.000 NA
cn_01 gfi agfi pgfi
NA 1.000 1.000 0.000
mfi ecvi
1.000 0.675
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~1
14 group ~ Matsmk f
15 group ~ Matagg g
16 group ~ FamScore h
17 group ~ EduPar i
18 group ~ n_trauma j
19 group ~ Age
20 group ~ int_dis
21 group ~ medication
22 group ~ contraceptives
23 group ~ cigday_1
24 group ~ V8
25 group ~ Epi z
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 Matsmk ~1
95 Matagg ~1
96 FamScore ~1
97 EduPar ~1
98 n_trauma ~1
99 Age ~1
100 int_dis ~1
101 medication ~1
102 contraceptives ~1
103 cigday_1 ~1
104 V8 ~1
105 directMatsmk := f directMatsmk
106 directMatagg := g directMatagg
107 directFamScore := h directFamScore
108 directEduPar := i directEduPar
109 directn_trauma := j directn_trauma
110 EpiMatsmk := a*z EpiMatsmk
111 EpiMatagg := b*z EpiMatagg
112 EpiFamScore := c*z EpiFamScore
113 EpiEduPar := d*z EpiEduPar
114 Epin_trauma := e*z Epin_trauma
115 total := f+g+h+i+j+(a*z)+(b*z)+(c*z)+(d*z)+(e*z) total
est se z pvalue ci.lower ci.upper
1 0.134 0.083 1.608 0.108 -0.029 0.297
2 0.014 0.023 0.614 0.539 -0.032 0.060
3 0.023 0.036 0.635 0.525 -0.047 0.093
4 -0.037 0.033 -1.093 0.274 -0.102 0.029
5 -0.090 0.043 -2.096 0.036 -0.175 -0.006
6 0.025 0.046 0.542 0.588 -0.066 0.116
7 0.034 0.046 0.747 0.455 -0.055 0.124
8 0.023 0.025 0.923 0.356 -0.026 0.071
9 0.007 0.027 0.276 0.783 -0.045 0.060
10 -0.016 0.024 -0.646 0.518 -0.064 0.032
11 0.022 0.047 0.463 0.643 -0.071 0.115
12 0.060 0.138 0.437 0.662 -0.210 0.330
13 -0.140 0.159 -0.881 0.378 -0.452 0.172
14 -0.019 0.044 -0.430 0.667 -0.106 0.068
15 0.134 0.067 1.998 0.046 0.003 0.266
16 0.089 0.063 1.404 0.160 -0.035 0.213
17 -0.125 0.083 -1.499 0.134 -0.288 0.038
18 0.118 0.087 1.356 0.175 -0.053 0.289
19 -0.372 0.086 -4.318 0.000 -0.542 -0.203
20 0.166 0.047 3.546 0.000 0.074 0.258
21 0.296 0.050 5.896 0.000 0.197 0.394
22 0.048 0.046 1.050 0.294 -0.042 0.139
23 0.698 0.089 7.809 0.000 0.523 0.873
24 0.202 0.259 0.779 0.436 -0.306 0.710
25 3.424 0.210 16.296 0.000 3.012 3.836
26 0.006 0.001 6.325 0.000 0.004 0.008
27 0.023 0.004 6.325 0.000 0.016 0.030
28 0.194 0.000 NA NA 0.194 0.194
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.005 0.000 NA NA -0.005 -0.005
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.021 0.000 NA NA 0.021 0.021
35 0.004 0.000 NA NA 0.004 0.004
36 0.021 0.000 NA NA 0.021 0.021
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.090 0.000 NA NA 0.090 0.090
40 0.034 0.000 NA NA 0.034 0.034
41 -0.016 0.000 NA NA -0.016 -0.016
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.032 0.000 NA NA 0.032 0.032
45 0.007 0.000 NA NA 0.007 0.007
46 0.007 0.000 NA NA 0.007 0.007
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.131 0.000 NA NA 0.131 0.131
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.026 0.000 NA NA 0.026 0.026
52 0.004 0.000 NA NA 0.004 0.004
53 0.064 0.000 NA NA 0.064 0.064
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.043 0.000 NA NA 0.043 0.043
57 0.001 0.000 NA NA 0.001 0.001
58 0.053 0.000 NA NA 0.053 0.053
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.041 0.000 NA NA 0.041 0.041
69 0.017 0.000 NA NA 0.017 0.017
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.008 0.000 NA NA 0.008 0.008
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.210 0.000 NA NA 0.210 0.210
80 0.060 0.000 NA NA 0.060 0.060
81 0.060 0.000 NA NA 0.060 0.060
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.144 0.000 NA NA 0.144 0.144
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.210 0.000 NA NA 0.210 0.210
89 0.048 0.000 NA NA 0.048 0.048
90 0.003 0.000 NA NA 0.003 0.003
