The Methylome in Female Adolescent Conduct Disorder: Neural Pathomechanisms and Environmental Risk Factors
Post-hoc Analyses
AG Chiocchetti
29 Dezember 2020
Last updated: 2021-09-17
Checks: 6 1
Knit directory: femNATCD_MethSeq/
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Home = getwd()Sensitivity Analyses
Sensitivity EWAS
including SES
collector=data.frame(originalP=results_Deseq$pvalue,
originall2FC=results_Deseq$log2FoldChange)
rownames(collector)=paste0("Epi", 1:nrow(collector))
parm="EduPar"
workingcopy = dds_filt
workingcopy=workingcopy[,as.vector(!is.na(colData(dds_filt)[parm]))]
modelpar=as.character(design(dds_filt))[2]
tmpmod=gsub("0", paste0("~ 0 +",parm), modelpar)
tmpmod=gsub("int_dis \\+", "", tmpmod)
modelpar=as.formula(tmpmod)
design(workingcopy) = modelpar
workingcopy = DESeq(workingcopy, parallel = T)
parmres=results(workingcopy)
collector[,paste0(parm,"P")] = parmres$pvalue
collector[,paste0(parm,"l2FC")] = parmres$log2FoldChange
idx=collector$originalP<=thresholdp
idx=collector[,paste0(parm,"P")]<=thresholdp
table(collector$originalP<=thresholdp, collector[paste0(parm,"P")]<=thresholdp) %>% fisher.test()
cor.test(collector$originall2FC[idx],collector[,paste0(parm,"l2FC")][idx],
method = "spearman")
qqplot(y=-log10(collector[,paste0(parm,"P")]),
x = -log(runif(nrow(collector))), xlim=c(0,12),ylim=c(0,12),
col="gray", ylab="", xlab="")
par(new=T)
qqplot(y=-log10(collector$originalP),
x = -log(runif(nrow(collector))),xlim=c(0,12),ylim=c(0,12),
xlab="expected",ylab="observed")
abline(0,1,col="red")
legend("topleft", pch=1, col=c("black", "gray"), legend=c("original", parm))
plot(collector$originall2FC[idx],collector[,paste0(parm,"l2FC")][idx], pch=16,
main="log 2 foldchange", ylab=parm, xlab="original")excluding int_dis
### excluding int_dist
modelpar=as.character(design(dds_filt))[2]
modelpar=as.formula(paste("~",gsub("int_dis +", "", modelpar)))
design(workingcopy) = modelpar
workingcopy = DESeq(workingcopy, parallel=T)
parmres=results(workingcopy)
parm="wo.int.dis"
collector[,paste0(parm,"P")] = parmres$pvalue
collector[,paste0(parm,"l2FC")] = parmres$log2FoldChange
idx=collector$originalP<=thresholdp
idx=collector[,paste0(parm,"P")]<=thresholdp
table(collector$originalP<=thresholdp, collector[paste0(parm,"P")]<=thresholdp) %>% fisher.test()
cor.test(collector$originall2FC[idx],collector[,paste0(parm,"l2FC")][idx],
method = "spearman")
qqplot(y=-log10(collector[,paste0(parm,"P")]),
x = -log(runif(nrow(collector))), xlim=c(0,12),ylim=c(0,12),
col="gray", ylab="", xlab="")
par(new=T)
qqplot(y=-log10(collector$originalP),
x = -log(runif(nrow(collector))),xlim=c(0,12),ylim=c(0,12),
xlab="expected",ylab="observed")
abline(0,1,col="red")
legend("topleft", pch=1, col=c("black", "gray"), legend=c("original", parm))
plot(collector$originall2FC[idx],collector[,paste0(parm,"l2FC")][idx], pch=16,
main="log 2 foldchange", ylab=parm, xlab="original")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 + ", model0))
lmres=lm(model0, data=Probdat)
lmrescoeff = as.data.frame(coefficients(summary(lmres)))
totestpar=c("site","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", "Pr(>|t|)")]
for( parm in totestpar){
modelpar=as.character(design(dds_filt))[2]
modelpar=as.formula(gsub("0", paste0("topHit ~ 0 +",parm), modelpar))
lmres=lm(modelpar, data=Probdat)
lmrescoeff = as.data.frame(coefficients(summary(lmres)))
ressens[parm,] = lmrescoeff["groupCD", c("Estimate", "Std. Error", "Pr(>|t|)")]
}
modelpar=as.character(design(dds_filt))[2]
modelpar=as.formula(gsub("int_dis +", "", gsub("0", "topHit ~ 0", modelpar)))
lmres=lm(modelpar, data=Probdat)
lmrescoeff = as.data.frame(coefficients(summary(lmres)))
ressens["w/o_int_dis",] = lmrescoeff["groupCD", c("Estimate", "Std. Error", "Pr(>|t|)")]
a = 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 + +Age + int_dis + medication + contraceptives + cigday_1 + V8 + group
all other models represent the original model + the variable of interest
Real-time PCR validation
Data loading and parsing
RefGenes = c("GUSB")
Targets_of_Int = c("SLITRK5", "MIR4500HG")
nreplicates = 3
flagscore=Inf #replication quality error
SamplesMeta=read_xlsx(paste0(Home,"/data/RTrawdata/ZelllinienRNA_femNAT.xlsx"))
as.data.frame(SamplesMeta) -> SamplesMeta
SamplesMeta$Pou=paste("POU", SamplesMeta$Pou)
rownames(SamplesMeta)=SamplesMeta$Pou
SamplesMeta$Group = dds_filt$group[match(SamplesMeta$femNATID, dds_filt$ID_femNAT)]
Files=list.files(paste0(Home,"/data/RTrawdata/"), full.names = T)
Files=Files[grepl("_data",Files)]
Sets=unique(substr(basename(Files), 1,8))
Targets_all=vector()
Samples_all=vector()
geoMean=function(x){
x=x[!is.na(x)]
if(length(x)==0)
return(NA)
else
return((prod(x))^(1/length(x)))}
for (Set in Sets){
Setfiles=Files[grep(Set, Files)]
for( i in 1:length(Setfiles)){
tmp=read.table(Setfiles[i], skip=8, header=T, sep="\t", comment.char = "", fill=T)[1:96,]
tmp=tmp[,c("Sample.Name", "Target.Name","CÑ.")]
colnames(tmp)=c("Sample.Name", "Target.Name", "CT")
tmp$Target.Name=gsub("SLITRK5_L", "SLITRK5_", tmp$Target.Name)
tmp$Target.Name=gsub("VD_", "", tmp$Target.Name)
tmp$Target.Name=gsub("_", "", tmp$Target.Name)
tmp$Target.Name=substr(tmp$Target.Name,1, regexpr("#", tmp$Target.Name)-1)
tmp$CT=as.numeric(tmp$CT)
# set bad replicates to NA
tmpmu = tapply(tmp$CT, paste0(tmp$Sample.Name,"_",tmp$Target.Name), mean, na.rm=T)
tmpsd = tapply(tmp$CT, paste0(tmp$Sample.Name,"_",tmp$Target.Name), sd, na.rm=T)
for (corr in which(tmpsd>flagscore)){
index=unlist(strsplit(names(tmpmu)[corr], "_"))
tmp[which(tmp$Sample.Name==index[1] & tmp$Target.Name==index[2]),"CT"] = NA
}
assign(paste0("tmp_",Set,"_",i),tmp)
}
tmp=do.call("rbind", mget(apropos(paste0("tmp_",Set))))
tmp=tmp[which(!(tmp$Sample.Name==""|is.na(tmp$Sample.Name))), ]
tmp=tmp[!tmp$Sample.Name=="NTC",]
Samples=unique(tmp$Sample.Name)
Targets=unique(tmp$Target.Name)
Samples_all=unique(c(Samples_all, Samples))
Targets_all=unique(c(Targets_all, Targets))
Reform=data.frame(matrix(NA, nrow=length(Samples), ncol=length(Targets)*nreplicates))
colnames(Reform)=paste0(rep(Targets, each=3), letters[1:nreplicates])
rownames(Reform)=Samples
for (i in Samples) {
#print(i)
for (j in Targets){
Reform[i,grep(j, colnames(Reform))]=tmp[tmp$Sample.Name==i & tmp$Target.Name==j,"CT"]
}
}
HK=colnames(Reform)[grep(paste0(RefGenes, collapse="|"),colnames(Reform))]
GMHK=apply(Reform[,HK], 1, geoMean)
tmp2=Reform-GMHK
assign(paste0(Set,"_dCT"), tmp2)
rm(list=c(apropos("tmp"), "Reform", "GMHK"))
}Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenWarning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenWarning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenWarning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenWarning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenWarning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenWarning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenWarning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugt
Warning: NAs durch Umwandlung erzeugtWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'get_java_tmp_dir' nicht gefundenWarning in rm(list = c(apropos("tmp"), "Reform", "GMHK")): Objekt
'set_java_tmp_dir' nicht gefundenSamples_all=unique(Samples_all)
Targets_all = unique(Targets_all)
mergedCTtable=data.frame(matrix(NA,ncol=length(Targets_all)*nreplicates, nrow=length(Samples_all)))
colnames(mergedCTtable)=paste0(rep(unique(Targets_all), each=nreplicates), letters[1:nreplicates])
rownames(mergedCTtable)=Samples_all
CTobj=apropos("_dCT")
for( obj in CTobj){
DF=get(obj)
for(k in colnames(DF)){
for(l in rownames(DF)){
mergedCTtable[l,k]=DF[l,k]
}
}
}
CTmeans=colMeans(mergedCTtable, na.rm = T)
meanvec=tapply(CTmeans,gsub(paste0(letters[1:nreplicates],collapse="|"),"",names(CTmeans)), mean, na.rm=T)
meanvec = rep(meanvec, each=nreplicates)
names(meanvec) = paste0(names(meanvec), letters[1:nreplicates])
meanvec=meanvec[colnames(mergedCTtable)]
ddCT=apply(mergedCTtable,1, function(x){x-meanvec})
FC=2^-ddCT
SamplesMeta$inset=F
SamplesMeta$inset[SamplesMeta$Pou %in% colnames(FC)]=T
SamplesMeta=SamplesMeta[SamplesMeta$inset,]
CTRLCASEsorter=c(which(SamplesMeta$Group=="CTRL"),which(SamplesMeta$Group=="CD"))
SamplesMeta = SamplesMeta[CTRLCASEsorter, ]
searcher=paste0(Targets_of_Int, collapse = "|")
FC = FC[grepl(searcher, rownames(FC)),SamplesMeta$Pou]
MuFC=apply(FC, 2, function(x){tapply(log2(x), gsub("a|b|c","",rownames(FC)), mean, na.rm=T)})
SDFC=apply(FC, 2, function(x){tapply(log2(x), gsub("a|b|c","",rownames(FC)), sd, na.rm=T)})plot relative expression by samples
pdf(paste0(Home, "/output/barplots.pdf"))
for(i in Targets_of_Int){
if(any(!is.na(MuFC[i,]))){
a=barplot(unlist(MuFC[i,]), col=as.numeric(SamplesMeta[colnames(MuFC),"Group"])+1, main=i, las=3,
ylim=c(-max(abs(MuFC[i,])*1.2, na.rm=T), (max(abs(MuFC[i,])*1.2, na.rm=T))))
arrows(a, MuFC[i,], a, MuFC[i,]+SDFC[i,], angle = 90, length = 0.1)
arrows(a, MuFC[i,], a, MuFC[i,]-SDFC[i,], angle = 90, length = 0.1)
legend("topleft", c("case", "control"), col=c(1,2), pch=15, bty="n")
} else {
plot(0,0, type="n", main=paste(i, "not detected"))
}
}
dev.off()png
2 for(i in Targets_of_Int){
if(any(!is.na(MuFC[i,]))){
a=barplot(unlist(MuFC[i,]), col=as.numeric(SamplesMeta[colnames(MuFC),"Group"])+1, main=i, las=3,
ylim=c(-max(abs(MuFC[i,])*1.2, na.rm=T), (max(abs(MuFC[i,])*1.2, na.rm=T))))
arrows(a, MuFC[i,], a, MuFC[i,]+SDFC[i,], angle = 90, length = 0.1)
arrows(a, MuFC[i,], a, MuFC[i,]-SDFC[i,], angle = 90, length = 0.1)
legend("topleft", c("case", "control"), col=c(1,2), pch=15, bty="n")
} else {
plot(0,0, type="n", main=paste(i, "not detected"))
}
}
compare across groups
sink(paste0(Home, "/output/ResultsgroupComp.txt"))
Group=SamplesMeta$Group
for(i in Targets_of_Int){
print(i)
print(summary(try(lm(unlist(MuFC[i,])~Group))))
print(t.test(unlist(MuFC[i,])~Group))
}[1] "SLITRK5"
Call:
lm(formula = unlist(MuFC[i, ]) ~ Group)
Residuals:
Min 1Q Median 3Q Max
-1.3710 -0.4489 0.1142 0.4382 0.9983
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1703 0.1718 -0.991 0.334
GroupCD 0.5109 0.2975 1.717 0.102
Residual standard error: 0.6427 on 19 degrees of freedom
Multiple R-squared: 0.1343, Adjusted R-squared: 0.08878
F-statistic: 2.949 on 1 and 19 DF, p-value: 0.1022
Welch Two Sample t-test
data: unlist(MuFC[i, ]) by Group
t = -2.0316, df = 18.197, p-value = 0.05706
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.03871726 0.01701686
sample estimates:
mean in group CTRL mean in group CD
-0.1702834 0.3405668
[1] "MIR4500HG"
Call:
lm(formula = unlist(MuFC[i, ]) ~ Group)
Residuals:
Min 1Q Median 3Q Max
-2.7172 -1.2116 -0.2660 0.5749 5.4474
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6053 0.5683 -1.065 0.301
GroupCD 0.6614 0.9607 0.688 0.500
Residual standard error: 2.049 on 18 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.02566, Adjusted R-squared: -0.02847
F-statistic: 0.474 on 1 and 18 DF, p-value: 0.4999
Welch Two Sample t-test
data: unlist(MuFC[i, ]) by Group
t = -0.65047, df = 10.602, p-value = 0.5292
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.909646 1.586853
sample estimates:
mean in group CTRL mean in group CD
-0.60525003 0.05614653 sink()
SamplesMeta$femNATID2=paste0("ID_",gsub("-","_",SamplesMeta$femNATID))
SamplesMeta=SamplesMeta[SamplesMeta$Pou %in% colnames(MuFC),]
MuFC=MuFC[,SamplesMeta$Pou]
TPM4RNA=selEpitpm[,SamplesMeta$femNATID2]
colnames(TPM4RNA)=SamplesMeta$Pou
tags=list()
Targets=Targets_of_Int
sigtags=which(restab$padj<=0.05)
tagsOI=grep(paste0(Targets, collapse = "|"),selEpiMeta$gene)
sigtagsOI = tagsOI[tagsOI %in% sigtags]
fintagsOI=data.frame(tags=sigtagsOI, gene=selEpiMeta[sigtagsOI,"gene"])
#Targ=Targets[1]
#tag=tags[1]compare against methylation level
pdf(paste0(Home,"/output/RNAvsMETplots.pdf"), width = 15, height = 8)
MuFC=as.data.frame(MuFC)
MuFCsel=MuFC[Targets,]
par(mar=c(5,5,5,3), mfrow=c(1,2))
for (Targ in Targets){
tags=fintagsOI$tags[grep(Targ, fintagsOI$gene)]
for (tag in tags){
data=data.frame(tpm=unlist(TPM4RNA[tag,SamplesMeta$Pou]) ,
RT=unlist(MuFCsel[grep(Targ, rownames(MuFCsel)),SamplesMeta$Pou]))
plot(data$tpm,data$RT,
xlab="methylation tpm",
ylab = "mRNA log2FC vs mean",
ylim=c(-3,3),
col=4-as.numeric(SamplesMeta$Group),
pch=as.numeric(SamplesMeta$Group)+14,
main=paste(tag, "Meth vs mRNA Expr", Targ))
legend("topleft", c("control", "case"), pch=c(15,16), col=c(3,2), bty="n")
a=lm(RT~tpm, data)
b=summary(a)
abline(a, col="blue")
SperCor=cor(data$RT,data$tpm,use = "c", method = "spearman")
mtext(3, text = paste("beta = ", round(coefficients(a)[2],2),
"; se =", round(b$coefficients[2,2],2),
"; pvalue = ", round(b$coefficients[2,4],3),
"; sperman cor = ", round(SperCor,3)))
}
}
dev.off()png
2 MuFC=as.data.frame(MuFC)
MuFCsel=MuFC[Targets,]
par(mar=c(5,5,5,3), mfrow=c(1,2))
for (Targ in Targets){
tags=fintagsOI$tags[grep(Targ, fintagsOI$gene)]
for (tag in tags){
data=data.frame(tpm=unlist(TPM4RNA[tag,SamplesMeta$Pou]) ,
RT=unlist(MuFCsel[grep(Targ, rownames(MuFCsel)),SamplesMeta$Pou]))
plot(data$tpm,data$RT,
xlab="methylation tpm",
ylab = "mRNA log2FC vs mean",
ylim=c(-3,3),
col=4-as.numeric(SamplesMeta$Group),
pch=as.numeric(SamplesMeta$Group)+14,
main=paste(tag, "Meth vs mRNA Expr", Targ))
legend("topleft", c("control", "case"), pch=c(15,16), col=c(3,2), bty="n")
a=lm(RT~tpm, data)
b=summary(a)
abline(a, col="blue")
SperCor=cor(data$RT,data$tpm,use = "c", method = "spearman")
mtext(3, text = paste("beta = ", round(coefficients(a)[2],2),
"; se =", round(b$coefficients[2,2],2),
"; pvalue = ", round(b$coefficients[2,4],3),
"; Spearman cor = ", round(SperCor,3)))
}
}
Systems Analysis
Genomic feature enrichment
Significant loci with a p-value <= 0.01 and a absolute log2 fold-change lager 0.5 were tested for enrichment in annotated genomic feature using fisher exact test.