91 0.061 0.000 NA NA 0.061 0.061
92 0.001 0.000 NA NA 0.001 0.001
93 0.005 0.000 NA NA 0.005 0.005
94 0.262 0.000 NA NA 0.262 0.262
95 0.100 0.000 NA NA 0.100 0.100
96 0.225 0.000 NA NA 0.225 0.225
97 0.606 0.000 NA NA 0.606 0.606
98 0.196 0.000 NA NA 0.196 0.196
99 0.562 0.000 NA NA 0.562 0.562
100 0.300 0.000 NA NA 0.300 0.300
101 0.175 0.000 NA NA 0.175 0.175
102 0.300 0.000 NA NA 0.300 0.300
103 0.124 0.000 NA NA 0.124 0.124
104 0.529 0.000 NA NA 0.529 0.529
105 -0.019 0.044 -0.430 0.667 -0.106 0.068
106 0.134 0.067 1.998 0.046 0.003 0.266
107 0.089 0.063 1.404 0.160 -0.035 0.213
108 -0.125 0.083 -1.499 0.134 -0.288 0.038
109 0.118 0.087 1.356 0.175 -0.053 0.289
110 0.049 0.080 0.614 0.540 -0.108 0.207
111 0.078 0.122 0.635 0.526 -0.162 0.317
112 -0.125 0.115 -1.091 0.275 -0.350 0.100
113 -0.310 0.149 -2.079 0.038 -0.602 -0.018
114 0.086 0.159 0.542 0.588 -0.225 0.397
115 -0.025 0.318 -0.077 0.938 -0.648 0.599
############################
############################
Epi_M2
############################
############################
lavaan 0.6-7 ended normally after 44 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 27
Used Total
Number of observations 80 99
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) 0.009 0.033 0.272 0.786
Matagg (b) -0.017 0.051 -0.328 0.743
FamScore (c) -0.054 0.047 -1.133 0.257
EduPar (d) -0.010 0.061 -0.164 0.870
n_trauma (e) 0.018 0.066 0.277 0.782
Age -0.042 0.065 -0.646 0.518
int_dis -0.010 0.035 -0.274 0.784
medication 0.018 0.038 0.473 0.636
contrcptvs 0.074 0.035 2.145 0.032
cigday_1 -0.058 0.067 -0.866 0.386
V8 -0.522 0.195 -2.674 0.007
group ~
Matsmk (f) 0.041 0.083 0.496 0.620
Matagg (g) 0.192 0.126 1.527 0.127
FamScore (h) -0.100 0.119 -0.841 0.400
EduPar (i) -0.447 0.152 -2.933 0.003
n_trauma (j) 0.226 0.163 1.382 0.167
Age -0.305 0.162 -1.887 0.059
int_dis 0.233 0.087 2.664 0.008
medication 0.342 0.094 3.632 0.000
contrcptvs 0.082 0.089 0.929 0.353
cigday_1 0.704 0.168 4.185 0.000
V8 -0.211 0.507 -0.416 0.678
Epi (z) -1.187 0.278 -4.262 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.886 0.118 7.504 0.000
.group 1.370 0.384 3.570 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.013 0.002 6.325 0.000
.group 0.080 0.013 6.325 0.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk 0.041 0.083 0.496 0.620
directMatagg 0.192 0.126 1.527 0.127
directFamScore -0.100 0.119 -0.841 0.400
directEduPar -0.447 0.152 -2.933 0.003
directn_trauma 0.226 0.163 1.382 0.167
EpiMatsmk -0.011 0.039 -0.271 0.786
EpiMatagg 0.020 0.060 0.327 0.744
EpiFamScore 0.064 0.058 1.095 0.273
EpiEduPar 0.012 0.073 0.164 0.870
Epin_trauma -0.022 0.078 -0.277 0.782
total -0.025 0.318 -0.077 0.938
npar fmin chisq df
27.000 0.000 0.000 0.000
pvalue baseline.chisq baseline.df baseline.pvalue
NA 101.992 23.000 0.000
cfi tli nnfi rfi
1.000 1.000 1.000 1.000
nfi pnfi ifi rni
1.000 0.000 1.000 1.000
logl unrestricted.logl aic bic
47.771 47.771 -41.542 22.773
ntotal bic2 rmsea rmsea.ci.lower
80.000 -62.368 0.000 0.000
rmsea.ci.upper rmsea.pvalue rmr rmr_nomean
0.000 NA 0.000 0.000
srmr srmr_bentler srmr_bentler_nomean crmr
0.000 0.000 0.000 0.000
crmr_nomean srmr_mplus srmr_mplus_nomean cn_05
0.000 0.000 0.000 NA
cn_01 gfi agfi pgfi
NA 1.000 1.000 0.000
mfi ecvi
1.000 0.675
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~1
14 group ~ Matsmk f
15 group ~ Matagg g
16 group ~ FamScore h
17 group ~ EduPar i
18 group ~ n_trauma j
19 group ~ Age
20 group ~ int_dis
21 group ~ medication
22 group ~ contraceptives
23 group ~ cigday_1
24 group ~ V8
25 group ~ Epi z
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 Matsmk ~1
95 Matagg ~1
96 FamScore ~1
97 EduPar ~1
98 n_trauma ~1
99 Age ~1
100 int_dis ~1
101 medication ~1
102 contraceptives ~1
103 cigday_1 ~1
104 V8 ~1
105 directMatsmk := f directMatsmk
106 directMatagg := g directMatagg
107 directFamScore := h directFamScore
108 directEduPar := i directEduPar
109 directn_trauma := j directn_trauma
110 EpiMatsmk := a*z EpiMatsmk
111 EpiMatagg := b*z EpiMatagg
112 EpiFamScore := c*z EpiFamScore
113 EpiEduPar := d*z EpiEduPar
114 Epin_trauma := e*z Epin_trauma
115 total := f+g+h+i+j+(a*z)+(b*z)+(c*z)+(d*z)+(e*z) total
est se z pvalue ci.lower ci.upper
1 0.886 0.118 7.504 0.000 0.655 1.117
2 0.009 0.033 0.272 0.786 -0.056 0.074
3 -0.017 0.051 -0.328 0.743 -0.116 0.083
4 -0.054 0.047 -1.133 0.257 -0.146 0.039
5 -0.010 0.061 -0.164 0.870 -0.130 0.110
6 0.018 0.066 0.277 0.782 -0.110 0.147
7 -0.042 0.065 -0.646 0.518 -0.169 0.085
8 -0.010 0.035 -0.274 0.784 -0.078 0.059
9 0.018 0.038 0.473 0.636 -0.056 0.092
10 0.074 0.035 2.145 0.032 0.006 0.142
11 -0.058 0.067 -0.866 0.386 -0.190 0.074
12 -0.522 0.195 -2.674 0.007 -0.905 -0.139
13 1.370 0.384 3.570 0.000 0.618 2.122
14 0.041 0.083 0.496 0.620 -0.121 0.203
15 0.192 0.126 1.527 0.127 -0.055 0.439
16 -0.100 0.119 -0.841 0.400 -0.333 0.133
17 -0.447 0.152 -2.933 0.003 -0.745 -0.148
18 0.226 0.163 1.382 0.167 -0.094 0.546
19 -0.305 0.162 -1.887 0.059 -0.622 0.012
20 0.233 0.087 2.664 0.008 0.062 0.404
21 0.342 0.094 3.632 0.000 0.158 0.527
22 0.082 0.089 0.929 0.353 -0.091 0.256
23 0.704 0.168 4.185 0.000 0.374 1.034
24 -0.211 0.507 -0.416 0.678 -1.205 0.783
25 -1.187 0.278 -4.262 0.000 -1.732 -0.641
26 0.013 0.002 6.325 0.000 0.009 0.017
27 0.080 0.013 6.325 0.000 0.055 0.105
28 0.194 0.000 NA NA 0.194 0.194
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.005 0.000 NA NA -0.005 -0.005
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.021 0.000 NA NA 0.021 0.021
35 0.004 0.000 NA NA 0.004 0.004
36 0.021 0.000 NA NA 0.021 0.021
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.090 0.000 NA NA 0.090 0.090
40 0.034 0.000 NA NA 0.034 0.034
41 -0.016 0.000 NA NA -0.016 -0.016
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.032 0.000 NA NA 0.032 0.032
45 0.007 0.000 NA NA 0.007 0.007
46 0.007 0.000 NA NA 0.007 0.007
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.131 0.000 NA NA 0.131 0.131
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.026 0.000 NA NA 0.026 0.026
52 0.004 0.000 NA NA 0.004 0.004
53 0.064 0.000 NA NA 0.064 0.064
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.043 0.000 NA NA 0.043 0.043
57 0.001 0.000 NA NA 0.001 0.001
58 0.053 0.000 NA NA 0.053 0.053
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.041 0.000 NA NA 0.041 0.041
69 0.017 0.000 NA NA 0.017 0.017
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.008 0.000 NA NA 0.008 0.008
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.210 0.000 NA NA 0.210 0.210
80 0.060 0.000 NA NA 0.060 0.060
81 0.060 0.000 NA NA 0.060 0.060
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.144 0.000 NA NA 0.144 0.144
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.210 0.000 NA NA 0.210 0.210
89 0.048 0.000 NA NA 0.048 0.048
90 0.003 0.000 NA NA 0.003 0.003
91 0.061 0.000 NA NA 0.061 0.061
92 0.001 0.000 NA NA 0.001 0.001
93 0.005 0.000 NA NA 0.005 0.005
94 0.262 0.000 NA NA 0.262 0.262
95 0.100 0.000 NA NA 0.100 0.100
96 0.225 0.000 NA NA 0.225 0.225
97 0.606 0.000 NA NA 0.606 0.606
98 0.196 0.000 NA NA 0.196 0.196
99 0.562 0.000 NA NA 0.562 0.562
100 0.300 0.000 NA NA 0.300 0.300
101 0.175 0.000 NA NA 0.175 0.175
102 0.300 0.000 NA NA 0.300 0.300
103 0.124 0.000 NA NA 0.124 0.124