Ranges=rowData(dds_filt)
TotTagsofInterest=sum(Ranges$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")Brain Developmental Processes Enrichment tests
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.
Kang Modules
# 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]]$Padj<=0.05]
Modsig = c(Modsig,a)
}Brain espresseion heatmaps
# show brains and expression
Modsig2=unique(Modsig[Modsig!="M_all"])
load(paste0(Home,"/data/Kang_DataPreprocessing.RData")) #Load the Kang expression data of all genes
datExprPlot=matriz #Expression data of Kang loaded as Rdata object DataPreprocessing.RData
Genes = GL_all[names(GL_all)!="M_all"]
Genes_expression<-list()
pcatest<-list()
for (i in names(Genes)){
Genes_expression[[i]]<-matriz[,which(colnames(matriz) %in% Genes[[i]])]
pcatest[[i]]=prcomp(t(as.matrix(Genes_expression[[i]])),retx=TRUE)
}
# PCA test
PCA<-data.frame(pcatest[[1]]$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)------------------------------------------------------------------------------
Attache Paket: '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 Risk Factor Mediation Analysis
Risk factor loading and correlation plots
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$dim1
EpiMarker = c(EpiMarker, epiname)
}
cormat = cor(apply(Patdata[,c("group", envFact, modelFact, EpiMarker)] %>% mutate_all(as.numeric), 2, minmax_scaling),
use = "pairwise.complete.obs")
par(mfrow=c(1,2))
corrplot(cormat, main="correlations")
corrplot(cormat, order = "hclust", main="correlations ordered")
SEM analysis
fullmodEnv=paste(unique(envFact,modelFact), sep = "+", collapse = "+")
Dataset = Patdata[,c("group", envFact, modelFact,EpiMarker)]
model = "
Epi~a*Matsmk+b*Matagg+c*FamScore+d*EduPar+e*n_trauma+Age+int_dis+medication+contraceptives+cigday_1+V8
group~f*Matsmk+g*Matagg+h*FamScore+i*EduPar+j*n_trauma+Age+int_dis+medication+contraceptives+cigday_1+V8+z*Epi
#direct
directMatsmk := f
directMatagg := g
directFamScore := h
directEduPar := i
directn_trauma := j
#indirect
EpiMatsmk := a*z
EpiMatagg := b*z
EpiFamScore := c*z
EpiEduPar := d*z
Epin_trauma := e*z
total := f + g + h + i + j + (a*z)+(b*z)+(c*z)+(d*z)+(e*z)
"
Netlist = list()
for (marker in EpiMarker) {
Dataset$Epi = Dataset[,marker]
Datasetscaled = Dataset %>% mutate_if(is.numeric, minmax_scaling)
Datasetscaled = Datasetscaled %>% mutate_if(is.factor, ~ as.numeric(.)-1)
Datasetscaled$group = ordered(as.factor(Datasetscaled$group))
fit<-sem(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")
cat("##Mediation Model ##\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
print(original_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$est)>0.1,]
DF = DF[DF$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$rhs,
width=abs(DF$est),
arrows ="to")
edges$dashes = F
edges$label = DF$est
edges$color=c("firebrick", "forestgreen")[1:2][factor(sign(DF$est), levels=c(-1,0,1),labels=c(1,2,2))]
edges$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"))
}Warning in lav_samplestats_step2(UNI = FIT, wt = wt, ov.names = ov.names, :
lavaan WARNING: correlation between variables group and Epi is (nearly) 1.0############################
############################
Epi_TopHit
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 132 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 26
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) -0.033 0.050 -0.657 0.511
Matagg (b) -0.014 0.072 -0.202 0.840
FamScore (c) 0.056 0.076 0.739 0.460
EduPar (d) -0.038 0.108 -0.358 0.721
n_trauma (e) 0.085 0.111 0.767 0.443
Age -0.090 0.097 -0.929 0.353
int_dis -0.074 0.057 -1.310 0.190
medication -0.044 0.059 -0.759 0.448
contrcptvs -0.017 0.049 -0.341 0.733
cigday_1 -0.111 0.136 -0.815 0.415
V8 -0.089 0.568 -0.156 0.876
group ~
Matsmk (f) 0.045 1.077 0.041 0.967
Matagg (g) 1.436 2.290 0.627 0.531
FamScore (h) 0.440 2.056 0.214 0.831
EduPar (i) -3.687 2.227 -1.655 0.098
n_trauma (j) 2.952 1.445 2.043 0.041
Age -3.468 2.438 -1.422 0.155
int_dis 0.802 0.808 0.992 0.321
medication 0.875 0.817 1.072 0.284
contrcptvs 0.031 0.843 0.037 0.970
cigday_1 9.678 7.094 1.364 0.172
V8 12.694 16.068 0.790 0.430
Epi (z) -6.500 0.589 -11.034 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.843 0.319 2.646 0.008
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 0.266 8.437 0.032 0.975
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.023 0.004 5.481 0.000
.group 0.014
Scales y*:
Estimate Std.Err z-value P(>|z|)
group 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk 0.045 1.077 0.041 0.967
directMatagg 1.436 2.290 0.627 0.531
directFamScore 0.440 2.056 0.214 0.831
directEduPar -3.687 2.227 -1.655 0.098
directn_trauma 2.952 1.445 2.043 0.041
EpiMatsmk 0.212 0.317 0.667 0.505
EpiMatagg 0.094 0.468 0.201 0.841
EpiFamScore -0.366 0.495 -0.741 0.459
EpiEduPar 0.250 0.705 0.354 0.723
Epin_trauma -0.552 0.722 -0.764 0.445
total 0.823 4.116 0.200 0.842
npar fmin
26.000 0.000
chisq df
0.000 0.000
pvalue chisq.scaled
NA 0.000
df.scaled pvalue.scaled
0.000 NA
chisq.scaling.factor baseline.chisq
NA 111.273
baseline.df baseline.pvalue
1.000 0.000
baseline.chisq.scaled baseline.df.scaled
111.273 1.000
baseline.pvalue.scaled baseline.chisq.scaling.factor
0.000 1.000
cfi tli
1.000 1.000
nnfi rfi
1.000 1.000
nfi pnfi
1.000 0.000
ifi rni
1.000 1.000
cfi.scaled tli.scaled
1.000 1.000
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
1.000 NA
rfi.scaled nfi.scaled
1.000 1.000
ifi.scaled rni.scaled
1.000 1.000
rni.robust rmsea
NA 0.000
rmsea.ci.lower rmsea.ci.upper
0.000 0.000
rmsea.pvalue rmsea.scaled
NA 0.000
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.000 0.000
rmsea.pvalue.scaled rmsea.robust
NA NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
0.000 0.000
rmsea.pvalue.robust rmr
NA 2.452
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
2.452 0.000
crmr crmr_nomean
3.165 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
NA NA
gfi agfi
1.000 1.000
pgfi mfi
0.000 1.000
lhs op rhs label
1 Epi ~ Matsmk a
2 Epi ~ Matagg b
3 Epi ~ FamScore c
4 Epi ~ EduPar d
5 Epi ~ n_trauma e
6 Epi ~ Age
7 Epi ~ int_dis
8 Epi ~ medication
9 Epi ~ contraceptives
10 Epi ~ cigday_1
11 Epi ~ V8
12 group ~ Matsmk f
13 group ~ Matagg g
14 group ~ FamScore h
15 group ~ EduPar i
16 group ~ n_trauma j
17 group ~ Age
18 group ~ int_dis
19 group ~ medication
20 group ~ contraceptives
21 group ~ cigday_1
22 group ~ V8
23 group ~ Epi z
24 group | t1
25 Epi ~~ Epi
26 group ~~ group
27 Matsmk ~~ Matsmk
28 Matsmk ~~ Matagg
29 Matsmk ~~ FamScore
30 Matsmk ~~ EduPar
31 Matsmk ~~ n_trauma
32 Matsmk ~~ Age
33 Matsmk ~~ int_dis
34 Matsmk ~~ medication
35 Matsmk ~~ contraceptives
36 Matsmk ~~ cigday_1
37 Matsmk ~~ V8
38 Matagg ~~ Matagg
39 Matagg ~~ FamScore
40 Matagg ~~ EduPar
41 Matagg ~~ n_trauma
42 Matagg ~~ Age
43 Matagg ~~ int_dis
44 Matagg ~~ medication
45 Matagg ~~ contraceptives
46 Matagg ~~ cigday_1
47 Matagg ~~ V8
48 FamScore ~~ FamScore
49 FamScore ~~ EduPar
50 FamScore ~~ n_trauma
51 FamScore ~~ Age
52 FamScore ~~ int_dis
53 FamScore ~~ medication
54 FamScore ~~ contraceptives
55 FamScore ~~ cigday_1
56 FamScore ~~ V8
57 EduPar ~~ EduPar
58 EduPar ~~ n_trauma
59 EduPar ~~ Age
60 EduPar ~~ int_dis
61 EduPar ~~ medication
62 EduPar ~~ contraceptives
63 EduPar ~~ cigday_1
64 EduPar ~~ V8
65 n_trauma ~~ n_trauma
66 n_trauma ~~ Age
67 n_trauma ~~ int_dis
68 n_trauma ~~ medication
69 n_trauma ~~ contraceptives
70 n_trauma ~~ cigday_1
71 n_trauma ~~ V8
72 Age ~~ Age
73 Age ~~ int_dis
74 Age ~~ medication
75 Age ~~ contraceptives
76 Age ~~ cigday_1
77 Age ~~ V8
78 int_dis ~~ int_dis
79 int_dis ~~ medication
80 int_dis ~~ contraceptives
81 int_dis ~~ cigday_1
82 int_dis ~~ V8
83 medication ~~ medication
84 medication ~~ contraceptives
85 medication ~~ cigday_1
86 medication ~~ V8
87 contraceptives ~~ contraceptives
88 contraceptives ~~ cigday_1
89 contraceptives ~~ V8
90 cigday_1 ~~ cigday_1
91 cigday_1 ~~ V8
92 V8 ~~ V8
93 group ~*~ group
94 Epi ~1
95 group ~1
96 Matsmk ~1
97 Matagg ~1
98 FamScore ~1
99 EduPar ~1
100 n_trauma ~1
101 Age ~1
102 int_dis ~1
103 medication ~1
104 contraceptives ~1
105 cigday_1 ~1
106 V8 ~1
107 directMatsmk := f directMatsmk
108 directMatagg := g directMatagg
109 directFamScore := h directFamScore
110 directEduPar := i directEduPar
111 directn_trauma := j directn_trauma
112 EpiMatsmk := a*z EpiMatsmk
113 EpiMatagg := b*z EpiMatagg
114 EpiFamScore := c*z EpiFamScore
115 EpiEduPar := d*z EpiEduPar
116 Epin_trauma := e*z Epin_trauma
117 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 -0.033 0.050 -0.657 0.511 -0.130 0.065
2 -0.014 0.072 -0.202 0.840 -0.155 0.126
3 0.056 0.076 0.739 0.460 -0.093 0.206
4 -0.038 0.108 -0.358 0.721 -0.249 0.172
5 0.085 0.111 0.767 0.443 -0.132 0.302
6 -0.090 0.097 -0.929 0.353 -0.280 0.100
7 -0.074 0.057 -1.310 0.190 -0.186 0.037
8 -0.044 0.059 -0.759 0.448 -0.159 0.070
9 -0.017 0.049 -0.341 0.733 -0.113 0.080
10 -0.111 0.136 -0.815 0.415 -0.378 0.156
11 -0.089 0.568 -0.156 0.876 -1.202 1.025
12 0.045 1.077 0.041 0.967 -2.066 2.155
13 1.436 2.290 0.627 0.531 -3.053 5.924
14 0.440 2.056 0.214 0.831 -3.590 4.471
15 -3.687 2.227 -1.655 0.098 -8.052 0.678
16 2.952 1.445 2.043 0.041 0.119 5.784
17 -3.468 2.438 -1.422 0.155 -8.247 1.310
18 0.802 0.808 0.992 0.321 -0.782 2.387
19 0.875 0.817 1.072 0.284 -0.725 2.475
20 0.031 0.843 0.037 0.970 -1.621 1.683
21 9.678 7.094 1.364 0.172 -4.225 23.582
22 12.694 16.068 0.790 0.430 -18.800 44.187
23 -6.500 0.589 -11.034 0.000 -7.654 -5.345
24 0.266 8.437 0.032 0.975 -16.271 16.803
25 0.023 0.004 5.481 0.000 0.015 0.032
26 0.014 0.000 NA NA 0.014 0.014
27 0.196 0.000 NA NA 0.196 0.196
28 0.049 0.000 NA NA 0.049 0.049
29 -0.009 0.000 NA NA -0.009 -0.009
30 -0.006 0.000 NA NA -0.006 -0.006
31 0.007 0.000 NA NA 0.007 0.007
32 -0.003 0.000 NA NA -0.003 -0.003
33 0.022 0.000 NA NA 0.022 0.022
34 0.004 0.000 NA NA 0.004 0.004
35 0.022 0.000 NA NA 0.022 0.022
36 0.017 0.000 NA NA 0.017 0.017
37 0.003 0.000 NA NA 0.003 0.003
38 0.091 0.000 NA NA 0.091 0.091
39 0.034 0.000 NA NA 0.034 0.034
40 -0.017 0.000 NA NA -0.017 -0.017
41 0.007 0.000 NA NA 0.007 0.007
42 0.000 0.000 NA NA 0.000 0.000
43 0.033 0.000 NA NA 0.033 0.033
44 0.008 0.000 NA NA 0.008 0.008
45 0.008 0.000 NA NA 0.008 0.008
46 0.010 0.000 NA NA 0.010 0.010
47 0.001 0.000 NA NA 0.001 0.001
48 0.132 0.000 NA NA 0.132 0.132
49 -0.029 0.000 NA NA -0.029 -0.029
50 0.027 0.000 NA NA 0.027 0.027
51 0.004 0.000 NA NA 0.004 0.004
52 0.065 0.000 NA NA 0.065 0.065
53 0.004 0.000 NA NA 0.004 0.004
54 0.058 0.000 NA NA 0.058 0.058
55 0.044 0.000 NA NA 0.044 0.044
56 0.001 0.000 NA NA 0.001 0.001
57 0.054 0.000 NA NA 0.054 0.054
58 -0.008 0.000 NA NA -0.008 -0.008
59 0.003 0.000 NA NA 0.003 0.003
60 -0.019 0.000 NA NA -0.019 -0.019
61 0.010 0.000 NA NA 0.010 0.010
62 -0.013 0.000 NA NA -0.013 -0.013
63 -0.015 0.000 NA NA -0.015 -0.015
64 0.000 0.000 NA NA 0.000 0.000
65 0.051 0.000 NA NA 0.051 0.051
66 0.002 0.000 NA NA 0.002 0.002
67 0.042 0.000 NA NA 0.042 0.042
68 0.018 0.000 NA NA 0.018 0.018
69 0.018 0.000 NA NA 0.018 0.018
70 0.021 0.000 NA NA 0.021 0.021
71 0.000 0.000 NA NA 0.000 0.000
72 0.047 0.000 NA NA 0.047 0.047
73 0.008 0.000 NA NA 0.008 0.008
74 -0.002 0.000 NA NA -0.002 -0.002
75 0.035 0.000 NA NA 0.035 0.035
76 0.009 0.000 NA NA 0.009 0.009
77 -0.001 0.000 NA NA -0.001 -0.001
78 0.213 0.000 NA NA 0.213 0.213
79 0.061 0.000 NA NA 0.061 0.061
80 0.061 0.000 NA NA 0.061 0.061
81 0.044 0.000 NA NA 0.044 0.044
82 0.006 0.000 NA NA 0.006 0.006
83 0.146 0.000 NA NA 0.146 0.146
84 0.035 0.000 NA NA 0.035 0.035
85 0.003 0.000 NA NA 0.003 0.003
86 0.002 0.000 NA NA 0.002 0.002
87 0.213 0.000 NA NA 0.213 0.213
88 0.049 0.000 NA NA 0.049 0.049
89 0.003 0.000 NA NA 0.003 0.003
90 0.062 0.000 NA NA 0.062 0.062
91 0.002 0.000 NA NA 0.002 0.002
92 0.005 0.000 NA NA 0.005 0.005
93 1.000 0.000 NA NA 1.000 1.000
94 0.843 0.319 2.646 0.008 0.219 1.468
95 0.000 0.000 NA NA 0.000 0.000
96 0.262 0.000 NA NA 0.262 0.262
97 0.100 0.000 NA NA 0.100 0.100
98 0.225 0.000 NA NA 0.225 0.225
99 0.606 0.000 NA NA 0.606 0.606
100 0.196 0.000 NA NA 0.196 0.196
101 0.562 0.000 NA NA 0.562 0.562
102 0.300 0.000 NA NA 0.300 0.300
103 0.175 0.000 NA NA 0.175 0.175