104 0.529 0.000 NA NA 0.529 0.529
105 0.041 0.083 0.496 0.620 -0.121 0.203
106 0.192 0.126 1.527 0.127 -0.055 0.439
107 -0.100 0.119 -0.841 0.400 -0.333 0.133
108 -0.447 0.152 -2.933 0.003 -0.745 -0.148
109 0.226 0.163 1.382 0.167 -0.094 0.546
110 -0.011 0.039 -0.271 0.786 -0.088 0.067
111 0.020 0.060 0.327 0.744 -0.098 0.138
112 0.064 0.058 1.095 0.273 -0.050 0.177
113 0.012 0.073 0.164 0.870 -0.130 0.154
114 -0.022 0.078 -0.277 0.782 -0.174 0.131
115 -0.025 0.318 -0.077 0.938 -0.648 0.599
############################
############################
Epi_M15
############################
############################
lavaan 0.6-7 ended normally after 43 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 27
Used Total
Number of observations 80 99
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) -0.055 0.046 -1.192 0.233
Matagg (b) 0.074 0.071 1.046 0.296
FamScore (c) 0.113 0.066 1.706 0.088
EduPar (d) 0.229 0.085 2.683 0.007
n_trauma (e) -0.062 0.092 -0.680 0.497
Age -0.088 0.090 -0.973 0.331
int_dis -0.065 0.049 -1.325 0.185
medication -0.024 0.053 -0.456 0.649
contrcptvs 0.032 0.048 0.660 0.509
cigday_1 -0.052 0.094 -0.552 0.581
V8 0.007 0.273 0.026 0.979
group ~
Matsmk (f) -0.055 0.058 -0.943 0.346
Matagg (g) 0.326 0.088 3.695 0.000
FamScore (h) 0.137 0.083 1.644 0.100
EduPar (i) -0.083 0.111 -0.747 0.455
n_trauma (j) 0.108 0.114 0.953 0.341
Age -0.391 0.113 -3.464 0.001
int_dis 0.145 0.061 2.353 0.019
medication 0.284 0.066 4.337 0.000
contrcptvs 0.043 0.060 0.722 0.470
cigday_1 0.693 0.117 5.945 0.000
V8 0.419 0.338 1.241 0.215
Epi (z) -1.538 0.139 -11.093 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.597 0.165 3.619 0.000
.group 1.236 0.220 5.606 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.025 0.004 6.325 0.000
.group 0.039 0.006 6.325 0.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk -0.055 0.058 -0.943 0.346
directMatagg 0.326 0.088 3.695 0.000
directFamScore 0.137 0.083 1.644 0.100
directEduPar -0.083 0.111 -0.747 0.455
directn_trauma 0.108 0.114 0.953 0.341
EpiMatsmk 0.085 0.072 1.185 0.236
EpiMatagg -0.114 0.109 -1.041 0.298
EpiFamScore -0.173 0.103 -1.686 0.092
EpiEduPar -0.352 0.135 -2.608 0.009
Epin_trauma 0.096 0.141 0.678 0.498
total -0.025 0.318 -0.077 0.938
npar fmin chisq df
27.000 0.000 0.000 0.000
pvalue baseline.chisq baseline.df baseline.pvalue
NA 161.655 23.000 0.000
cfi tli nnfi rfi
1.000 1.000 1.000 1.000
nfi pnfi ifi rni
1.000 0.000 1.000 1.000
logl unrestricted.logl aic bic
50.121 50.121 -46.242 18.073
ntotal bic2 rmsea rmsea.ci.lower
80.000 -67.068 0.000 0.000
rmsea.ci.upper rmsea.pvalue rmr rmr_nomean
0.000 NA 0.000 0.000
srmr srmr_bentler srmr_bentler_nomean crmr
0.000 0.000 0.000 0.000
crmr_nomean srmr_mplus srmr_mplus_nomean cn_05
0.000 0.000 0.000 1.000
cn_01 gfi agfi pgfi
1.000 1.000 1.000 0.000
mfi ecvi
1.000 0.675
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~1
14 group ~ Matsmk f
15 group ~ Matagg g
16 group ~ FamScore h
17 group ~ EduPar i
18 group ~ n_trauma j
19 group ~ Age
20 group ~ int_dis
21 group ~ medication
22 group ~ contraceptives
23 group ~ cigday_1
24 group ~ V8
25 group ~ Epi z
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 Matsmk ~1
95 Matagg ~1
96 FamScore ~1
97 EduPar ~1
98 n_trauma ~1
99 Age ~1
100 int_dis ~1
101 medication ~1
102 contraceptives ~1
103 cigday_1 ~1
104 V8 ~1
105 directMatsmk := f directMatsmk
106 directMatagg := g directMatagg
107 directFamScore := h directFamScore
108 directEduPar := i directEduPar
109 directn_trauma := j directn_trauma
110 EpiMatsmk := a*z EpiMatsmk
111 EpiMatagg := b*z EpiMatagg
112 EpiFamScore := c*z EpiFamScore