104 0.300 0.000 NA NA 0.300 0.300
105 0.124 0.000 NA NA 0.124 0.124
106 0.529 0.000 NA NA 0.529 0.529
107 0.045 1.077 0.041 0.967 -2.066 2.155
108 1.436 2.290 0.627 0.531 -3.053 5.924
109 0.440 2.056 0.214 0.831 -3.590 4.471
110 -3.687 2.227 -1.655 0.098 -8.052 0.678
111 2.952 1.445 2.043 0.041 0.119 5.784
112 0.212 0.317 0.667 0.505 -0.410 0.834
113 0.094 0.468 0.201 0.841 -0.822 1.010
114 -0.366 0.495 -0.741 0.459 -1.336 0.603
115 0.250 0.705 0.354 0.723 -1.133 1.632
116 -0.552 0.722 -0.764 0.445 -1.966 0.863
117 0.823 4.116 0.200 0.842 -7.244 8.889
label lhs edge rhs est std group
1 a Matsmk ~> Epi -3.256589e-02 -0.0872133419
2 b Matagg ~> Epi -1.446988e-02 -0.0264216870
3 c FamScore ~> Epi 5.638498e-02 0.1240368997
4 d EduPar ~> Epi -3.844427e-02 -0.0539309025
5 e n_trauma ~> Epi 8.486733e-02 0.1163122416
6 Age ~> Epi -9.011773e-02 -0.1187813154
7 int_dis ~> Epi -7.447596e-02 -0.2077304276
8 medication ~> Epi -4.445645e-02 -0.1028146778
9 contraceptives ~> Epi -1.677936e-02 -0.0468014586
10 cigday_1 ~> Epi -1.109172e-01 -0.1663967836
11 V8 ~> Epi -8.888125e-02 -0.0366069688
12 f Matsmk ~> group 4.459322e-02 0.0049084093
13 g Matagg ~> group 1.435869e+00 0.1077612087
14 h FamScore ~> group 4.400936e-01 0.0397909584
15 i EduPar ~> group -3.687103e+00 -0.2125902008
16 j n_trauma ~> group 2.951804e+00 0.1662739924
17 Age ~> group -3.468157e+00 -0.1878834534
18 int_dis ~> group 8.022992e-01 0.0919755210
19 medication ~> group 8.750760e-01 0.0831798311
20 contraceptives ~> group 3.125001e-02 0.0035824991
21 cigday_1 ~> group 9.678494e+00 0.5967683249
22 V8 ~> group 1.269350e+01 0.2148755047
23 z Epi ~> group -6.499591e+00 -0.2671393575
25 Epi <-> Epi 2.334009e-02 0.8538634661
26 group <-> group 1.400524e-02 0.0008655263
27 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
28 Matsmk <-> Matagg 4.936709e-02 0.3693241433
29 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
30 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
31 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
32 Matsmk <-> Age -3.217300e-03 -0.0333441329
33 Matsmk <-> int_dis 2.151899e-02 0.1053910232
34 Matsmk <-> medication 4.113924e-03 0.0242997446
35 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
36 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
37 Matsmk <-> V8 2.928139e-03 0.0971189320
38 Matagg <-> Matagg 9.113924e-02 1.0000000000
39 Matagg <-> FamScore 3.417722e-02 0.3112715087
40 Matagg <-> EduPar -1.656118e-02 -0.2365241196
41 Matagg <-> n_trauma 7.233273e-03 0.1057402114
42 Matagg <-> Age 3.118694e-04 0.0047405101
43 Matagg <-> int_dis 3.291139e-02 0.2364027144
44 Matagg <-> medication 7.594937e-03 0.0657951695
45 Matagg <-> contraceptives 7.594937e-03 0.0545544726
46 Matagg <-> cigday_1 1.018987e-02 0.1360858260
47 Matagg <-> V8 8.272067e-04 0.0402393217
48 FamScore <-> FamScore 1.322785e-01 1.0000000000
49 FamScore <-> EduPar -2.948312e-02 -0.3495149022
50 FamScore <-> n_trauma 2.667269e-02 0.3236534989
51 FamScore <-> Age 3.636947e-03 0.0458878230
52 FamScore <-> int_dis 6.455696e-02 0.3849084009
53 FamScore <-> medication 4.430380e-03 0.0318580293
54 FamScore <-> contraceptives 5.822785e-02 0.3471722832
55 FamScore <-> cigday_1 4.381329e-02 0.4856887960
56 FamScore <-> V8 7.814844e-04 0.0315547719
57 EduPar <-> EduPar 5.379307e-02 1.0000000000
58 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
59 EduPar <-> Age 2.762108e-03 0.0546490350
60 EduPar <-> int_dis -1.909283e-02 -0.1785114035
61 EduPar <-> medication 1.017932e-02 0.1147832062
62 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
63 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
64 EduPar <-> V8 -8.860887e-06 -0.0005610523
65 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
66 n_trauma <-> Age 1.582278e-03 0.0320439451
67 n_trauma <-> int_dis 4.159132e-02 0.3980335009
68 n_trauma <-> medication 1.763110e-02 0.2034979577
69 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
70 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
71 n_trauma <-> V8 -4.694340e-04 -0.0304243917
72 Age <-> Age 4.748866e-02 1.0000000000
73 Age <-> int_dis 8.090259e-03 0.0805056484
74 Age <-> medication -1.655660e-03 -0.0198700345
75 Age <-> contraceptives 3.524124e-02 0.3506833348
76 Age <-> cigday_1 8.542355e-03 0.1580445206
77 Age <-> V8 -1.333633e-03 -0.0898732659
78 int_dis <-> int_dis 2.126582e-01 1.0000000000
79 int_dis <-> medication 6.075949e-02 0.3445843938
80 int_dis <-> contraceptives 6.075949e-02 0.2857142857
81 int_dis <-> cigday_1 4.449367e-02 0.3890038953
82 int_dis <-> V8 5.645344e-03 0.1797788722
83 medication <-> medication 1.462025e-01 1.0000000000
84 medication <-> contraceptives 3.544304e-02 0.2010075631
85 medication <-> cigday_1 3.275316e-03 0.0345360471
86 medication <-> V8 2.084232e-03 0.0800493604
87 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
88 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
89 contraceptives <-> V8 2.688833e-03 0.0856272484
90 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
91 cigday_1 <-> V8 1.509276e-03 0.0893623867
92 V8 <-> V8 4.636832e-03 1.0000000000
93 group <-> group 1.000000e+00 1.0000000000
94 int Epi 8.434318e-01 5.1014410172
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 2.625000e-01 0.5928600601
97 int Matagg 1.000000e-01 0.3312434486
98 int FamScore 2.250000e-01 0.6186398880
99 int EduPar 6.062500e-01 2.6138976225
100 int n_trauma 1.964286e-01 0.8668873691
101 int Age 5.621377e-01 2.5795724974
102 int int_dis 3.000000e-01 0.6505492185
103 int medication 1.750000e-01 0.4576785957
104 int contraceptives 3.000000e-01 0.6505492185
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 FALSE 1
2 FALSE 2
3 FALSE 3
4 FALSE 4
5 FALSE 5
6 FALSE 6
7 FALSE 7
8 FALSE 8
9 FALSE 9
10 FALSE 10
11 FALSE 11
12 FALSE 12
13 FALSE 13
14 FALSE 14
15 FALSE 15
16 FALSE 16
17 FALSE 17
18 FALSE 18
19 FALSE 19
20 FALSE 20
21 FALSE 21
22 FALSE 22
23 FALSE 23
25 FALSE 25
26 TRUE 0
27 TRUE 0
28 TRUE 0
29 TRUE 0
30 TRUE 0
31 TRUE 0
32 TRUE 0
33 TRUE 0
34 TRUE 0
35 TRUE 0
36 TRUE 0
37 TRUE 0
38 TRUE 0
39 TRUE 0
40 TRUE 0
41 TRUE 0
42 TRUE 0
43 TRUE 0
44 TRUE 0
45 TRUE 0
46 TRUE 0
47 TRUE 0
48 TRUE 0
49 TRUE 0
50 TRUE 0
51 TRUE 0
52 TRUE 0
53 TRUE 0
54 TRUE 0
55 TRUE 0
56 TRUE 0
57 TRUE 0
58 TRUE 0
59 TRUE 0
60 TRUE 0
61 TRUE 0
62 TRUE 0
63 TRUE 0
64 TRUE 0
65 TRUE 0
66 TRUE 0
67 TRUE 0
68 TRUE 0
69 TRUE 0
70 TRUE 0
71 TRUE 0
72 TRUE 0
73 TRUE 0
74 TRUE 0
75 TRUE 0
76 TRUE 0
77 TRUE 0
78 TRUE 0
79 TRUE 0
80 TRUE 0
81 TRUE 0
82 TRUE 0
83 TRUE 0
84 TRUE 0
85 TRUE 0
86 TRUE 0
87 TRUE 0
88 TRUE 0
89 TRUE 0
90 TRUE 0
91 TRUE 0
92 TRUE 0
93 TRUE 0
94 FALSE 26
95 TRUE 0
96 TRUE 0
97 TRUE 0
98 TRUE 0
99 TRUE 0
100 TRUE 0
101 TRUE 0
102 TRUE 0
103 TRUE 0
104 TRUE 0
105 TRUE 0
106 TRUE 0Warning in lav_samplestats_step2(UNI = FIT, wt = wt, ov.names = ov.names, :
lavaan WARNING: correlation between variables group and Epi is (nearly) 1.0############################
############################
Epi_all
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 138 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 26
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) 0.014 0.025 0.573 0.567
Matagg (b) 0.023 0.038 0.590 0.555
FamScore (c) -0.037 0.042 -0.871 0.384
EduPar (d) -0.090 0.060 -1.514 0.130
n_trauma (e) 0.025 0.048 0.523 0.601
Age 0.034 0.064 0.534 0.593
int_dis 0.023 0.024 0.934 0.350
medication 0.007 0.030 0.248 0.804
contrcptvs -0.016 0.027 -0.583 0.560
cigday_1 0.022 0.052 0.425 0.671
V8 0.060 0.306 0.197 0.844
group ~
Matsmk (f) 0.078 1.107 0.071 0.944
Matagg (g) 1.249 2.383 0.524 0.600
FamScore (h) 0.526 1.914 0.275 0.783
EduPar (i) -2.316 2.797 -0.828 0.408
n_trauma (j) 2.090 1.387 1.506 0.132
Age -3.306 1.889 -1.750 0.080
int_dis 1.003 0.828 1.212 0.226
medication 1.073 0.724 1.482 0.138
contrcptvs 0.336 0.720 0.466 0.641
cigday_1 10.127 7.243 1.398 0.162
V8 12.524 14.939 0.838 0.402
Epi (z) 12.386 20.689 0.599 0.549
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.134 0.171 0.784 0.433
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 7.408 8.008 0.925 0.355
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.006 0.001 5.129 0.000
.group 0.010
Scales y*:
Estimate Std.Err z-value P(>|z|)
group 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk 0.078 1.107 0.071 0.944
directMatagg 1.249 2.383 0.524 0.600
directFamScore 0.526 1.914 0.275 0.783
directEduPar -2.316 2.797 -0.828 0.408
directn_trauma 2.090 1.387 1.506 0.132
EpiMatsmk 0.178 0.432 0.412 0.680
EpiMatagg 0.281 0.668 0.420 0.674
EpiFamScore -0.452 0.917 -0.493 0.622
EpiEduPar -1.121 2.019 -0.555 0.579
Epin_trauma 0.311 0.788 0.394 0.693
total 0.823 4.116 0.200 0.842
npar fmin
26.000 0.000
chisq df
0.000 0.000
pvalue chisq.scaled
NA 0.000
df.scaled pvalue.scaled
0.000 NA
chisq.scaling.factor baseline.chisq
NA 0.358
baseline.df baseline.pvalue
1.000 0.549
baseline.chisq.scaled baseline.df.scaled
0.358 1.000
baseline.pvalue.scaled baseline.chisq.scaling.factor
0.549 1.000
cfi tli
1.000 1.000
nnfi rfi
1.000 1.000
nfi pnfi
1.000 0.000
ifi rni
1.000 1.000
cfi.scaled tli.scaled
1.000 1.000
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
1.000 NA
rfi.scaled nfi.scaled
1.000 1.000
ifi.scaled rni.scaled
1.000 1.000
rni.robust rmsea
NA 0.000
rmsea.ci.lower rmsea.ci.upper
0.000 0.000
rmsea.pvalue rmsea.scaled
NA 0.000
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.000 0.000
rmsea.pvalue.scaled rmsea.robust
NA NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
0.000 0.000
rmsea.pvalue.robust rmr
NA 0.742
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
0.742 0.000
crmr crmr_nomean
0.959 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
1.000 1.000
gfi agfi
1.000 1.000
pgfi mfi
0.000 1.000
lhs op rhs label
1 Epi ~ Matsmk a
2 Epi ~ Matagg b
3 Epi ~ FamScore c
4 Epi ~ EduPar d
5 Epi ~ n_trauma e
6 Epi ~ Age
7 Epi ~ int_dis
8 Epi ~ medication
9 Epi ~ contraceptives
10 Epi ~ cigday_1
11 Epi ~ V8
12 group ~ Matsmk f
13 group ~ Matagg g
14 group ~ FamScore h
15 group ~ EduPar i
16 group ~ n_trauma j
17 group ~ Age
18 group ~ int_dis
19 group ~ medication
20 group ~ contraceptives
21 group ~ cigday_1
22 group ~ V8
23 group ~ Epi z
24 group | t1
25 Epi ~~ Epi
26 group ~~ group
27 Matsmk ~~ Matsmk
28 Matsmk ~~ Matagg
29 Matsmk ~~ FamScore
30 Matsmk ~~ EduPar
31 Matsmk ~~ n_trauma
32 Matsmk ~~ Age
33 Matsmk ~~ int_dis
34 Matsmk ~~ medication
35 Matsmk ~~ contraceptives
36 Matsmk ~~ cigday_1
37 Matsmk ~~ V8
38 Matagg ~~ Matagg
39 Matagg ~~ FamScore
40 Matagg ~~ EduPar
41 Matagg ~~ n_trauma
42 Matagg ~~ Age
43 Matagg ~~ int_dis
44 Matagg ~~ medication
45 Matagg ~~ contraceptives
46 Matagg ~~ cigday_1
47 Matagg ~~ V8
48 FamScore ~~ FamScore
49 FamScore ~~ EduPar
50 FamScore ~~ n_trauma
51 FamScore ~~ Age
52 FamScore ~~ int_dis
53 FamScore ~~ medication
54 FamScore ~~ contraceptives
55 FamScore ~~ cigday_1
56 FamScore ~~ V8
57 EduPar ~~ EduPar
58 EduPar ~~ n_trauma
59 EduPar ~~ Age
60 EduPar ~~ int_dis
61 EduPar ~~ medication
62 EduPar ~~ contraceptives
63 EduPar ~~ cigday_1
64 EduPar ~~ V8
65 n_trauma ~~ n_trauma
66 n_trauma ~~ Age
67 n_trauma ~~ int_dis
68 n_trauma ~~ medication
69 n_trauma ~~ contraceptives
70 n_trauma ~~ cigday_1
71 n_trauma ~~ V8
72 Age ~~ Age
73 Age ~~ int_dis
74 Age ~~ medication
75 Age ~~ contraceptives
76 Age ~~ cigday_1
77 Age ~~ V8
78 int_dis ~~ int_dis
79 int_dis ~~ medication
80 int_dis ~~ contraceptives
81 int_dis ~~ cigday_1
82 int_dis ~~ V8
83 medication ~~ medication
84 medication ~~ contraceptives
85 medication ~~ cigday_1
86 medication ~~ V8
87 contraceptives ~~ contraceptives
88 contraceptives ~~ cigday_1
89 contraceptives ~~ V8
90 cigday_1 ~~ cigday_1
91 cigday_1 ~~ V8
92 V8 ~~ V8
93 group ~*~ group
94 Epi ~1
95 group ~1
96 Matsmk ~1
97 Matagg ~1
98 FamScore ~1
99 EduPar ~1
100 n_trauma ~1
101 Age ~1
102 int_dis ~1
103 medication ~1
104 contraceptives ~1
105 cigday_1 ~1
106 V8 ~1
107 directMatsmk := f directMatsmk
108 directMatagg := g directMatagg
109 directFamScore := h directFamScore
110 directEduPar := i directEduPar
111 directn_trauma := j directn_trauma
112 EpiMatsmk := a*z EpiMatsmk
113 EpiMatagg := b*z EpiMatagg
114 EpiFamScore := c*z EpiFamScore