113 EpiEduPar := d*z EpiEduPar
114 Epin_trauma := e*z Epin_trauma
115 total := f+g+h+i+j+(a*z)+(b*z)+(c*z)+(d*z)+(e*z) total
est se z pvalue ci.lower ci.upper
1 0.597 0.165 3.619 0.000 0.273 0.920
2 -0.055 0.046 -1.192 0.233 -0.146 0.036
3 0.074 0.071 1.046 0.296 -0.065 0.212
4 0.113 0.066 1.706 0.088 -0.017 0.242
5 0.229 0.085 2.683 0.007 0.062 0.396
6 -0.062 0.092 -0.680 0.497 -0.242 0.117
7 -0.088 0.090 -0.973 0.331 -0.265 0.089
8 -0.065 0.049 -1.325 0.185 -0.161 0.031
9 -0.024 0.053 -0.456 0.649 -0.127 0.079
10 0.032 0.048 0.660 0.509 -0.063 0.127
11 -0.052 0.094 -0.552 0.581 -0.236 0.132
12 0.007 0.273 0.026 0.979 -0.527 0.541
13 1.236 0.220 5.606 0.000 0.804 1.668
14 -0.055 0.058 -0.943 0.346 -0.168 0.059
15 0.326 0.088 3.695 0.000 0.153 0.498
16 0.137 0.083 1.644 0.100 -0.026 0.301
17 -0.083 0.111 -0.747 0.455 -0.299 0.134
18 0.108 0.114 0.953 0.341 -0.115 0.331
19 -0.391 0.113 -3.464 0.001 -0.612 -0.170
20 0.145 0.061 2.353 0.019 0.024 0.265
21 0.284 0.066 4.337 0.000 0.156 0.412
22 0.043 0.060 0.722 0.470 -0.074 0.161
23 0.693 0.117 5.945 0.000 0.465 0.922
24 0.419 0.338 1.241 0.215 -0.243 1.082
25 -1.538 0.139 -11.093 0.000 -1.809 -1.266
26 0.025 0.004 6.325 0.000 0.017 0.033
27 0.039 0.006 6.325 0.000 0.027 0.051
28 0.194 0.000 NA NA 0.194 0.194
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.005 0.000 NA NA -0.005 -0.005
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.021 0.000 NA NA 0.021 0.021
35 0.004 0.000 NA NA 0.004 0.004
36 0.021 0.000 NA NA 0.021 0.021
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.090 0.000 NA NA 0.090 0.090
40 0.034 0.000 NA NA 0.034 0.034
41 -0.016 0.000 NA NA -0.016 -0.016
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.032 0.000 NA NA 0.032 0.032
45 0.007 0.000 NA NA 0.007 0.007
46 0.007 0.000 NA NA 0.007 0.007
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.131 0.000 NA NA 0.131 0.131
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.026 0.000 NA NA 0.026 0.026
52 0.004 0.000 NA NA 0.004 0.004
53 0.064 0.000 NA NA 0.064 0.064
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.043 0.000 NA NA 0.043 0.043
57 0.001 0.000 NA NA 0.001 0.001
58 0.053 0.000 NA NA 0.053 0.053
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.041 0.000 NA NA 0.041 0.041
69 0.017 0.000 NA NA 0.017 0.017
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.008 0.000 NA NA 0.008 0.008
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.210 0.000 NA NA 0.210 0.210
80 0.060 0.000 NA NA 0.060 0.060
81 0.060 0.000 NA NA 0.060 0.060
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.144 0.000 NA NA 0.144 0.144
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.210 0.000 NA NA 0.210 0.210
89 0.048 0.000 NA NA 0.048 0.048
90 0.003 0.000 NA NA 0.003 0.003
91 0.061 0.000 NA NA 0.061 0.061
92 0.001 0.000 NA NA 0.001 0.001
93 0.005 0.000 NA NA 0.005 0.005
94 0.262 0.000 NA NA 0.262 0.262
95 0.100 0.000 NA NA 0.100 0.100
96 0.225 0.000 NA NA 0.225 0.225
97 0.606 0.000 NA NA 0.606 0.606
98 0.196 0.000 NA NA 0.196 0.196
99 0.562 0.000 NA NA 0.562 0.562
100 0.300 0.000 NA NA 0.300 0.300
101 0.175 0.000 NA NA 0.175 0.175
102 0.300 0.000 NA NA 0.300 0.300
103 0.124 0.000 NA NA 0.124 0.124
104 0.529 0.000 NA NA 0.529 0.529
105 -0.055 0.058 -0.943 0.346 -0.168 0.059
106 0.326 0.088 3.695 0.000 0.153 0.498
107 0.137 0.083 1.644 0.100 -0.026 0.301
108 -0.083 0.111 -0.747 0.455 -0.299 0.134
109 0.108 0.114 0.953 0.341 -0.115 0.331
110 0.085 0.072 1.185 0.236 -0.056 0.225
111 -0.114 0.109 -1.041 0.298 -0.327 0.100
112 -0.173 0.103 -1.686 0.092 -0.375 0.028
113 -0.352 0.135 -2.608 0.009 -0.617 -0.088
114 0.096 0.141 0.678 0.498 -0.181 0.372
115 -0.025 0.318 -0.077 0.938 -0.648 0.599
############################
############################
Epi_M_all
############################
############################
lavaan 0.6-7 ended normally after 57 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 27
Used Total
Number of observations 80 99
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) 0.025 0.075 0.330 0.742
Matagg (b) 0.132 0.114 1.162 0.245
FamScore (c) -0.102 0.106 -0.956 0.339
EduPar (d) -0.281 0.137 -2.043 0.041
n_trauma (e) 0.074 0.147 0.506 0.613
Age 0.122 0.146 0.836 0.403
int_dis 0.085 0.079 1.082 0.279
medication 0.050 0.085 0.589 0.556
contrcptvs -0.074 0.078 -0.948 0.343
cigday_1 0.168 0.151 1.114 0.265
V8 0.285 0.439 0.649 0.516
group ~
Matsmk (f) 0.002 0.031 0.060 0.952
Matagg (g) 0.059 0.047 1.256 0.209
FamScore (h) 0.081 0.044 1.838 0.066
EduPar (i) -0.110 0.058 -1.893 0.058
n_trauma (j) 0.118 0.061 1.936 0.053
Age -0.396 0.060 -6.555 0.000
int_dis 0.146 0.033 4.440 0.000
medication 0.263 0.035 7.484 0.000
contrcptvs 0.079 0.032 2.459 0.014
cigday_1 0.578 0.063 9.193 0.000
V8 0.079 0.182 0.437 0.662
Epi (z) 1.156 0.046 25.015 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.288 0.265 1.085 0.278
.group -0.014 0.110 -0.127 0.899
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.065 0.010 6.325 0.000
.group 0.011 0.002 6.325 0.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk 0.002 0.031 0.060 0.952
directMatagg 0.059 0.047 1.256 0.209
directFamScore 0.081 0.044 1.838 0.066
directEduPar -0.110 0.058 -1.893 0.058
directn_trauma 0.118 0.061 1.936 0.053
EpiMatsmk 0.028 0.086 0.330 0.742
EpiMatagg 0.153 0.131 1.161 0.246
EpiFamScore -0.117 0.123 -0.955 0.340
EpiEduPar -0.325 0.159 -2.037 0.042
Epin_trauma 0.086 0.170 0.506 0.613
total -0.025 0.318 -0.077 0.938
npar fmin chisq df
27.000 0.000 0.000 0.000
pvalue baseline.chisq baseline.df baseline.pvalue
NA 261.781 23.000 0.000
cfi tli nnfi rfi
1.000 1.000 1.000 1.000
nfi pnfi ifi rni
1.000 0.000 1.000 1.000
logl unrestricted.logl aic bic
61.906 61.906 -69.811 -5.496
ntotal bic2 rmsea rmsea.ci.lower
80.000 -90.637 0.000 0.000
rmsea.ci.upper rmsea.pvalue rmr rmr_nomean
0.000 NA 0.000 0.000
srmr srmr_bentler srmr_bentler_nomean crmr
0.000 0.000 0.000 0.000
crmr_nomean srmr_mplus srmr_mplus_nomean cn_05
0.000 0.000 0.000 NA
cn_01 gfi agfi pgfi
NA 1.000 1.000 0.000
mfi ecvi
1.000 0.675
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~1
14 group ~ Matsmk f
15 group ~ Matagg g
16 group ~ FamScore h
17 group ~ EduPar i
18 group ~ n_trauma j
19 group ~ Age
20 group ~ int_dis
21 group ~ medication
22 group ~ contraceptives
23 group ~ cigday_1
24 group ~ V8
25 group ~ Epi z
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 Matsmk ~1
95 Matagg ~1
96 FamScore ~1
97 EduPar ~1
98 n_trauma ~1
99 Age ~1
100 int_dis ~1
101 medication ~1
102 contraceptives ~1
103 cigday_1 ~1
104 V8 ~1
105 directMatsmk := f directMatsmk
106 directMatagg := g directMatagg
107 directFamScore := h directFamScore
108 directEduPar := i directEduPar
109 directn_trauma := j directn_trauma
110 EpiMatsmk := a*z EpiMatsmk
111 EpiMatagg := b*z EpiMatagg
112 EpiFamScore := c*z EpiFamScore
113 EpiEduPar := d*z EpiEduPar
114 Epin_trauma := e*z Epin_trauma
115 total := f+g+h+i+j+(a*z)+(b*z)+(c*z)+(d*z)+(e*z) total
est se z pvalue ci.lower ci.upper
1 0.288 0.265 1.085 0.278 -0.232 0.808
2 0.025 0.075 0.330 0.742 -0.122 0.171
3 0.132 0.114 1.162 0.245 -0.091 0.355
4 -0.102 0.106 -0.956 0.339 -0.310 0.107
5 -0.281 0.137 -2.043 0.041 -0.550 -0.011
6 0.074 0.147 0.506 0.613 -0.214 0.363