115 EpiEduPar := d*z EpiEduPar
116 Epin_trauma := e*z Epin_trauma
117 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 0.014 0.025 0.573 0.567 -0.035 0.064
2 0.023 0.038 0.590 0.555 -0.053 0.098
3 -0.037 0.042 -0.871 0.384 -0.119 0.046
4 -0.090 0.060 -1.514 0.130 -0.208 0.027
5 0.025 0.048 0.523 0.601 -0.069 0.119
6 0.034 0.064 0.534 0.593 -0.091 0.160
7 0.023 0.024 0.934 0.350 -0.025 0.071
8 0.007 0.030 0.248 0.804 -0.051 0.066
9 -0.016 0.027 -0.583 0.560 -0.069 0.037
10 0.022 0.052 0.425 0.671 -0.079 0.123
11 0.060 0.306 0.197 0.844 -0.540 0.660
12 0.078 1.107 0.071 0.944 -2.092 2.248
13 1.249 2.383 0.524 0.600 -3.421 5.919
14 0.526 1.914 0.275 0.783 -3.226 4.278
15 -2.316 2.797 -0.828 0.408 -7.798 3.165
16 2.090 1.387 1.506 0.132 -0.629 4.808
17 -3.306 1.889 -1.750 0.080 -7.007 0.396
18 1.003 0.828 1.212 0.226 -0.619 2.625
19 1.073 0.724 1.482 0.138 -0.346 2.492
20 0.336 0.720 0.466 0.641 -1.076 1.748
21 10.127 7.243 1.398 0.162 -4.070 24.323
22 12.524 14.939 0.838 0.402 -16.756 41.805
23 12.386 20.689 0.599 0.549 -28.163 52.936
24 7.408 8.008 0.925 0.355 -8.288 23.104
25 0.006 0.001 5.129 0.000 0.004 0.009
26 0.010 0.000 NA NA 0.010 0.010
27 0.196 0.000 NA NA 0.196 0.196
28 0.049 0.000 NA NA 0.049 0.049
29 -0.009 0.000 NA NA -0.009 -0.009
30 -0.006 0.000 NA NA -0.006 -0.006
31 0.007 0.000 NA NA 0.007 0.007
32 -0.003 0.000 NA NA -0.003 -0.003
33 0.022 0.000 NA NA 0.022 0.022
34 0.004 0.000 NA NA 0.004 0.004
35 0.022 0.000 NA NA 0.022 0.022
36 0.017 0.000 NA NA 0.017 0.017
37 0.003 0.000 NA NA 0.003 0.003
38 0.091 0.000 NA NA 0.091 0.091
39 0.034 0.000 NA NA 0.034 0.034
40 -0.017 0.000 NA NA -0.017 -0.017
41 0.007 0.000 NA NA 0.007 0.007
42 0.000 0.000 NA NA 0.000 0.000
43 0.033 0.000 NA NA 0.033 0.033
44 0.008 0.000 NA NA 0.008 0.008
45 0.008 0.000 NA NA 0.008 0.008
46 0.010 0.000 NA NA 0.010 0.010
47 0.001 0.000 NA NA 0.001 0.001
48 0.132 0.000 NA NA 0.132 0.132
49 -0.029 0.000 NA NA -0.029 -0.029
50 0.027 0.000 NA NA 0.027 0.027
51 0.004 0.000 NA NA 0.004 0.004
52 0.065 0.000 NA NA 0.065 0.065
53 0.004 0.000 NA NA 0.004 0.004
54 0.058 0.000 NA NA 0.058 0.058
55 0.044 0.000 NA NA 0.044 0.044
56 0.001 0.000 NA NA 0.001 0.001
57 0.054 0.000 NA NA 0.054 0.054
58 -0.008 0.000 NA NA -0.008 -0.008
59 0.003 0.000 NA NA 0.003 0.003
60 -0.019 0.000 NA NA -0.019 -0.019
61 0.010 0.000 NA NA 0.010 0.010
62 -0.013 0.000 NA NA -0.013 -0.013
63 -0.015 0.000 NA NA -0.015 -0.015
64 0.000 0.000 NA NA 0.000 0.000
65 0.051 0.000 NA NA 0.051 0.051
66 0.002 0.000 NA NA 0.002 0.002
67 0.042 0.000 NA NA 0.042 0.042
68 0.018 0.000 NA NA 0.018 0.018
69 0.018 0.000 NA NA 0.018 0.018
70 0.021 0.000 NA NA 0.021 0.021
71 0.000 0.000 NA NA 0.000 0.000
72 0.047 0.000 NA NA 0.047 0.047
73 0.008 0.000 NA NA 0.008 0.008
74 -0.002 0.000 NA NA -0.002 -0.002
75 0.035 0.000 NA NA 0.035 0.035
76 0.009 0.000 NA NA 0.009 0.009
77 -0.001 0.000 NA NA -0.001 -0.001
78 0.213 0.000 NA NA 0.213 0.213
79 0.061 0.000 NA NA 0.061 0.061
80 0.061 0.000 NA NA 0.061 0.061
81 0.044 0.000 NA NA 0.044 0.044
82 0.006 0.000 NA NA 0.006 0.006
83 0.146 0.000 NA NA 0.146 0.146
84 0.035 0.000 NA NA 0.035 0.035
85 0.003 0.000 NA NA 0.003 0.003
86 0.002 0.000 NA NA 0.002 0.002
87 0.213 0.000 NA NA 0.213 0.213
88 0.049 0.000 NA NA 0.049 0.049
89 0.003 0.000 NA NA 0.003 0.003
90 0.062 0.000 NA NA 0.062 0.062
91 0.002 0.000 NA NA 0.002 0.002
92 0.005 0.000 NA NA 0.005 0.005
93 1.000 0.000 NA NA 1.000 1.000
94 0.134 0.171 0.784 0.433 -0.201 0.469
95 0.000 0.000 NA NA 0.000 0.000
96 0.262 0.000 NA NA 0.262 0.262
97 0.100 0.000 NA NA 0.100 0.100
98 0.225 0.000 NA NA 0.225 0.225
99 0.606 0.000 NA NA 0.606 0.606
100 0.196 0.000 NA NA 0.196 0.196
101 0.562 0.000 NA NA 0.562 0.562
102 0.300 0.000 NA NA 0.300 0.300
103 0.175 0.000 NA NA 0.175 0.175
104 0.300 0.000 NA NA 0.300 0.300
105 0.124 0.000 NA NA 0.124 0.124
106 0.529 0.000 NA NA 0.529 0.529
107 0.078 1.107 0.071 0.944 -2.092 2.248
108 1.249 2.383 0.524 0.600 -3.421 5.919
109 0.526 1.914 0.275 0.783 -3.226 4.278
110 -2.316 2.797 -0.828 0.408 -7.798 3.165
111 2.090 1.387 1.506 0.132 -0.629 4.808
112 0.178 0.432 0.412 0.680 -0.669 1.025
113 0.281 0.668 0.420 0.674 -1.029 1.591
114 -0.452 0.917 -0.493 0.622 -2.251 1.346
115 -1.121 2.019 -0.555 0.579 -5.078 2.836
116 0.311 0.788 0.394 0.693 -1.233 1.855
117 0.823 4.116 0.200 0.842 -7.244 8.889
label lhs edge rhs est std group
1 a Matsmk ~> Epi 1.438673e-02 0.0741179628
2 b Matagg ~> Epi 2.267876e-02 0.0796629313
3 c FamScore ~> Epi -3.652791e-02 -0.1545801550
4 d EduPar ~> Epi -9.048835e-02 -0.2441969198
5 e n_trauma ~> Epi 2.508125e-02 0.0661264989
6 Age ~> Epi 3.417168e-02 0.0866454295
7 int_dis ~> Epi 2.287155e-02 0.1227216212
8 medication ~> Epi 7.364842e-03 0.0327661364
9 contraceptives ~> Epi -1.577718e-02 -0.0846554328
10 cigday_1 ~> Epi 2.200542e-02 0.0635063420
11 V8 ~> Epi 6.028764e-02 0.0477664897
12 f Matsmk ~> group 7.806065e-02 0.0085921942
13 g Matagg ~> group 1.249013e+00 0.0937377451
14 h FamScore ~> group 5.260579e-01 0.0475633998
15 i EduPar ~> group -2.316420e+00 -0.1335596716
16 j n_trauma ~> group 2.089539e+00 0.1177029208
17 Age ~> group -3.305688e+00 -0.1790818485
18 int_dis ~> group 1.003070e+00 0.1149918512
19 medication ~> group 1.072802e+00 0.1019745562
20 contraceptives ~> group 3.357290e-01 0.0384879458
21 cigday_1 ~> group 1.012685e+01 0.6244133457
22 V8 ~> group 1.252446e+01 0.2120138949
23 z Epi ~> group 1.238624e+01 0.2646366947
25 Epi <-> Epi 6.453078e-03 0.8736462116
26 group <-> group 9.975008e-03 0.0006164571
27 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
28 Matsmk <-> Matagg 4.936709e-02 0.3693241433
29 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
30 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
31 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
32 Matsmk <-> Age -3.217300e-03 -0.0333441329
33 Matsmk <-> int_dis 2.151899e-02 0.1053910232
34 Matsmk <-> medication 4.113924e-03 0.0242997446
35 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
36 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
37 Matsmk <-> V8 2.928139e-03 0.0971189320
38 Matagg <-> Matagg 9.113924e-02 1.0000000000
39 Matagg <-> FamScore 3.417722e-02 0.3112715087
40 Matagg <-> EduPar -1.656118e-02 -0.2365241196
41 Matagg <-> n_trauma 7.233273e-03 0.1057402114
42 Matagg <-> Age 3.118694e-04 0.0047405101
43 Matagg <-> int_dis 3.291139e-02 0.2364027144
44 Matagg <-> medication 7.594937e-03 0.0657951695
45 Matagg <-> contraceptives 7.594937e-03 0.0545544726
46 Matagg <-> cigday_1 1.018987e-02 0.1360858260
47 Matagg <-> V8 8.272067e-04 0.0402393217
48 FamScore <-> FamScore 1.322785e-01 1.0000000000
49 FamScore <-> EduPar -2.948312e-02 -0.3495149022
50 FamScore <-> n_trauma 2.667269e-02 0.3236534989
51 FamScore <-> Age 3.636947e-03 0.0458878230
52 FamScore <-> int_dis 6.455696e-02 0.3849084009
53 FamScore <-> medication 4.430380e-03 0.0318580293
54 FamScore <-> contraceptives 5.822785e-02 0.3471722832
55 FamScore <-> cigday_1 4.381329e-02 0.4856887960
56 FamScore <-> V8 7.814844e-04 0.0315547719
57 EduPar <-> EduPar 5.379307e-02 1.0000000000
58 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
59 EduPar <-> Age 2.762108e-03 0.0546490350
60 EduPar <-> int_dis -1.909283e-02 -0.1785114035
61 EduPar <-> medication 1.017932e-02 0.1147832062
62 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
63 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
64 EduPar <-> V8 -8.860887e-06 -0.0005610523
65 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
66 n_trauma <-> Age 1.582278e-03 0.0320439451
67 n_trauma <-> int_dis 4.159132e-02 0.3980335009
68 n_trauma <-> medication 1.763110e-02 0.2034979577
69 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
70 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
71 n_trauma <-> V8 -4.694340e-04 -0.0304243917
72 Age <-> Age 4.748866e-02 1.0000000000
73 Age <-> int_dis 8.090259e-03 0.0805056484
74 Age <-> medication -1.655660e-03 -0.0198700345
75 Age <-> contraceptives 3.524124e-02 0.3506833348
76 Age <-> cigday_1 8.542355e-03 0.1580445206
77 Age <-> V8 -1.333633e-03 -0.0898732659
78 int_dis <-> int_dis 2.126582e-01 1.0000000000
79 int_dis <-> medication 6.075949e-02 0.3445843938
80 int_dis <-> contraceptives 6.075949e-02 0.2857142857
81 int_dis <-> cigday_1 4.449367e-02 0.3890038953
82 int_dis <-> V8 5.645344e-03 0.1797788722
83 medication <-> medication 1.462025e-01 1.0000000000
84 medication <-> contraceptives 3.544304e-02 0.2010075631
85 medication <-> cigday_1 3.275316e-03 0.0345360471
86 medication <-> V8 2.084232e-03 0.0800493604
87 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
88 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
89 contraceptives <-> V8 2.688833e-03 0.0856272484
90 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
91 cigday_1 <-> V8 1.509276e-03 0.0893623867
92 V8 <-> V8 4.636832e-03 1.0000000000
93 group <-> group 1.000000e+00 1.0000000000
94 int Epi 1.340350e-01 1.5595617056
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 2.625000e-01 0.5928600601
97 int Matagg 1.000000e-01 0.3312434486
98 int FamScore 2.250000e-01 0.6186398880
99 int EduPar 6.062500e-01 2.6138976225
100 int n_trauma 1.964286e-01 0.8668873691
101 int Age 5.621377e-01 2.5795724974
102 int int_dis 3.000000e-01 0.6505492185
103 int medication 1.750000e-01 0.4576785957
104 int contraceptives 3.000000e-01 0.6505492185
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 FALSE 1
2 FALSE 2
3 FALSE 3
4 FALSE 4
5 FALSE 5
6 FALSE 6
7 FALSE 7
8 FALSE 8
9 FALSE 9
10 FALSE 10
11 FALSE 11
12 FALSE 12
13 FALSE 13
14 FALSE 14
15 FALSE 15
16 FALSE 16
17 FALSE 17
18 FALSE 18
19 FALSE 19
20 FALSE 20
21 FALSE 21
22 FALSE 22
23 FALSE 23
25 FALSE 25
26 TRUE 0
27 TRUE 0
28 TRUE 0
29 TRUE 0
30 TRUE 0
31 TRUE 0
32 TRUE 0
33 TRUE 0
34 TRUE 0
35 TRUE 0
36 TRUE 0
37 TRUE 0
38 TRUE 0
39 TRUE 0
40 TRUE 0
41 TRUE 0
42 TRUE 0
43 TRUE 0
44 TRUE 0
45 TRUE 0
46 TRUE 0
47 TRUE 0
48 TRUE 0
49 TRUE 0
50 TRUE 0
51 TRUE 0
52 TRUE 0
53 TRUE 0
54 TRUE 0
55 TRUE 0
56 TRUE 0
57 TRUE 0
58 TRUE 0
59 TRUE 0
60 TRUE 0
61 TRUE 0
62 TRUE 0
63 TRUE 0
64 TRUE 0
65 TRUE 0
66 TRUE 0
67 TRUE 0
68 TRUE 0
69 TRUE 0
70 TRUE 0
71 TRUE 0
72 TRUE 0
73 TRUE 0
74 TRUE 0
75 TRUE 0
76 TRUE 0
77 TRUE 0
78 TRUE 0
79 TRUE 0
80 TRUE 0
81 TRUE 0
82 TRUE 0
83 TRUE 0
84 TRUE 0
85 TRUE 0
86 TRUE 0
87 TRUE 0
88 TRUE 0
89 TRUE 0
90 TRUE 0
91 TRUE 0
92 TRUE 0
93 TRUE 0
94 FALSE 26
95 TRUE 0
96 TRUE 0
97 TRUE 0
98 TRUE 0
99 TRUE 0
100 TRUE 0
101 TRUE 0
102 TRUE 0
103 TRUE 0
104 TRUE 0
105 TRUE 0
106 TRUE 0
############################
############################
Epi_M2
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 125 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 26
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) 0.009 0.042 0.217 0.829
Matagg (b) -0.017 0.064 -0.261 0.794
FamScore (c) -0.054 0.063 -0.853 0.393
EduPar (d) -0.010 0.092 -0.109 0.913
n_trauma (e) 0.018 0.096 0.188 0.851
Age -0.042 0.101 -0.416 0.677
int_dis -0.010 0.038 -0.255 0.799
medication 0.018 0.039 0.453 0.651
contrcptvs 0.074 0.053 1.390 0.165
cigday_1 -0.058 0.091 -0.641 0.522
V8 -0.522 0.239 -2.187 0.029
group ~
Matsmk (f) 0.335 1.112 0.301 0.764
Matagg (g) 1.386 2.234 0.620 0.535
FamScore (h) -0.392 1.882 -0.208 0.835
EduPar (i) -3.524 2.208 -1.596 0.110
n_trauma (j) 2.558 1.304 1.962 0.050
Age -3.246 2.417 -1.343 0.179
int_dis 1.203 0.762 1.579 0.114
medication 1.319 0.808 1.632 0.103
contrcptvs 0.784 0.728 1.076 0.282
cigday_1 9.894 7.152 1.383 0.167
V8 8.740 16.605 0.526 0.599
Epi (z) -8.681 0.632 -13.736 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.886 0.141 6.296 0.000
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 -1.943 8.652 -0.225 0.822
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.013 0.002 6.750 0.000
.group 0.025
Scales y*:
Estimate Std.Err z-value P(>|z|)
group 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk 0.335 1.112 0.301 0.764
directMatagg 1.386 2.234 0.620 0.535
directFamScore -0.392 1.882 -0.208 0.835
directEduPar -3.524 2.208 -1.596 0.110
directn_trauma 2.558 1.304 1.962 0.050
EpiMatsmk -0.078 0.360 -0.217 0.828
EpiMatagg 0.144 0.549 0.262 0.793
EpiFamScore 0.466 0.550 0.847 0.397