7 0.122 0.146 0.836 0.403 -0.164 0.407
8 0.085 0.079 1.082 0.279 -0.069 0.240
9 0.050 0.085 0.589 0.556 -0.116 0.216
10 -0.074 0.078 -0.948 0.343 -0.226 0.079
11 0.168 0.151 1.114 0.265 -0.128 0.464
12 0.285 0.439 0.649 0.516 -0.575 1.144
13 -0.014 0.110 -0.127 0.899 -0.230 0.202
14 0.002 0.031 0.060 0.952 -0.059 0.062
15 0.059 0.047 1.256 0.209 -0.033 0.152
16 0.081 0.044 1.838 0.066 -0.005 0.168
17 -0.110 0.058 -1.893 0.058 -0.224 0.004
18 0.118 0.061 1.936 0.053 -0.001 0.237
19 -0.396 0.060 -6.555 0.000 -0.514 -0.278
20 0.146 0.033 4.440 0.000 0.081 0.210
21 0.263 0.035 7.484 0.000 0.194 0.332
22 0.079 0.032 2.459 0.014 0.016 0.143
23 0.578 0.063 9.193 0.000 0.455 0.702
24 0.079 0.182 0.437 0.662 -0.277 0.436
25 1.156 0.046 25.015 0.000 1.065 1.246
26 0.065 0.010 6.325 0.000 0.045 0.086
27 0.011 0.002 6.325 0.000 0.008 0.015
28 0.194 0.000 NA NA 0.194 0.194
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.005 0.000 NA NA -0.005 -0.005
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.021 0.000 NA NA 0.021 0.021
35 0.004 0.000 NA NA 0.004 0.004
36 0.021 0.000 NA NA 0.021 0.021
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.090 0.000 NA NA 0.090 0.090
40 0.034 0.000 NA NA 0.034 0.034
41 -0.016 0.000 NA NA -0.016 -0.016
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.032 0.000 NA NA 0.032 0.032
45 0.007 0.000 NA NA 0.007 0.007
46 0.007 0.000 NA NA 0.007 0.007
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.131 0.000 NA NA 0.131 0.131
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.026 0.000 NA NA 0.026 0.026
52 0.004 0.000 NA NA 0.004 0.004
53 0.064 0.000 NA NA 0.064 0.064
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.043 0.000 NA NA 0.043 0.043
57 0.001 0.000 NA NA 0.001 0.001
58 0.053 0.000 NA NA 0.053 0.053
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.041 0.000 NA NA 0.041 0.041
69 0.017 0.000 NA NA 0.017 0.017
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.008 0.000 NA NA 0.008 0.008
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.210 0.000 NA NA 0.210 0.210
80 0.060 0.000 NA NA 0.060 0.060
81 0.060 0.000 NA NA 0.060 0.060
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.144 0.000 NA NA 0.144 0.144
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.210 0.000 NA NA 0.210 0.210
89 0.048 0.000 NA NA 0.048 0.048
90 0.003 0.000 NA NA 0.003 0.003
91 0.061 0.000 NA NA 0.061 0.061
92 0.001 0.000 NA NA 0.001 0.001
93 0.005 0.000 NA NA 0.005 0.005
94 0.262 0.000 NA NA 0.262 0.262
95 0.100 0.000 NA NA 0.100 0.100
96 0.225 0.000 NA NA 0.225 0.225
97 0.606 0.000 NA NA 0.606 0.606
98 0.196 0.000 NA NA 0.196 0.196
99 0.562 0.000 NA NA 0.562 0.562
100 0.300 0.000 NA NA 0.300 0.300
101 0.175 0.000 NA NA 0.175 0.175
102 0.300 0.000 NA NA 0.300 0.300
103 0.124 0.000 NA NA 0.124 0.124
104 0.529 0.000 NA NA 0.529 0.529
105 0.002 0.031 0.060 0.952 -0.059 0.062
106 0.059 0.047 1.256 0.209 -0.033 0.152
107 0.081 0.044 1.838 0.066 -0.005 0.168
108 -0.110 0.058 -1.893 0.058 -0.224 0.004
109 0.118 0.061 1.936 0.053 -0.001 0.237
110 0.028 0.086 0.330 0.742 -0.141 0.197
111 0.153 0.131 1.161 0.246 -0.105 0.410
112 -0.117 0.123 -0.955 0.340 -0.358 0.124
113 -0.325 0.159 -2.037 0.042 -0.637 -0.012
114 0.086 0.170 0.506 0.613 -0.248 0.420
115 -0.025 0.318 -0.077 0.938 -0.648 0.599rmd_paths <-paste0(tempfile(c(names(Netlist))),".Rmd")
names(rmd_paths) <- names(Netlist)
for (n in names(rmd_paths)) {
sink(file = rmd_paths[n])
cat(" \n",
"```{r, echo = FALSE}",
"Netlist[[n]]",
"```",
sep = " \n")
sink()
}Interactive results SEM analysis
only direct effects with a significant standardized effect of p<0.05 are shown.