EpiEduPar 0.087 0.796 0.109 0.913
Epin_trauma -0.158 0.837 -0.188 0.851
total 0.823 4.116 0.200 0.842
npar fmin
26.000 0.000
chisq df
0.000 0.000
pvalue chisq.scaled
NA 0.000
df.scaled pvalue.scaled
0.000 NA
chisq.scaling.factor baseline.chisq
NA 171.816
baseline.df baseline.pvalue
1.000 0.000
baseline.chisq.scaled baseline.df.scaled
171.816 1.000
baseline.pvalue.scaled baseline.chisq.scaling.factor
0.000 1.000
cfi tli
1.000 1.000
nnfi rfi
1.000 1.000
nfi pnfi
1.000 0.000
ifi rni
1.000 1.000
cfi.scaled tli.scaled
1.000 1.000
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
1.000 NA
rfi.scaled nfi.scaled
1.000 1.000
ifi.scaled rni.scaled
1.000 1.000
rni.robust rmsea
NA 0.000
rmsea.ci.lower rmsea.ci.upper
0.000 0.000
rmsea.pvalue rmsea.scaled
NA 0.000
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.000 0.000
rmsea.pvalue.scaled rmsea.robust
NA NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
0.000 0.000
rmsea.pvalue.robust rmr
NA 3.439
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
3.439 0.000
crmr crmr_nomean
4.440 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
1.000 1.000
gfi agfi
1.000 1.000
pgfi mfi
0.000 1.000
lhs op rhs label
1 Epi ~ Matsmk a
2 Epi ~ Matagg b
3 Epi ~ FamScore c
4 Epi ~ EduPar d
5 Epi ~ n_trauma e
6 Epi ~ Age
7 Epi ~ int_dis
8 Epi ~ medication
9 Epi ~ contraceptives
10 Epi ~ cigday_1
11 Epi ~ V8
12 group ~ Matsmk f
13 group ~ Matagg g
14 group ~ FamScore h
15 group ~ EduPar i
16 group ~ n_trauma j
17 group ~ Age
18 group ~ int_dis
19 group ~ medication
20 group ~ contraceptives
21 group ~ cigday_1
22 group ~ V8
23 group ~ Epi z
24 group | t1
25 Epi ~~ Epi
26 group ~~ group
27 Matsmk ~~ Matsmk
28 Matsmk ~~ Matagg
29 Matsmk ~~ FamScore
30 Matsmk ~~ EduPar
31 Matsmk ~~ n_trauma
32 Matsmk ~~ Age
33 Matsmk ~~ int_dis
34 Matsmk ~~ medication
35 Matsmk ~~ contraceptives
36 Matsmk ~~ cigday_1
37 Matsmk ~~ V8
38 Matagg ~~ Matagg
39 Matagg ~~ FamScore
40 Matagg ~~ EduPar
41 Matagg ~~ n_trauma
42 Matagg ~~ Age
43 Matagg ~~ int_dis
44 Matagg ~~ medication
45 Matagg ~~ contraceptives
46 Matagg ~~ cigday_1
47 Matagg ~~ V8
48 FamScore ~~ FamScore
49 FamScore ~~ EduPar
50 FamScore ~~ n_trauma
51 FamScore ~~ Age
52 FamScore ~~ int_dis
53 FamScore ~~ medication
54 FamScore ~~ contraceptives
55 FamScore ~~ cigday_1
56 FamScore ~~ V8
57 EduPar ~~ EduPar
58 EduPar ~~ n_trauma
59 EduPar ~~ Age
60 EduPar ~~ int_dis
61 EduPar ~~ medication
62 EduPar ~~ contraceptives
63 EduPar ~~ cigday_1
64 EduPar ~~ V8
65 n_trauma ~~ n_trauma
66 n_trauma ~~ Age
67 n_trauma ~~ int_dis
68 n_trauma ~~ medication
69 n_trauma ~~ contraceptives
70 n_trauma ~~ cigday_1
71 n_trauma ~~ V8
72 Age ~~ Age
73 Age ~~ int_dis
74 Age ~~ medication
75 Age ~~ contraceptives
76 Age ~~ cigday_1
77 Age ~~ V8
78 int_dis ~~ int_dis
79 int_dis ~~ medication
80 int_dis ~~ contraceptives
81 int_dis ~~ cigday_1
82 int_dis ~~ V8
83 medication ~~ medication
84 medication ~~ contraceptives
85 medication ~~ cigday_1
86 medication ~~ V8
87 contraceptives ~~ contraceptives
88 contraceptives ~~ cigday_1
89 contraceptives ~~ V8
90 cigday_1 ~~ cigday_1
91 cigday_1 ~~ V8
92 V8 ~~ V8
93 group ~*~ group
94 Epi ~1
95 group ~1
96 Matsmk ~1
97 Matagg ~1
98 FamScore ~1
99 EduPar ~1
100 n_trauma ~1
101 Age ~1
102 int_dis ~1
103 medication ~1
104 contraceptives ~1
105 cigday_1 ~1
106 V8 ~1
107 directMatsmk := f directMatsmk
108 directMatagg := g directMatagg
109 directFamScore := h directFamScore
110 directEduPar := i directEduPar
111 directn_trauma := j directn_trauma
112 EpiMatsmk := a*z EpiMatsmk
113 EpiMatagg := b*z EpiMatagg
114 EpiFamScore := c*z EpiFamScore
115 EpiEduPar := d*z EpiEduPar
116 Epin_trauma := e*z Epin_trauma
117 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 0.009 0.042 0.217 0.829 -0.073 0.091
2 -0.017 0.064 -0.261 0.794 -0.141 0.108
3 -0.054 0.063 -0.853 0.393 -0.177 0.070
4 -0.010 0.092 -0.109 0.913 -0.190 0.170
5 0.018 0.096 0.188 0.851 -0.171 0.207
6 -0.042 0.101 -0.416 0.677 -0.239 0.155
7 -0.010 0.038 -0.255 0.799 -0.083 0.064
8 0.018 0.039 0.453 0.651 -0.060 0.095
9 0.074 0.053 1.390 0.165 -0.030 0.179
10 -0.058 0.091 -0.641 0.522 -0.236 0.120
11 -0.522 0.239 -2.187 0.029 -0.990 -0.054
12 0.335 1.112 0.301 0.764 -1.845 2.515
13 1.386 2.234 0.620 0.535 -2.992 5.765
14 -0.392 1.882 -0.208 0.835 -4.080 3.296
15 -3.524 2.208 -1.596 0.110 -7.851 0.803
16 2.558 1.304 1.962 0.050 0.003 5.113
17 -3.246 2.417 -1.343 0.179 -7.984 1.493
18 1.203 0.762 1.579 0.114 -0.290 2.696
19 1.319 0.808 1.632 0.103 -0.265 2.903
20 0.784 0.728 1.076 0.282 -0.644 2.212
21 9.894 7.152 1.383 0.167 -4.125 23.912
22 8.740 16.605 0.526 0.599 -23.805 41.285
23 -8.681 0.632 -13.736 0.000 -9.920 -7.443
24 -1.943 8.652 -0.225 0.822 -18.900 15.015
25 0.013 0.002 6.750 0.000 0.009 0.017
26 0.025 0.000 NA NA 0.025 0.025
27 0.196 0.000 NA NA 0.196 0.196
28 0.049 0.000 NA NA 0.049 0.049
29 -0.009 0.000 NA NA -0.009 -0.009
30 -0.006 0.000 NA NA -0.006 -0.006
31 0.007 0.000 NA NA 0.007 0.007
32 -0.003 0.000 NA NA -0.003 -0.003
33 0.022 0.000 NA NA 0.022 0.022
34 0.004 0.000 NA NA 0.004 0.004
35 0.022 0.000 NA NA 0.022 0.022
36 0.017 0.000 NA NA 0.017 0.017
37 0.003 0.000 NA NA 0.003 0.003
38 0.091 0.000 NA NA 0.091 0.091
39 0.034 0.000 NA NA 0.034 0.034
40 -0.017 0.000 NA NA -0.017 -0.017
41 0.007 0.000 NA NA 0.007 0.007
42 0.000 0.000 NA NA 0.000 0.000
43 0.033 0.000 NA NA 0.033 0.033
44 0.008 0.000 NA NA 0.008 0.008
45 0.008 0.000 NA NA 0.008 0.008
46 0.010 0.000 NA NA 0.010 0.010
47 0.001 0.000 NA NA 0.001 0.001
48 0.132 0.000 NA NA 0.132 0.132
49 -0.029 0.000 NA NA -0.029 -0.029
50 0.027 0.000 NA NA 0.027 0.027
51 0.004 0.000 NA NA 0.004 0.004
52 0.065 0.000 NA NA 0.065 0.065
53 0.004 0.000 NA NA 0.004 0.004
54 0.058 0.000 NA NA 0.058 0.058
55 0.044 0.000 NA NA 0.044 0.044
56 0.001 0.000 NA NA 0.001 0.001
57 0.054 0.000 NA NA 0.054 0.054
58 -0.008 0.000 NA NA -0.008 -0.008
59 0.003 0.000 NA NA 0.003 0.003
60 -0.019 0.000 NA NA -0.019 -0.019
61 0.010 0.000 NA NA 0.010 0.010
62 -0.013 0.000 NA NA -0.013 -0.013
63 -0.015 0.000 NA NA -0.015 -0.015
64 0.000 0.000 NA NA 0.000 0.000
65 0.051 0.000 NA NA 0.051 0.051
66 0.002 0.000 NA NA 0.002 0.002
67 0.042 0.000 NA NA 0.042 0.042
68 0.018 0.000 NA NA 0.018 0.018
69 0.018 0.000 NA NA 0.018 0.018
70 0.021 0.000 NA NA 0.021 0.021
71 0.000 0.000 NA NA 0.000 0.000
72 0.047 0.000 NA NA 0.047 0.047
73 0.008 0.000 NA NA 0.008 0.008
74 -0.002 0.000 NA NA -0.002 -0.002
75 0.035 0.000 NA NA 0.035 0.035
76 0.009 0.000 NA NA 0.009 0.009
77 -0.001 0.000 NA NA -0.001 -0.001
78 0.213 0.000 NA NA 0.213 0.213
79 0.061 0.000 NA NA 0.061 0.061
80 0.061 0.000 NA NA 0.061 0.061
81 0.044 0.000 NA NA 0.044 0.044
82 0.006 0.000 NA NA 0.006 0.006
83 0.146 0.000 NA NA 0.146 0.146
84 0.035 0.000 NA NA 0.035 0.035
85 0.003 0.000 NA NA 0.003 0.003
86 0.002 0.000 NA NA 0.002 0.002
87 0.213 0.000 NA NA 0.213 0.213
88 0.049 0.000 NA NA 0.049 0.049
89 0.003 0.000 NA NA 0.003 0.003
90 0.062 0.000 NA NA 0.062 0.062
91 0.002 0.000 NA NA 0.002 0.002
92 0.005 0.000 NA NA 0.005 0.005
93 1.000 0.000 NA NA 1.000 1.000
94 0.886 0.141 6.296 0.000 0.610 1.162
95 0.000 0.000 NA NA 0.000 0.000
96 0.262 0.000 NA NA 0.262 0.262
97 0.100 0.000 NA NA 0.100 0.100
98 0.225 0.000 NA NA 0.225 0.225
99 0.606 0.000 NA NA 0.606 0.606
100 0.196 0.000 NA NA 0.196 0.196
101 0.562 0.000 NA NA 0.562 0.562
102 0.300 0.000 NA NA 0.300 0.300
103 0.175 0.000 NA NA 0.175 0.175
104 0.300 0.000 NA NA 0.300 0.300
105 0.124 0.000 NA NA 0.124 0.124
106 0.529 0.000 NA NA 0.529 0.529
107 0.335 1.112 0.301 0.764 -1.845 2.515
108 1.386 2.234 0.620 0.535 -2.992 5.765
109 -0.392 1.882 -0.208 0.835 -4.080 3.296
110 -3.524 2.208 -1.596 0.110 -7.851 0.803
111 2.558 1.304 1.962 0.050 0.003 5.113
112 -0.078 0.360 -0.217 0.828 -0.784 0.628
113 0.144 0.549 0.262 0.793 -0.932 1.219
114 0.466 0.550 0.847 0.397 -0.612 1.543
115 0.087 0.796 0.109 0.913 -1.472 1.647
116 -0.158 0.837 -0.188 0.851 -1.799 1.483
117 0.823 4.116 0.200 0.842 -7.244 8.889
label lhs edge rhs est std group
1 a Matsmk ~> Epi 9.021715e-03 0.0322271117
2 b Matagg ~> Epi -1.656132e-02 -0.0403369374
3 c FamScore ~> Epi -5.363064e-02 -0.1573666703
4 d EduPar ~> Epi -1.003047e-02 -0.0187689362
5 e n_trauma ~> Epi 1.817114e-02 0.0332184436
6 Age ~> Epi -4.183866e-02 -0.0735576520
7 int_dis ~> Epi -9.609875e-03 -0.0357531231
8 medication ~> Epi 1.786833e-02 0.0551209256
9 contraceptives ~> Epi 7.415668e-02 0.2758966954
10 cigday_1 ~> Epi -5.823697e-02 -0.1165352062
11 V8 ~> Epi -5.220008e-01 -0.2867721968
12 f Matsmk ~> group 3.345784e-01 0.0368272908
13 g Matagg ~> group 1.386144e+00 0.1040293437
14 h FamScore ~> group -3.919697e-01 -0.0354398486
15 i EduPar ~> group -3.524309e+00 -0.2032038294
16 j n_trauma ~> group 2.557950e+00 0.1440883623
17 Age ~> group -3.245643e+00 -0.1758289888
18 int_dis ~> group 1.202936e+00 0.1379045202
19 medication ~> group 1.319145e+00 0.1253905485
20 contraceptives ~> group 7.840853e-01 0.0898874779
21 cigday_1 ~> group 9.893837e+00 0.6100462328
22 V8 ~> group 8.739550e+00 0.1479430498
23 z Epi ~> group -8.681300e+00 -0.2675003892
25 Epi <-> Epi 1.294098e-02 0.8423209205
26 group <-> group 2.470311e-02 0.0015266561
27 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
28 Matsmk <-> Matagg 4.936709e-02 0.3693241433
29 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
30 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
31 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
32 Matsmk <-> Age -3.217300e-03 -0.0333441329
33 Matsmk <-> int_dis 2.151899e-02 0.1053910232
34 Matsmk <-> medication 4.113924e-03 0.0242997446
35 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
36 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
37 Matsmk <-> V8 2.928139e-03 0.0971189320
38 Matagg <-> Matagg 9.113924e-02 1.0000000000
39 Matagg <-> FamScore 3.417722e-02 0.3112715087
40 Matagg <-> EduPar -1.656118e-02 -0.2365241196
41 Matagg <-> n_trauma 7.233273e-03 0.1057402114
42 Matagg <-> Age 3.118694e-04 0.0047405101
43 Matagg <-> int_dis 3.291139e-02 0.2364027144
44 Matagg <-> medication 7.594937e-03 0.0657951695
45 Matagg <-> contraceptives 7.594937e-03 0.0545544726
46 Matagg <-> cigday_1 1.018987e-02 0.1360858260
47 Matagg <-> V8 8.272067e-04 0.0402393217
48 FamScore <-> FamScore 1.322785e-01 1.0000000000
49 FamScore <-> EduPar -2.948312e-02 -0.3495149022
50 FamScore <-> n_trauma 2.667269e-02 0.3236534989
51 FamScore <-> Age 3.636947e-03 0.0458878230
52 FamScore <-> int_dis 6.455696e-02 0.3849084009
53 FamScore <-> medication 4.430380e-03 0.0318580293
54 FamScore <-> contraceptives 5.822785e-02 0.3471722832
55 FamScore <-> cigday_1 4.381329e-02 0.4856887960
56 FamScore <-> V8 7.814844e-04 0.0315547719
57 EduPar <-> EduPar 5.379307e-02 1.0000000000
58 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
59 EduPar <-> Age 2.762108e-03 0.0546490350
60 EduPar <-> int_dis -1.909283e-02 -0.1785114035
61 EduPar <-> medication 1.017932e-02 0.1147832062
62 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
63 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
64 EduPar <-> V8 -8.860887e-06 -0.0005610523
65 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
66 n_trauma <-> Age 1.582278e-03 0.0320439451
67 n_trauma <-> int_dis 4.159132e-02 0.3980335009
68 n_trauma <-> medication 1.763110e-02 0.2034979577
69 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
70 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
71 n_trauma <-> V8 -4.694340e-04 -0.0304243917
72 Age <-> Age 4.748866e-02 1.0000000000
73 Age <-> int_dis 8.090259e-03 0.0805056484
74 Age <-> medication -1.655660e-03 -0.0198700345
75 Age <-> contraceptives 3.524124e-02 0.3506833348
76 Age <-> cigday_1 8.542355e-03 0.1580445206
77 Age <-> V8 -1.333633e-03 -0.0898732659
78 int_dis <-> int_dis 2.126582e-01 1.0000000000
79 int_dis <-> medication 6.075949e-02 0.3445843938
80 int_dis <-> contraceptives 6.075949e-02 0.2857142857
81 int_dis <-> cigday_1 4.449367e-02 0.3890038953
82 int_dis <-> V8 5.645344e-03 0.1797788722
83 medication <-> medication 1.462025e-01 1.0000000000
84 medication <-> contraceptives 3.544304e-02 0.2010075631
85 medication <-> cigday_1 3.275316e-03 0.0345360471
86 medication <-> V8 2.084232e-03 0.0800493604