for (n in names(rmd_paths)) {
cat(knitr::knit_child(rmd_paths[[n]],
quiet= TRUE))
file.remove(rmd_paths[[n]])
}
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] plyr_1.8.6 scales_1.1.1
[3] RCircos_1.2.1 compareGroups_4.4.6
[5] lme4_1.1-26 Matrix_1.2-18
[7] webshot_0.5.2 visNetwork_2.0.9
[9] org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0
[11] xlsx_0.6.5 gprofiler2_0.2.0
[13] BiocParallel_1.24.1 kableExtra_1.3.1
[15] glmpca_0.2.0 knitr_1.30
[17] DESeq2_1.30.0 SummarizedExperiment_1.20.0
[19] Biobase_2.50.0 MatrixGenerics_1.2.0
[21] matrixStats_0.57.0 GenomicRanges_1.42.0
[23] GenomeInfoDb_1.26.2 IRanges_2.24.1
[25] S4Vectors_0.28.1 BiocGenerics_0.36.0
[27] forcats_0.5.0 stringr_1.4.0
[29] dplyr_1.0.2 purrr_0.3.4
[31] readr_1.4.0 tidyr_1.1.2
[33] tibble_3.0.4 tidyverse_1.3.0
[35] semPlot_1.1.2 lavaan_0.6-7
[37] viridis_0.5.1 viridisLite_0.3.0
[39] WGCNA_1.69 fastcluster_1.1.25
[41] dynamicTreeCut_1.63-1 ggplot2_3.3.3
[43] gplots_3.1.1 corrplot_0.84
[45] RColorBrewer_1.1-2
loaded via a namespace (and not attached):
[1] coda_0.19-4 bit64_4.0.5 DelayedArray_0.16.0
[4] data.table_1.13.6 rpart_4.1-15 RCurl_1.98-1.2
[7] doParallel_1.0.16 generics_0.1.0 preprocessCore_1.52.1
[10] callr_3.5.1 RSQLite_2.2.2 mice_3.12.0
[13] chron_2.3-56 bit_4.0.4 xml2_1.3.2
[16] lubridate_1.7.9.2 httpuv_1.5.5 assertthat_0.2.1
[19] d3Network_0.5.2.1 xfun_0.20 hms_1.0.0
[22] rJava_0.9-13 evaluate_0.14 promises_1.1.1
[25] fansi_0.4.1 caTools_1.18.1 dbplyr_2.0.0
[28] readxl_1.3.1 igraph_1.2.6 DBI_1.1.1
[31] geneplotter_1.68.0 tmvnsim_1.0-2 Rsolnp_1.16
[34] htmlwidgets_1.5.3 ellipsis_0.3.1 crosstalk_1.1.1
[37] backports_1.2.0 pbivnorm_0.6.0 annotate_1.68.0
[40] vctrs_0.3.6 abind_1.4-5 cachem_1.0.1
[43] withr_2.4.1 HardyWeinberg_1.7.1 checkmate_2.0.0
[46] fdrtool_1.2.16 mnormt_2.0.2 cluster_2.1.0
[49] mi_1.0 lazyeval_0.2.2 crayon_1.3.4
[52] genefilter_1.72.0 pkgconfig_2.0.3 nlme_3.1-151
[55] nnet_7.3-15 rlang_0.4.10 lifecycle_0.2.0
[58] kutils_1.70 modelr_0.1.8 cellranger_1.1.0
[61] rprojroot_2.0.2 flextable_0.6.2 regsem_1.6.2
[64] carData_3.0-4 boot_1.3-26 reprex_1.0.0
[67] base64enc_0.1-3 processx_3.4.5 whisker_0.4
[70] png_0.1-7 rjson_0.2.20 bitops_1.0-6
[73] KernSmooth_2.23-18 blob_1.2.1 workflowr_1.6.2
[76] arm_1.11-2 jpeg_0.1-8.1 rockchalk_1.8.144
[79] memoise_2.0.0 magrittr_2.0.1 zlibbioc_1.36.0
[82] compiler_4.0.3 cli_2.2.0 XVector_0.30.0
[85] ps_1.5.0 pbapply_1.4-3 htmlTable_2.1.0
[88] Formula_1.2-4 MASS_7.3-53 tidyselect_1.1.0
[91] stringi_1.5.3 lisrelToR_0.1.4 sem_3.1-11
[94] yaml_2.2.1 OpenMx_2.18.1 locfit_1.5-9.4
[97] latticeExtra_0.6-29 grid_4.0.3 tools_4.0.3
[100] matrixcalc_1.0-3 rstudioapi_0.13 uuid_0.1-4
[103] foreach_1.5.1 foreign_0.8-81 git2r_0.28.0
[106] gridExtra_2.3 farver_2.0.3 BDgraph_2.63
[109] digest_0.6.27 shiny_1.6.0 Rcpp_1.0.5
[112] broom_0.7.3 later_1.1.0.1 writexl_1.3.1
[115] httr_1.4.2 gdtools_0.2.3 psych_2.0.12
[118] colorspace_2.0-0 rvest_0.3.6 XML_3.99-0.5
[121] fs_1.5.0 truncnorm_1.0-8 splines_4.0.3
[124] statmod_1.4.35 xlsxjars_0.6.1 plotly_4.9.3
[127] systemfonts_0.3.2 xtable_1.8-4 jsonlite_1.7.2
[130] nloptr_1.2.2.2 corpcor_1.6.9 glasso_1.11
[133] R6_2.5.0 Hmisc_4.4-2 pillar_1.4.7
[136] htmltools_0.5.1.1 mime_0.9 glue_1.4.2
[139] fastmap_1.1.0 minqa_1.2.4 codetools_0.2-18
[142] lattice_0.20-41 huge_1.3.4.1 gtools_3.8.2
[145] officer_0.3.16 zip_2.1.1 GO.db_3.12.1
[148] openxlsx_4.2.3 survival_3.2-7 rmarkdown_2.6
[151] qgraph_1.6.5 munsell_0.5.0 GenomeInfoDbData_1.2.4
[154] iterators_1.0.13 impute_1.64.0 haven_2.3.1
[157] reshape2_1.4.4 gtable_0.3.0