87 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
88 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
89 contraceptives <-> V8 2.688833e-03 0.0856272484
90 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
91 cigday_1 <-> V8 1.509276e-03 0.0893623867
92 V8 <-> V8 4.636832e-03 1.0000000000
93 group <-> group 1.000000e+00 1.0000000000
94 int Epi 8.859017e-01 7.1472786192
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 2.625000e-01 0.5928600601
97 int Matagg 1.000000e-01 0.3312434486
98 int FamScore 2.250000e-01 0.6186398880
99 int EduPar 6.062500e-01 2.6138976225
100 int n_trauma 1.964286e-01 0.8668873691
101 int Age 5.621377e-01 2.5795724974
102 int int_dis 3.000000e-01 0.6505492185
103 int medication 1.750000e-01 0.4576785957
104 int contraceptives 3.000000e-01 0.6505492185
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 FALSE 1
2 FALSE 2
3 FALSE 3
4 FALSE 4
5 FALSE 5
6 FALSE 6
7 FALSE 7
8 FALSE 8
9 FALSE 9
10 FALSE 10
11 FALSE 11
12 FALSE 12
13 FALSE 13
14 FALSE 14
15 FALSE 15
16 FALSE 16
17 FALSE 17
18 FALSE 18
19 FALSE 19
20 FALSE 20
21 FALSE 21
22 FALSE 22
23 FALSE 23
25 FALSE 25
26 TRUE 0
27 TRUE 0
28 TRUE 0
29 TRUE 0
30 TRUE 0
31 TRUE 0
32 TRUE 0
33 TRUE 0
34 TRUE 0
35 TRUE 0
36 TRUE 0
37 TRUE 0
38 TRUE 0
39 TRUE 0
40 TRUE 0
41 TRUE 0
42 TRUE 0
43 TRUE 0
44 TRUE 0
45 TRUE 0
46 TRUE 0
47 TRUE 0
48 TRUE 0
49 TRUE 0
50 TRUE 0
51 TRUE 0
52 TRUE 0
53 TRUE 0
54 TRUE 0
55 TRUE 0
56 TRUE 0
57 TRUE 0
58 TRUE 0
59 TRUE 0
60 TRUE 0
61 TRUE 0
62 TRUE 0
63 TRUE 0
64 TRUE 0
65 TRUE 0
66 TRUE 0
67 TRUE 0
68 TRUE 0
69 TRUE 0
70 TRUE 0
71 TRUE 0
72 TRUE 0
73 TRUE 0
74 TRUE 0
75 TRUE 0
76 TRUE 0
77 TRUE 0
78 TRUE 0
79 TRUE 0
80 TRUE 0
81 TRUE 0
82 TRUE 0
83 TRUE 0
84 TRUE 0
85 TRUE 0
86 TRUE 0
87 TRUE 0
88 TRUE 0
89 TRUE 0
90 TRUE 0
91 TRUE 0
92 TRUE 0
93 TRUE 0
94 FALSE 26
95 TRUE 0
96 TRUE 0
97 TRUE 0
98 TRUE 0
99 TRUE 0
100 TRUE 0
101 TRUE 0
102 TRUE 0
103 TRUE 0
104 TRUE 0
105 TRUE 0
106 TRUE 0
############################
############################
Epi_M15
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 128 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 26
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) -0.055 0.057 -0.964 0.335
Matagg (b) 0.074 0.106 0.695 0.487
FamScore (c) 0.113 0.082 1.378 0.168
EduPar (d) 0.229 0.104 2.213 0.027
n_trauma (e) -0.062 0.089 -0.701 0.483
Age -0.088 0.121 -0.727 0.467
int_dis -0.065 0.047 -1.378 0.168
medication -0.024 0.056 -0.433 0.665
contrcptvs 0.032 0.055 0.579 0.562
cigday_1 -0.052 0.113 -0.458 0.647
V8 0.007 0.271 0.026 0.979
group ~
Matsmk (f) -0.073 1.061 -0.069 0.945
Matagg (g) 1.971 2.351 0.838 0.402
FamScore (h) 0.746 1.901 0.392 0.695
EduPar (i) -2.071 2.241 -0.924 0.355
n_trauma (j) 2.029 1.289 1.574 0.115
Age -3.407 2.353 -1.448 0.148
int_dis 0.899 0.769 1.169 0.242
medication 1.021 0.729 1.400 0.161
contrcptvs 0.331 0.694 0.476 0.634
cigday_1 10.090 7.086 1.424 0.154
V8 13.314 16.711 0.797 0.426
Epi (z) -5.965 1.303 -4.578 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.597 0.171 3.490 0.000
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 2.189 8.807 0.249 0.804
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.025 0.005 5.557 0.000
.group 0.102
Scales y*:
Estimate Std.Err z-value P(>|z|)
group 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk -0.073 1.061 -0.069 0.945
directMatagg 1.971 2.351 0.838 0.402
directFamScore 0.746 1.901 0.392 0.695
directEduPar -2.071 2.241 -0.924 0.355
directn_trauma 2.029 1.289 1.574 0.115
EpiMatsmk 0.329 0.338 0.976 0.329
EpiMatagg -0.441 0.632 -0.697 0.486
EpiFamScore -0.673 0.538 -1.249 0.212
EpiEduPar -1.367 0.653 -2.093 0.036
Epin_trauma 0.371 0.547 0.679 0.497
total 0.823 4.116 0.200 0.842
npar fmin
26.000 0.000
chisq df
0.000 0.000
pvalue chisq.scaled
NA 0.000
df.scaled pvalue.scaled
0.000 NA
chisq.scaling.factor baseline.chisq
NA 30.016
baseline.df baseline.pvalue
1.000 0.000
baseline.chisq.scaled baseline.df.scaled
30.016 1.000
baseline.pvalue.scaled baseline.chisq.scaling.factor
0.000 1.000
cfi tli
1.000 1.000
nnfi rfi
1.000 1.000
nfi pnfi
1.000 0.000
ifi rni
1.000 1.000
cfi.scaled tli.scaled
1.000 1.000
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
1.000 NA
rfi.scaled nfi.scaled
1.000 1.000
ifi.scaled rni.scaled
1.000 1.000
rni.robust rmsea
NA 0.000
rmsea.ci.lower rmsea.ci.upper
0.000 0.000
rmsea.pvalue rmsea.scaled
NA 0.000
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.000 0.000
rmsea.pvalue.scaled rmsea.robust
NA NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
0.000 0.000
rmsea.pvalue.robust rmr
NA 1.592
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
1.592 0.000
crmr crmr_nomean
2.055 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
1.000 1.000
gfi agfi
1.000 1.000
pgfi mfi
0.000 1.000
lhs op rhs label
1 Epi ~ Matsmk a
2 Epi ~ Matagg b
3 Epi ~ FamScore c
4 Epi ~ EduPar d
5 Epi ~ n_trauma e
6 Epi ~ Age
7 Epi ~ int_dis
8 Epi ~ medication
9 Epi ~ contraceptives
10 Epi ~ cigday_1
11 Epi ~ V8
12 group ~ Matsmk f
13 group ~ Matagg g
14 group ~ FamScore h
15 group ~ EduPar i
16 group ~ n_trauma j
17 group ~ Age
18 group ~ int_dis
19 group ~ medication
20 group ~ contraceptives
21 group ~ cigday_1
22 group ~ V8
23 group ~ Epi z
24 group | t1
25 Epi ~~ Epi
26 group ~~ group
27 Matsmk ~~ Matsmk
28 Matsmk ~~ Matagg
29 Matsmk ~~ FamScore
30 Matsmk ~~ EduPar
31 Matsmk ~~ n_trauma
32 Matsmk ~~ Age
33 Matsmk ~~ int_dis
34 Matsmk ~~ medication
35 Matsmk ~~ contraceptives
36 Matsmk ~~ cigday_1
37 Matsmk ~~ V8
38 Matagg ~~ Matagg
39 Matagg ~~ FamScore
40 Matagg ~~ EduPar
41 Matagg ~~ n_trauma
42 Matagg ~~ Age
43 Matagg ~~ int_dis
44 Matagg ~~ medication
45 Matagg ~~ contraceptives
46 Matagg ~~ cigday_1
47 Matagg ~~ V8
48 FamScore ~~ FamScore
49 FamScore ~~ EduPar
50 FamScore ~~ n_trauma
51 FamScore ~~ Age
52 FamScore ~~ int_dis
53 FamScore ~~ medication
54 FamScore ~~ contraceptives
55 FamScore ~~ cigday_1
56 FamScore ~~ V8
57 EduPar ~~ EduPar
58 EduPar ~~ n_trauma
59 EduPar ~~ Age
60 EduPar ~~ int_dis
61 EduPar ~~ medication
62 EduPar ~~ contraceptives
63 EduPar ~~ cigday_1
64 EduPar ~~ V8
65 n_trauma ~~ n_trauma
66 n_trauma ~~ Age
67 n_trauma ~~ int_dis
68 n_trauma ~~ medication
69 n_trauma ~~ contraceptives
70 n_trauma ~~ cigday_1
71 n_trauma ~~ V8
72 Age ~~ Age
73 Age ~~ int_dis
74 Age ~~ medication
75 Age ~~ contraceptives
76 Age ~~ cigday_1
77 Age ~~ V8
78 int_dis ~~ int_dis
79 int_dis ~~ medication
80 int_dis ~~ contraceptives
81 int_dis ~~ cigday_1
82 int_dis ~~ V8
83 medication ~~ medication
84 medication ~~ contraceptives
85 medication ~~ cigday_1
86 medication ~~ V8
87 contraceptives ~~ contraceptives
88 contraceptives ~~ cigday_1
89 contraceptives ~~ V8
90 cigday_1 ~~ cigday_1
91 cigday_1 ~~ V8
92 V8 ~~ V8
93 group ~*~ group
94 Epi ~1
95 group ~1
96 Matsmk ~1
97 Matagg ~1
98 FamScore ~1
99 EduPar ~1
100 n_trauma ~1
101 Age ~1
102 int_dis ~1
103 medication ~1
104 contraceptives ~1
105 cigday_1 ~1
106 V8 ~1
107 directMatsmk := f directMatsmk
108 directMatagg := g directMatagg
109 directFamScore := h directFamScore
110 directEduPar := i directEduPar
111 directn_trauma := j directn_trauma
112 EpiMatsmk := a*z EpiMatsmk
113 EpiMatagg := b*z EpiMatagg
114 EpiFamScore := c*z EpiFamScore
115 EpiEduPar := d*z EpiEduPar
116 Epin_trauma := e*z Epin_trauma
117 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 -0.055 0.057 -0.964 0.335 -0.168 0.057
2 0.074 0.106 0.695 0.487 -0.134 0.282
3 0.113 0.082 1.378 0.168 -0.048 0.273
4 0.229 0.104 2.213 0.027 0.026 0.432
5 -0.062 0.089 -0.701 0.483 -0.236 0.112
6 -0.088 0.121 -0.727 0.467 -0.325 0.149
7 -0.065 0.047 -1.378 0.168 -0.157 0.027
8 -0.024 0.056 -0.433 0.665 -0.133 0.085
9 0.032 0.055 0.579 0.562 -0.076 0.140
10 -0.052 0.113 -0.458 0.647 -0.274 0.170
11 0.007 0.271 0.026 0.979 -0.524 0.539
12 -0.073 1.061 -0.069 0.945 -2.152 2.006
13 1.971 2.351 0.838 0.402 -2.637 6.579
14 0.746 1.901 0.392 0.695 -2.980 4.472
15 -2.071 2.241 -0.924 0.355 -6.462 2.321
16 2.029 1.289 1.574 0.115 -0.498 4.556
17 -3.407 2.353 -1.448 0.148 -8.018 1.204
18 0.899 0.769 1.169 0.242 -0.608 2.406
19 1.021 0.729 1.400 0.161 -0.408 2.449
20 0.331 0.694 0.476 0.634 -1.030 1.691
21 10.090 7.086 1.424 0.154 -3.798 23.979
22 13.314 16.711 0.797 0.426 -19.439 46.066
23 -5.965 1.303 -4.578 0.000 -8.519 -3.411
24 2.189 8.807 0.249 0.804 -15.072 19.450
25 0.025 0.005 5.557 0.000 0.016 0.034
26 0.102 0.000 NA NA 0.102 0.102
27 0.196 0.000 NA NA 0.196 0.196
28 0.049 0.000 NA NA 0.049 0.049
29 -0.009 0.000 NA NA -0.009 -0.009
30 -0.006 0.000 NA NA -0.006 -0.006
31 0.007 0.000 NA NA 0.007 0.007
32 -0.003 0.000 NA NA -0.003 -0.003
33 0.022 0.000 NA NA 0.022 0.022
34 0.004 0.000 NA NA 0.004 0.004
35 0.022 0.000 NA NA 0.022 0.022
36 0.017 0.000 NA NA 0.017 0.017
37 0.003 0.000 NA NA 0.003 0.003
38 0.091 0.000 NA NA 0.091 0.091
39 0.034 0.000 NA NA 0.034 0.034
40 -0.017 0.000 NA NA -0.017 -0.017
41 0.007 0.000 NA NA 0.007 0.007
42 0.000 0.000 NA NA 0.000 0.000
43 0.033 0.000 NA NA 0.033 0.033
44 0.008 0.000 NA NA 0.008 0.008
45 0.008 0.000 NA NA 0.008 0.008
46 0.010 0.000 NA NA 0.010 0.010
47 0.001 0.000 NA NA 0.001 0.001
48 0.132 0.000 NA NA 0.132 0.132
49 -0.029 0.000 NA NA -0.029 -0.029
50 0.027 0.000 NA NA 0.027 0.027
51 0.004 0.000 NA NA 0.004 0.004
52 0.065 0.000 NA NA 0.065 0.065
53 0.004 0.000 NA NA 0.004 0.004
54 0.058 0.000 NA NA 0.058 0.058
55 0.044 0.000 NA NA 0.044 0.044
56 0.001 0.000 NA NA 0.001 0.001
57 0.054 0.000 NA NA 0.054 0.054
58 -0.008 0.000 NA NA -0.008 -0.008
59 0.003 0.000 NA NA 0.003 0.003
60 -0.019 0.000 NA NA -0.019 -0.019
61 0.010 0.000 NA NA 0.010 0.010
62 -0.013 0.000 NA NA -0.013 -0.013
63 -0.015 0.000 NA NA -0.015 -0.015
64 0.000 0.000 NA NA 0.000 0.000
65 0.051 0.000 NA NA 0.051 0.051
66 0.002 0.000 NA NA 0.002 0.002
67 0.042 0.000 NA NA 0.042 0.042
68 0.018 0.000 NA NA 0.018 0.018
69 0.018 0.000 NA NA 0.018 0.018
70 0.021 0.000 NA NA 0.021 0.021
71 0.000 0.000 NA NA 0.000 0.000
72 0.047 0.000 NA NA 0.047 0.047
73 0.008 0.000 NA NA 0.008 0.008
74 -0.002 0.000 NA NA -0.002 -0.002
75 0.035 0.000 NA NA 0.035 0.035
76 0.009 0.000 NA NA 0.009 0.009
77 -0.001 0.000 NA NA -0.001 -0.001
78 0.213 0.000 NA NA 0.213 0.213
79 0.061 0.000 NA NA 0.061 0.061
80 0.061 0.000 NA NA 0.061 0.061
81 0.044 0.000 NA NA 0.044 0.044
82 0.006 0.000 NA NA 0.006 0.006
83 0.146 0.000 NA NA 0.146 0.146
84 0.035 0.000 NA NA 0.035 0.035
85 0.003 0.000 NA NA 0.003 0.003
86 0.002 0.000 NA NA 0.002 0.002
87 0.213 0.000 NA NA 0.213 0.213
88 0.049 0.000 NA NA 0.049 0.049
89 0.003 0.000 NA NA 0.003 0.003
90 0.062 0.000 NA NA 0.062 0.062
91 0.002 0.000 NA NA 0.002 0.002
92 0.005 0.000 NA NA 0.005 0.005
93 1.000 0.000 NA NA 1.000 1.000
94 0.597 0.171 3.490 0.000 0.262 0.932
95 0.000 0.000 NA NA 0.000 0.000
96 0.262 0.000 NA NA 0.262 0.262
97 0.100 0.000 NA NA 0.100 0.100
98 0.225 0.000 NA NA 0.225 0.225
99 0.606 0.000 NA NA 0.606 0.606
100 0.196 0.000 NA NA 0.196 0.196
101 0.562 0.000 NA NA 0.562 0.562
102 0.300 0.000 NA NA 0.300 0.300
103 0.175 0.000 NA NA 0.175 0.175
104 0.300 0.000 NA NA 0.300 0.300
105 0.124 0.000 NA NA 0.124 0.124
106 0.529 0.000 NA NA 0.529 0.529
107 -0.073 1.061 -0.069 0.945 -2.152 2.006
108 1.971 2.351 0.838 0.402 -2.637 6.579
109 0.746 1.901 0.392 0.695 -2.980 4.472
110 -2.071 2.241 -0.924 0.355 -6.462 2.321
111 2.029 1.289 1.574 0.115 -0.498 4.556
112 0.329 0.338 0.976 0.329 -0.332 0.991
113 -0.441 0.632 -0.697 0.486 -1.680 0.799
114 -0.673 0.538 -1.249 0.212 -1.728 0.383
115 -1.367 0.653 -2.093 0.036 -2.647 -0.087
116 0.371 0.547 0.679 0.497 -0.700 1.442
117 0.823 4.116 0.200 0.842 -7.244 8.889
label lhs edge rhs est std group
1 a Matsmk ~> Epi -5.522110e-02 -0.1398958713
2 b Matagg ~> Epi 7.387035e-02 0.1275985011
3 c FamScore ~> Epi 1.127425e-01 0.2346145133
4 d EduPar ~> Epi 2.291129e-01 0.3040432768
5 e n_trauma ~> Epi -6.219035e-02 -0.0806283393
6 Age ~> Epi -8.796430e-02 -0.1096791548
7 int_dis ~> Epi -6.492648e-02 -0.1713111722
8 medication ~> Epi -2.404003e-02 -0.0525938795
9 contraceptives ~> Epi 3.188492e-02 0.0841296697
10 cigday_1 ~> Epi -5.184015e-02 -0.0735685389
11 V8 ~> Epi 7.148729e-03 0.0027852375
12 f Matsmk ~> group -7.313453e-02 -0.0080499725
13 g Matagg ~> group 1.970553e+00 0.1478888978
14 h FamScore ~> group 7.461203e-01 0.0674603007
15 i EduPar ~> group -2.070577e+00 -0.1193849131
16 j n_trauma ~> group 2.029237e+00 0.1143061573
17 Age ~> group -3.407134e+00 -0.1845775805
18 int_dis ~> group 8.990774e-01 0.1030701679
19 medication ~> group 1.020627e+00 0.0970150471
20 contraceptives ~> group 3.305022e-01 0.0378887435
21 cigday_1 ~> group 1.009018e+01 0.6221528685
22 V8 ~> group 1.331384e+01 0.2253765130
23 z Epi ~> group -5.964978e+00 -0.2591677250
25 Epi <-> Epi 2.524032e-02 0.8263046934
26 group <-> group 1.019253e-01 0.0062989979
27 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
28 Matsmk <-> Matagg 4.936709e-02 0.3693241433
29 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
30 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
31 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
32 Matsmk <-> Age -3.217300e-03 -0.0333441329
33 Matsmk <-> int_dis 2.151899e-02 0.1053910232
34 Matsmk <-> medication 4.113924e-03 0.0242997446
35 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
36 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
37 Matsmk <-> V8 2.928139e-03 0.0971189320
38 Matagg <-> Matagg 9.113924e-02 1.0000000000
39 Matagg <-> FamScore 3.417722e-02 0.3112715087
40 Matagg <-> EduPar -1.656118e-02 -0.2365241196
41 Matagg <-> n_trauma 7.233273e-03 0.1057402114
42 Matagg <-> Age 3.118694e-04 0.0047405101
43 Matagg <-> int_dis 3.291139e-02 0.2364027144
44 Matagg <-> medication 7.594937e-03 0.0657951695
45 Matagg <-> contraceptives 7.594937e-03 0.0545544726
46 Matagg <-> cigday_1 1.018987e-02 0.1360858260
47 Matagg <-> V8 8.272067e-04 0.0402393217
48 FamScore <-> FamScore 1.322785e-01 1.0000000000
49 FamScore <-> EduPar -2.948312e-02 -0.3495149022
50 FamScore <-> n_trauma 2.667269e-02 0.3236534989
51 FamScore <-> Age 3.636947e-03 0.0458878230
52 FamScore <-> int_dis 6.455696e-02 0.3849084009
53 FamScore <-> medication 4.430380e-03 0.0318580293
54 FamScore <-> contraceptives 5.822785e-02 0.3471722832
55 FamScore <-> cigday_1 4.381329e-02 0.4856887960
56 FamScore <-> V8 7.814844e-04 0.0315547719
57 EduPar <-> EduPar 5.379307e-02 1.0000000000
58 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
59 EduPar <-> Age 2.762108e-03 0.0546490350
60 EduPar <-> int_dis -1.909283e-02 -0.1785114035
61 EduPar <-> medication 1.017932e-02 0.1147832062
62 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
63 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
64 EduPar <-> V8 -8.860887e-06 -0.0005610523
65 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
66 n_trauma <-> Age 1.582278e-03 0.0320439451
67 n_trauma <-> int_dis 4.159132e-02 0.3980335009
68 n_trauma <-> medication 1.763110e-02 0.2034979577
69 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
70 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
71 n_trauma <-> V8 -4.694340e-04 -0.0304243917
72 Age <-> Age 4.748866e-02 1.0000000000
73 Age <-> int_dis 8.090259e-03 0.0805056484
74 Age <-> medication -1.655660e-03 -0.0198700345
75 Age <-> contraceptives 3.524124e-02 0.3506833348
76 Age <-> cigday_1 8.542355e-03 0.1580445206
77 Age <-> V8 -1.333633e-03 -0.0898732659
78 int_dis <-> int_dis 2.126582e-01 1.0000000000
79 int_dis <-> medication 6.075949e-02 0.3445843938
80 int_dis <-> contraceptives 6.075949e-02 0.2857142857
81 int_dis <-> cigday_1 4.449367e-02 0.3890038953
82 int_dis <-> V8 5.645344e-03 0.1797788722
83 medication <-> medication 1.462025e-01 1.0000000000
84 medication <-> contraceptives 3.544304e-02 0.2010075631
85 medication <-> cigday_1 3.275316e-03 0.0345360471
86 medication <-> V8 2.084232e-03 0.0800493604
87 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
88 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
89 contraceptives <-> V8 2.688833e-03 0.0856272484
90 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
91 cigday_1 <-> V8 1.509276e-03 0.0893623867
92 V8 <-> V8 4.636832e-03 1.0000000000
93 group <-> group 1.000000e+00 1.0000000000
94 int Epi 5.966198e-01 3.4136605955
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 2.625000e-01 0.5928600601
97 int Matagg 1.000000e-01 0.3312434486
98 int FamScore 2.250000e-01 0.6186398880
99 int EduPar 6.062500e-01 2.6138976225
100 int n_trauma 1.964286e-01 0.8668873691
101 int Age 5.621377e-01 2.5795724974
102 int int_dis 3.000000e-01 0.6505492185
103 int medication 1.750000e-01 0.4576785957
104 int contraceptives 3.000000e-01 0.6505492185
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 FALSE 1
2 FALSE 2
3 FALSE 3
4 FALSE 4
5 FALSE 5
6 FALSE 6
7 FALSE 7
8 FALSE 8
9 FALSE 9
10 FALSE 10
11 FALSE 11
12 FALSE 12
13 FALSE 13
14 FALSE 14
15 FALSE 15
16 FALSE 16
17 FALSE 17
18 FALSE 18
19 FALSE 19
20 FALSE 20
21 FALSE 21
22 FALSE 22
23 FALSE 23
25 FALSE 25
26 TRUE 0
27 TRUE 0
28 TRUE 0
29 TRUE 0
30 TRUE 0
31 TRUE 0
32 TRUE 0
33 TRUE 0
34 TRUE 0
35 TRUE 0
36 TRUE 0
37 TRUE 0
38 TRUE 0
39 TRUE 0
40 TRUE 0
41 TRUE 0
42 TRUE 0
43 TRUE 0
44 TRUE 0
45 TRUE 0
46 TRUE 0
47 TRUE 0
48 TRUE 0
49 TRUE 0
50 TRUE 0
51 TRUE 0
52 TRUE 0
53 TRUE 0
54 TRUE 0
55 TRUE 0
56 TRUE 0
57 TRUE 0
58 TRUE 0
59 TRUE 0
60 TRUE 0
61 TRUE 0
62 TRUE 0
63 TRUE 0
64 TRUE 0
65 TRUE 0
66 TRUE 0
67 TRUE 0
68 TRUE 0
69 TRUE 0
70 TRUE 0
71 TRUE 0
72 TRUE 0
73 TRUE 0
74 TRUE 0
75 TRUE 0
76 TRUE 0
77 TRUE 0
78 TRUE 0
79 TRUE 0
80 TRUE 0
81 TRUE 0
82 TRUE 0
83 TRUE 0
84 TRUE 0
85 TRUE 0
86 TRUE 0
87 TRUE 0
88 TRUE 0
89 TRUE 0
90 TRUE 0
91 TRUE 0
92 TRUE 0
93 TRUE 0
94 FALSE 26
95 TRUE 0
96 TRUE 0
97 TRUE 0
98 TRUE 0
99 TRUE 0
100 TRUE 0
101 TRUE 0
102 TRUE 0
103 TRUE 0
104 TRUE 0
105 TRUE 0
106 TRUE 0Warning in lav_samplestats_step2(UNI = FIT, wt = wt, ov.names = ov.names, :
lavaan WARNING: correlation between variables group and Epi is (nearly) 1.0############################
############################
Epi_M_all
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 136 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 26
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Regressions:
Estimate Std.Err z-value P(>|z|)
Epi ~
Matsmk (a) 0.025 0.082 0.301 0.764
Matagg (b) 0.132 0.124 1.068 0.285
FamScore (c) -0.102 0.109 -0.934 0.350
EduPar (d) -0.281 0.169 -1.658 0.097
n_trauma (e) 0.074 0.126 0.589 0.556
Age 0.122 0.179 0.679 0.497
int_dis 0.085 0.074 1.148 0.251
medication 0.050 0.080 0.623 0.533
contrcptvs -0.074 0.084 -0.872 0.383
cigday_1 0.168 0.140 1.205 0.228
V8 0.285 1.060 0.268 0.788
group ~
Matsmk (f) 0.161 1.017 0.158 0.874
Matagg (g) 1.018 2.327 0.437 0.662
FamScore (h) 0.468 1.959 0.239 0.811
EduPar (i) -2.348 2.257 -1.040 0.298
n_trauma (j) 2.111 1.316 1.604 0.109
Age -3.354 2.203 -1.523 0.128
int_dis 0.955 0.779 1.227 0.220
medication 0.970 0.728 1.333 0.183
contrcptvs 0.426 0.665 0.641 0.522
cigday_1 9.746 7.183 1.357 0.175
V8 12.167 14.326 0.849 0.396
Epi (z) 3.880 1.952 1.988 0.047
Intercepts:
Estimate Std.Err z-value P(>|z|)
.Epi 0.288 0.585 0.493 0.622
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 6.865 7.336 0.936 0.349
Variances:
Estimate Std.Err z-value P(>|z|)
.Epi 0.065 0.018 3.666 0.000
.group 0.016
Scales y*:
Estimate Std.Err z-value P(>|z|)
group 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
directMatsmk 0.161 1.017 0.158 0.874
directMatagg 1.018 2.327 0.437 0.662
directFamScore 0.468 1.959 0.239 0.811
directEduPar -2.348 2.257 -1.040 0.298
directn_trauma 2.111 1.316 1.604 0.109
EpiMatsmk 0.095 0.322 0.296 0.767
EpiMatagg 0.512 0.526 0.973 0.330
EpiFamScore -0.394 0.458 -0.861 0.389
EpiEduPar -1.089 0.818 -1.332 0.183
Epin_trauma 0.289 0.512 0.564 0.573
total 0.823 4.116 0.200 0.842
npar fmin
26.000 0.000
chisq df
0.000 0.000
pvalue chisq.scaled
NA 0.000
df.scaled pvalue.scaled
0.000 NA
chisq.scaling.factor baseline.chisq
NA 5.965
baseline.df baseline.pvalue
1.000 0.015
baseline.chisq.scaled baseline.df.scaled
5.965 1.000
baseline.pvalue.scaled baseline.chisq.scaling.factor
0.015 1.000
cfi tli
1.000 1.000
nnfi rfi
1.000 1.000
nfi pnfi
1.000 0.000
ifi rni
1.000 1.000
cfi.scaled tli.scaled
1.000 1.000
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
1.000 NA
rfi.scaled nfi.scaled
1.000 1.000
ifi.scaled rni.scaled
1.000 1.000
rni.robust rmsea
NA 0.000
rmsea.ci.lower rmsea.ci.upper
0.000 0.000
rmsea.pvalue rmsea.scaled
NA 0.000
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.000 0.000
rmsea.pvalue.scaled rmsea.robust
NA NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
0.000 0.000
rmsea.pvalue.robust rmr
NA 0.500
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
0.500 0.000
crmr crmr_nomean
0.645 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
1.000 1.000
gfi agfi
1.000 1.000
pgfi mfi
0.000 1.000
lhs op rhs label
1 Epi ~ Matsmk a
2 Epi ~ Matagg b
3 Epi ~ FamScore c
4 Epi ~ EduPar d
5 Epi ~ n_trauma e
6 Epi ~ Age
7 Epi ~ int_dis
8 Epi ~ medication
9 Epi ~ contraceptives
10 Epi ~ cigday_1
11 Epi ~ V8
12 group ~ Matsmk f
13 group ~ Matagg g
14 group ~ FamScore h
15 group ~ EduPar i
16 group ~ n_trauma j
17 group ~ Age
18 group ~ int_dis
19 group ~ medication
20 group ~ contraceptives
21 group ~ cigday_1
22 group ~ V8
23 group ~ Epi z
24 group | t1
25 Epi ~~ Epi
26 group ~~ group
27 Matsmk ~~ Matsmk
28 Matsmk ~~ Matagg
29 Matsmk ~~ FamScore
30 Matsmk ~~ EduPar
31 Matsmk ~~ n_trauma
32 Matsmk ~~ Age
33 Matsmk ~~ int_dis
34 Matsmk ~~ medication
35 Matsmk ~~ contraceptives
36 Matsmk ~~ cigday_1
37 Matsmk ~~ V8
38 Matagg ~~ Matagg
39 Matagg ~~ FamScore
40 Matagg ~~ EduPar
41 Matagg ~~ n_trauma
42 Matagg ~~ Age
43 Matagg ~~ int_dis
44 Matagg ~~ medication
45 Matagg ~~ contraceptives
46 Matagg ~~ cigday_1
47 Matagg ~~ V8
48 FamScore ~~ FamScore
49 FamScore ~~ EduPar
50 FamScore ~~ n_trauma
51 FamScore ~~ Age
52 FamScore ~~ int_dis
53 FamScore ~~ medication
54 FamScore ~~ contraceptives
55 FamScore ~~ cigday_1
56 FamScore ~~ V8
57 EduPar ~~ EduPar
58 EduPar ~~ n_trauma
59 EduPar ~~ Age
60 EduPar ~~ int_dis
61 EduPar ~~ medication
62 EduPar ~~ contraceptives
63 EduPar ~~ cigday_1
64 EduPar ~~ V8
65 n_trauma ~~ n_trauma
66 n_trauma ~~ Age
67 n_trauma ~~ int_dis
68 n_trauma ~~ medication
69 n_trauma ~~ contraceptives
70 n_trauma ~~ cigday_1
71 n_trauma ~~ V8
72 Age ~~ Age
73 Age ~~ int_dis
74 Age ~~ medication
75 Age ~~ contraceptives
76 Age ~~ cigday_1
77 Age ~~ V8
78 int_dis ~~ int_dis
79 int_dis ~~ medication
80 int_dis ~~ contraceptives
81 int_dis ~~ cigday_1
82 int_dis ~~ V8
83 medication ~~ medication
84 medication ~~ contraceptives
85 medication ~~ cigday_1
86 medication ~~ V8
87 contraceptives ~~ contraceptives
88 contraceptives ~~ cigday_1
89 contraceptives ~~ V8
90 cigday_1 ~~ cigday_1
91 cigday_1 ~~ V8
92 V8 ~~ V8
93 group ~*~ group
94 Epi ~1
95 group ~1
96 Matsmk ~1
97 Matagg ~1
98 FamScore ~1
99 EduPar ~1
100 n_trauma ~1
101 Age ~1
102 int_dis ~1
103 medication ~1
104 contraceptives ~1
105 cigday_1 ~1
106 V8 ~1
107 directMatsmk := f directMatsmk
108 directMatagg := g directMatagg
109 directFamScore := h directFamScore
110 directEduPar := i directEduPar
111 directn_trauma := j directn_trauma
112 EpiMatsmk := a*z EpiMatsmk
113 EpiMatagg := b*z EpiMatagg
114 EpiFamScore := c*z EpiFamScore
115 EpiEduPar := d*z EpiEduPar
116 Epin_trauma := e*z Epin_trauma
117 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 0.025 0.082 0.301 0.764 -0.136 0.185
2 0.132 0.124 1.068 0.285 -0.110 0.374
3 -0.102 0.109 -0.934 0.350 -0.315 0.112
4 -0.281 0.169 -1.658 0.097 -0.613 0.051
5 0.074 0.126 0.589 0.556 -0.173 0.322
6 0.122 0.179 0.679 0.497 -0.230 0.473
7 0.085 0.074 1.148 0.251 -0.060 0.231
8 0.050 0.080 0.623 0.533 -0.107 0.207
9 -0.074 0.084 -0.872 0.383 -0.239 0.092
10 0.168 0.140 1.205 0.228 -0.105 0.442
11 0.285 1.060 0.268 0.788 -1.793 2.362
12 0.161 1.017 0.158 0.874 -1.833 2.155
13 1.018 2.327 0.437 0.662 -3.543 5.578
14 0.468 1.959 0.239 0.811 -3.372 4.307
15 -2.348 2.257 -1.040 0.298 -6.772 2.076
16 2.111 1.316 1.604 0.109 -0.469 4.691
17 -3.354 2.203 -1.523 0.128 -7.671 0.963
18 0.955 0.779 1.227 0.220 -0.571 2.482
19 0.970 0.728 1.333 0.183 -0.457 2.396
20 0.426 0.665 0.641 0.522 -0.877 1.729
21 9.746 7.183 1.357 0.175 -4.333 23.825
22 12.167 14.326 0.849 0.396 -15.911 40.245
23 3.880 1.952 1.988 0.047 0.054 7.706
24 6.865 7.336 0.936 0.349 -7.514 21.244
25 0.065 0.018 3.666 0.000 0.030 0.100
26 0.016 0.000 NA NA 0.016 0.016
27 0.196 0.000 NA NA 0.196 0.196
28 0.049 0.000 NA NA 0.049 0.049
29 -0.009 0.000 NA NA -0.009 -0.009
30 -0.006 0.000 NA NA -0.006 -0.006
31 0.007 0.000 NA NA 0.007 0.007
32 -0.003 0.000 NA NA -0.003 -0.003
33 0.022 0.000 NA NA 0.022 0.022
34 0.004 0.000 NA NA 0.004 0.004
35 0.022 0.000 NA NA 0.022 0.022
36 0.017 0.000 NA NA 0.017 0.017
37 0.003 0.000 NA NA 0.003 0.003
38 0.091 0.000 NA NA 0.091 0.091
39 0.034 0.000 NA NA 0.034 0.034
40 -0.017 0.000 NA NA -0.017 -0.017
41 0.007 0.000 NA NA 0.007 0.007
42 0.000 0.000 NA NA 0.000 0.000
43 0.033 0.000 NA NA 0.033 0.033
44 0.008 0.000 NA NA 0.008 0.008
45 0.008 0.000 NA NA 0.008 0.008
46 0.010 0.000 NA NA 0.010 0.010
47 0.001 0.000 NA NA 0.001 0.001
48 0.132 0.000 NA NA 0.132 0.132
49 -0.029 0.000 NA NA -0.029 -0.029
50 0.027 0.000 NA NA 0.027 0.027
51 0.004 0.000 NA NA 0.004 0.004
52 0.065 0.000 NA NA 0.065 0.065
53 0.004 0.000 NA NA 0.004 0.004
54 0.058 0.000 NA NA 0.058 0.058
55 0.044 0.000 NA NA 0.044 0.044
56 0.001 0.000 NA NA 0.001 0.001
57 0.054 0.000 NA NA 0.054 0.054
58 -0.008 0.000 NA NA -0.008 -0.008
59 0.003 0.000 NA NA 0.003 0.003
60 -0.019 0.000 NA NA -0.019 -0.019
61 0.010 0.000 NA NA 0.010 0.010
62 -0.013 0.000 NA NA -0.013 -0.013
63 -0.015 0.000 NA NA -0.015 -0.015
64 0.000 0.000 NA NA 0.000 0.000
65 0.051 0.000 NA NA 0.051 0.051
66 0.002 0.000 NA NA 0.002 0.002
67 0.042 0.000 NA NA 0.042 0.042
68 0.018 0.000 NA NA 0.018 0.018
69 0.018 0.000 NA NA 0.018 0.018
70 0.021 0.000 NA NA 0.021 0.021
71 0.000 0.000 NA NA 0.000 0.000
72 0.047 0.000 NA NA 0.047 0.047
73 0.008 0.000 NA NA 0.008 0.008
74 -0.002 0.000 NA NA -0.002 -0.002
75 0.035 0.000 NA NA 0.035 0.035
76 0.009 0.000 NA NA 0.009 0.009
77 -0.001 0.000 NA NA -0.001 -0.001
78 0.213 0.000 NA NA 0.213 0.213
79 0.061 0.000 NA NA 0.061 0.061
80 0.061 0.000 NA NA 0.061 0.061
81 0.044 0.000 NA NA 0.044 0.044
82 0.006 0.000 NA NA 0.006 0.006
83 0.146 0.000 NA NA 0.146 0.146
84 0.035 0.000 NA NA 0.035 0.035
85 0.003 0.000 NA NA 0.003 0.003
86 0.002 0.000 NA NA 0.002 0.002
87 0.213 0.000 NA NA 0.213 0.213
88 0.049 0.000 NA NA 0.049 0.049
89 0.003 0.000 NA NA 0.003 0.003
90 0.062 0.000 NA NA 0.062 0.062
91 0.002 0.000 NA NA 0.002 0.002
92 0.005 0.000 NA NA 0.005 0.005
93 1.000 0.000 NA NA 1.000 1.000
94 0.288 0.585 0.493 0.622 -0.858 1.434
95 0.000 0.000 NA NA 0.000 0.000
96 0.262 0.000 NA NA 0.262 0.262
97 0.100 0.000 NA NA 0.100 0.100
98 0.225 0.000 NA NA 0.225 0.225
99 0.606 0.000 NA NA 0.606 0.606
100 0.196 0.000 NA NA 0.196 0.196
101 0.562 0.000 NA NA 0.562 0.562
102 0.300 0.000 NA NA 0.300 0.300
103 0.175 0.000 NA NA 0.175 0.175
104 0.300 0.000 NA NA 0.300 0.300
105 0.124 0.000 NA NA 0.124 0.124
106 0.529 0.000 NA NA 0.529 0.529
107 0.161 1.017 0.158 0.874 -1.833 2.155
108 1.018 2.327 0.437 0.662 -3.543 5.578
109 0.468 1.959 0.239 0.811 -3.372 4.307
110 -2.348 2.257 -1.040 0.298 -6.772 2.076
111 2.111 1.316 1.604 0.109 -0.469 4.691
112 0.095 0.322 0.296 0.767 -0.536 0.727
113 0.512 0.526 0.973 0.330 -0.519 1.544
114 -0.394 0.458 -0.861 0.389 -1.292 0.503
115 -1.089 0.818 -1.332 0.183 -2.692 0.514
116 0.289 0.512 0.564 0.573 -0.715 1.293
117 0.823 4.116 0.200 0.842 -7.244 8.889
label lhs edge rhs est std group
1 a Matsmk ~> Epi 2.458187e-02 0.0385922838
2 b Matagg ~> Epi 1.320288e-01 0.1413284937
3 c FamScore ~> Epi -1.016065e-01 -0.1310311340
4 d EduPar ~> Epi -2.807576e-01 -0.2308889324
5 e n_trauma ~> Epi 7.446007e-02 0.0598237817
6 Age ~> Epi 1.216014e-01 0.0939597533
7 int_dis ~> Epi 8.529375e-02 0.1394654866
8 medication ~> Epi 5.004596e-02 0.0678507993
9 contraceptives ~> Epi -7.362480e-02 -0.1203853562
10 cigday_1 ~> Epi 1.683722e-01 0.1480751015
11 V8 ~> Epi 2.846111e-01 0.0687180812
12 f Matsmk ~> group 1.608809e-01 0.0177082752
13 g Matagg ~> group 1.017648e+00 0.0763739054
14 h FamScore ~> group 4.678460e-01 0.0423001876
15 i EduPar ~> group -2.347896e+00 -0.1353744772
16 j n_trauma ~> group 2.111297e+00 0.1189285637
17 Age ~> group -3.354240e+00 -0.1817121416
18 int_dis ~> group 9.554240e-01 0.1095297311
19 medication ~> group 9.698471e-01 0.0921882416
20 contraceptives ~> group 4.259721e-01 0.0488334116
21 cigday_1 ~> group 9.746129e+00 0.6009386284
22 V8 ~> group 1.216691e+01 0.2059612926
23 z Epi ~> group 3.879985e+00 0.2720297673
25 Epi <-> Epi 6.534223e-02 0.8215062257
26 group <-> group 1.631934e-02 0.0010085380
27 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
28 Matsmk <-> Matagg 4.936709e-02 0.3693241433
29 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
30 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
31 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
32 Matsmk <-> Age -3.217300e-03 -0.0333441329
33 Matsmk <-> int_dis 2.151899e-02 0.1053910232
34 Matsmk <-> medication 4.113924e-03 0.0242997446
35 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
36 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
37 Matsmk <-> V8 2.928139e-03 0.0971189320
38 Matagg <-> Matagg 9.113924e-02 1.0000000000
39 Matagg <-> FamScore 3.417722e-02 0.3112715087
40 Matagg <-> EduPar -1.656118e-02 -0.2365241196
41 Matagg <-> n_trauma 7.233273e-03 0.1057402114
42 Matagg <-> Age 3.118694e-04 0.0047405101
43 Matagg <-> int_dis 3.291139e-02 0.2364027144
44 Matagg <-> medication 7.594937e-03 0.0657951695
45 Matagg <-> contraceptives 7.594937e-03 0.0545544726
46 Matagg <-> cigday_1 1.018987e-02 0.1360858260
47 Matagg <-> V8 8.272067e-04 0.0402393217
48 FamScore <-> FamScore 1.322785e-01 1.0000000000
49 FamScore <-> EduPar -2.948312e-02 -0.3495149022
50 FamScore <-> n_trauma 2.667269e-02 0.3236534989
51 FamScore <-> Age 3.636947e-03 0.0458878230
52 FamScore <-> int_dis 6.455696e-02 0.3849084009
53 FamScore <-> medication 4.430380e-03 0.0318580293
54 FamScore <-> contraceptives 5.822785e-02 0.3471722832
55 FamScore <-> cigday_1 4.381329e-02 0.4856887960
56 FamScore <-> V8 7.814844e-04 0.0315547719
57 EduPar <-> EduPar 5.379307e-02 1.0000000000
58 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
59 EduPar <-> Age 2.762108e-03 0.0546490350
60 EduPar <-> int_dis -1.909283e-02 -0.1785114035
61 EduPar <-> medication 1.017932e-02 0.1147832062
62 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
63 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
64 EduPar <-> V8 -8.860887e-06 -0.0005610523
65 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
66 n_trauma <-> Age 1.582278e-03 0.0320439451
67 n_trauma <-> int_dis 4.159132e-02 0.3980335009
68 n_trauma <-> medication 1.763110e-02 0.2034979577
69 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
70 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
71 n_trauma <-> V8 -4.694340e-04 -0.0304243917
72 Age <-> Age 4.748866e-02 1.0000000000
73 Age <-> int_dis 8.090259e-03 0.0805056484
74 Age <-> medication -1.655660e-03 -0.0198700345
75 Age <-> contraceptives 3.524124e-02 0.3506833348
76 Age <-> cigday_1 8.542355e-03 0.1580445206
77 Age <-> V8 -1.333633e-03 -0.0898732659
78 int_dis <-> int_dis 2.126582e-01 1.0000000000
79 int_dis <-> medication 6.075949e-02 0.3445843938
80 int_dis <-> contraceptives 6.075949e-02 0.2857142857
81 int_dis <-> cigday_1 4.449367e-02 0.3890038953
82 int_dis <-> V8 5.645344e-03 0.1797788722
83 medication <-> medication 1.462025e-01 1.0000000000
84 medication <-> contraceptives 3.544304e-02 0.2010075631
85 medication <-> cigday_1 3.275316e-03 0.0345360471
86 medication <-> V8 2.084232e-03 0.0800493604
87 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
88 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
89 contraceptives <-> V8 2.688833e-03 0.0856272484
90 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
91 cigday_1 <-> V8 1.509276e-03 0.0893623867
92 V8 <-> V8 4.636832e-03 1.0000000000
93 group <-> group 1.000000e+00 1.0000000000
94 int Epi 2.878933e-01 1.0207984706
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 2.625000e-01 0.5928600601
97 int Matagg 1.000000e-01 0.3312434486
98 int FamScore 2.250000e-01 0.6186398880
99 int EduPar 6.062500e-01 2.6138976225
100 int n_trauma 1.964286e-01 0.8668873691
101 int Age 5.621377e-01 2.5795724974
102 int int_dis 3.000000e-01 0.6505492185
103 int medication 1.750000e-01 0.4576785957
104 int contraceptives 3.000000e-01 0.6505492185
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 FALSE 1
2 FALSE 2
3 FALSE 3
4 FALSE 4
5 FALSE 5
6 FALSE 6
7 FALSE 7
8 FALSE 8
9 FALSE 9
10 FALSE 10
11 FALSE 11
12 FALSE 12
13 FALSE 13
14 FALSE 14
15 FALSE 15
16 FALSE 16
17 FALSE 17
18 FALSE 18
19 FALSE 19
20 FALSE 20
21 FALSE 21
22 FALSE 22
23 FALSE 23
25 FALSE 25
26 TRUE 0
27 TRUE 0
28 TRUE 0
29 TRUE 0
30 TRUE 0
31 TRUE 0
32 TRUE 0
33 TRUE 0
34 TRUE 0
35 TRUE 0
36 TRUE 0
37 TRUE 0
38 TRUE 0
39 TRUE 0
40 TRUE 0
41 TRUE 0
42 TRUE 0
43 TRUE 0
44 TRUE 0
45 TRUE 0
46 TRUE 0
47 TRUE 0
48 TRUE 0
49 TRUE 0
50 TRUE 0
51 TRUE 0
52 TRUE 0
53 TRUE 0
54 TRUE 0
55 TRUE 0
56 TRUE 0
57 TRUE 0
58 TRUE 0
59 TRUE 0
60 TRUE 0
61 TRUE 0
62 TRUE 0
63 TRUE 0
64 TRUE 0
65 TRUE 0
66 TRUE 0
67 TRUE 0
68 TRUE 0
69 TRUE 0
70 TRUE 0
71 TRUE 0
72 TRUE 0
73 TRUE 0
74 TRUE 0
75 TRUE 0
76 TRUE 0
77 TRUE 0
78 TRUE 0
79 TRUE 0
80 TRUE 0
81 TRUE 0
82 TRUE 0
83 TRUE 0
84 TRUE 0
85 TRUE 0
86 TRUE 0
87 TRUE 0
88 TRUE 0
89 TRUE 0
90 TRUE 0
91 TRUE 0
92 TRUE 0
93 TRUE 0
94 FALSE 26
95 TRUE 0
96 TRUE 0
97 TRUE 0
98 TRUE 0
99 TRUE 0
100 TRUE 0
101 TRUE 0
102 TRUE 0
103 TRUE 0
104 TRUE 0
105 TRUE 0
106 TRUE 0rmd_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 SEM plots
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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] plyr_1.8.6 scales_1.1.1
[3] RCircos_1.2.1 compareGroups_4.4.6
[5] readxl_1.3.1 RRHO_1.28.0
[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 workflowr_1.6.2
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 lambda.r_1.2.4 RSQLite_2.2.2
[13] mice_3.12.0 chron_2.3-56 bit_4.0.4
[16] xml2_1.3.2 lubridate_1.7.9.2 httpuv_1.5.5
[19] assertthat_0.2.1 d3Network_0.5.2.1 xfun_0.20
[22] hms_1.0.0 rJava_0.9-13 evaluate_0.14
[25] promises_1.1.1 fansi_0.4.1 caTools_1.18.1
[28] dbplyr_2.0.0 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 futile.logger_1.4.3 ellipsis_0.3.1
[37] crosstalk_1.1.1 backports_1.2.0 pbivnorm_0.6.0
[40] annotate_1.68.0 vctrs_0.3.6 abind_1.4-5
[43] cachem_1.0.1 withr_2.4.1 HardyWeinberg_1.7.1
[46] checkmate_2.0.0 fdrtool_1.2.16 mnormt_2.0.2
[49] cluster_2.1.0 mi_1.0 lazyeval_0.2.2
[52] crayon_1.3.4 genefilter_1.72.0 pkgconfig_2.0.3
[55] nlme_3.1-151 nnet_7.3-15 rlang_0.4.10
[58] lifecycle_0.2.0 kutils_1.70 modelr_0.1.8
[61] VennDiagram_1.6.20 cellranger_1.1.0 rprojroot_2.0.2
[64] flextable_0.6.2 Matrix_1.2-18 regsem_1.6.2
[67] carData_3.0-4 boot_1.3-26 reprex_1.0.0
[70] base64enc_0.1-3 processx_3.4.5 whisker_0.4
[73] png_0.1-7 rjson_0.2.20 bitops_1.0-6
[76] KernSmooth_2.23-18 blob_1.2.1 arm_1.11-2
[79] jpeg_0.1-8.1 rockchalk_1.8.144 memoise_2.0.0
[82] magrittr_2.0.1 zlibbioc_1.36.0 compiler_4.0.3
[85] lme4_1.1-26 cli_2.2.0 XVector_0.30.0
[88] pbapply_1.4-3 ps_1.5.0 htmlTable_2.1.0
[91] formatR_1.7 Formula_1.2-4 MASS_7.3-53
[94] tidyselect_1.1.0 stringi_1.5.3 lisrelToR_0.1.4
[97] sem_3.1-11 yaml_2.2.1 OpenMx_2.18.1
[100] locfit_1.5-9.4 latticeExtra_0.6-29 tools_4.0.3
[103] matrixcalc_1.0-3 rstudioapi_0.13 uuid_0.1-4
[106] foreach_1.5.1 foreign_0.8-81 git2r_0.28.0
[109] gridExtra_2.3 farver_2.0.3 BDgraph_2.63
[112] digest_0.6.27 shiny_1.6.0 Rcpp_1.0.5
[115] broom_0.7.3 later_1.1.0.1 writexl_1.3.1
[118] gdtools_0.2.3 httr_1.4.2 psych_2.0.12
[121] colorspace_2.0-0 rvest_0.3.6 XML_3.99-0.5
[124] fs_1.5.0 truncnorm_1.0-8 splines_4.0.3
[127] statmod_1.4.35 xlsxjars_0.6.1 systemfonts_0.3.2
[130] plotly_4.9.3 xtable_1.8-4 jsonlite_1.7.2
[133] nloptr_1.2.2.2 futile.options_1.0.1 corpcor_1.6.9
[136] glasso_1.11 R6_2.5.0 Hmisc_4.4-2
[139] mime_0.9 pillar_1.4.7 htmltools_0.5.1.1
[142] glue_1.4.2 fastmap_1.1.0 minqa_1.2.4
[145] codetools_0.2-18 lattice_0.20-41 huge_1.3.4.1
[148] gtools_3.8.2 officer_0.3.16 zip_2.1.1
[151] GO.db_3.12.1 openxlsx_4.2.3 survival_3.2-7
[154] rmarkdown_2.6 qgraph_1.6.5 munsell_0.5.0
[157] GenomeInfoDbData_1.2.4 iterators_1.0.13 impute_1.64.0
[160] haven_2.3.1 reshape2_1.4.4 gtable_0.3.0