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$dim1
EpiMarker = c(EpiMarker, epiname)
}
cormat = cor(apply(Patdata[,c("group", envFact, modelFact, EpiMarker)] %>% mutate_all(as.numeric), 2, minmax_scaling),
use = "pairwise.complete.obs")
par(mfrow=c(1,2))
corrplot(cormat, main="correlations")
corrplot(cormat, order = "hclust", main="correlations ordered")
SEM analysis
fullmodEnv=paste(unique(envFact,modelFact), sep = "+", collapse = "+")
Dataset = Patdata[,c("group", envFact, modelFact,EpiMarker)]
model = "
Epi~0+a*Matsmk+b*Matagg+c*FamScore+d*EduPar+e*n_trauma+Age+int_dis+medication+contraceptives+cigday_1+V8
group~f*Matsmk+g*Matagg+h*FamScore+i*EduPar+j*n_trauma+Age+int_dis+medication+contraceptives+cigday_1+V8+z*Epi
#direct
directMatsmk := f
directMatagg := g
directFamScore := h
directEduPar := i
directn_trauma := j
#indirect
EpiMatsmk := a*z
EpiMatagg := b*z
EpiFamScore := c*z
EpiEduPar := d*z
Epin_trauma := e*z
total := f + g + h + i + j + (a*z)+(b*z)+(c*z)+(d*z)+(e*z)
"
Netlist = list()
nothing = function(x){return(x)}
for (marker in EpiMarker) {
Dataset$Epi = Dataset[,marker]
Datasetscaled = Dataset %>% mutate_if(is.numeric, minmax_scaling)
Datasetscaled = Datasetscaled %>% mutate_if(is.factor,ordered)
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::parameterEstimates(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::parameterEstimates(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$label %in% envFact] = Dark8[5]
if(nrow(DF)>0){
edges <- data.frame(from = DF$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
}
else {edges = data.frame(from=NULL, to = NULL)}
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_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: exogenous variable(s) declared as ordered in data: Matsmk Matagg
int_dis medication contraceptivesWarning 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 120 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 25
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 10.983 10.983
Degrees of freedom 1 1
P-value (Chi-square) 0.001 0.001
Scaling correction factor 1.000
Shift parameter -0.000
simple second-order correction
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 0.000
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 10.125 9.268 1.092 0.275
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
25.000 0.069
chisq df
10.983 1.000
pvalue chisq.scaled
0.001 10.983
df.scaled pvalue.scaled
1.000 0.001
chisq.scaling.factor baseline.chisq
1.000 111.273
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
0.909 0.909
nnfi rfi
0.909 0.901
nfi pnfi
0.901 0.901
ifi rni
0.909 0.909
cfi.scaled tli.scaled
0.909 0.909
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
0.909 NA
rfi.scaled nfi.scaled
0.901 0.901
ifi.scaled rni.scaled
0.909 0.909
rni.robust rmsea
NA 0.355
rmsea.ci.lower rmsea.ci.upper
0.188 0.558
rmsea.pvalue rmsea.scaled
0.002 0.355
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.188 0.558
rmsea.pvalue.scaled rmsea.robust
0.002 NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
NA NA
rmsea.pvalue.robust rmr
NA 0.459
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
3.004 0.000
crmr crmr_nomean
3.878 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
28.632 48.726
gfi agfi
0.937 -0.650
pgfi mfi
0.036 0.939
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~ Matsmk f
14 group ~ Matagg g
15 group ~ FamScore h
16 group ~ EduPar i
17 group ~ n_trauma j
18 group ~ Age
19 group ~ int_dis
20 group ~ medication
21 group ~ contraceptives
22 group ~ cigday_1
23 group ~ V8
24 group ~ Epi z
25 group | t1
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 group ~*~ group
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 0.000 0.000 NA NA 0.000 0.000
2 -0.033 0.050 -0.657 0.511 -0.130 0.065
3 -0.014 0.072 -0.202 0.840 -0.155 0.126
4 0.056 0.076 0.739 0.460 -0.093 0.206
5 -0.038 0.108 -0.358 0.721 -0.249 0.172
6 0.085 0.111 0.767 0.443 -0.132 0.302
7 -0.090 0.097 -0.929 0.353 -0.280 0.100
8 -0.074 0.057 -1.310 0.190 -0.186 0.037
9 -0.044 0.059 -0.759 0.448 -0.159 0.070
10 -0.017 0.049 -0.341 0.733 -0.113 0.080
11 -0.111 0.136 -0.815 0.415 -0.378 0.156
12 -0.089 0.568 -0.156 0.876 -1.202 1.025
13 0.045 1.077 0.041 0.967 -2.066 2.155
14 1.436 2.290 0.627 0.531 -3.053 5.924
15 0.440 2.056 0.214 0.831 -3.590 4.471
16 -3.687 2.227 -1.655 0.098 -8.052 0.678
17 2.952 1.445 2.043 0.041 0.119 5.784
18 -3.468 2.438 -1.422 0.155 -8.247 1.310
19 0.802 0.808 0.992 0.321 -0.782 2.387
20 0.875 0.817 1.072 0.284 -0.725 2.475
21 0.031 0.843 0.037 0.970 -1.621 1.683
22 9.678 7.094 1.364 0.172 -4.225 23.582
23 12.694 16.068 0.790 0.430 -18.799 44.187
24 -6.500 0.589 -11.034 0.000 -7.654 -5.345
25 10.125 9.268 1.092 0.275 -8.040 28.289
26 0.023 0.004 5.481 0.000 0.015 0.032
27 0.014 0.000 NA NA 0.014 0.014
28 0.196 0.000 NA NA 0.196 0.196
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.006 0.000 NA NA -0.006 -0.006
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.022 0.000 NA NA 0.022 0.022
35 0.004 0.000 NA NA 0.004 0.004
36 0.022 0.000 NA NA 0.022 0.022
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.091 0.000 NA NA 0.091 0.091
40 0.034 0.000 NA NA 0.034 0.034
41 -0.017 0.000 NA NA -0.017 -0.017
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.033 0.000 NA NA 0.033 0.033
45 0.008 0.000 NA NA 0.008 0.008
46 0.008 0.000 NA NA 0.008 0.008
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.132 0.000 NA NA 0.132 0.132
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.027 0.000 NA NA 0.027 0.027
52 0.004 0.000 NA NA 0.004 0.004
53 0.065 0.000 NA NA 0.065 0.065
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.044 0.000 NA NA 0.044 0.044
57 0.001 0.000 NA NA 0.001 0.001
58 0.054 0.000 NA NA 0.054 0.054
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.042 0.000 NA NA 0.042 0.042
69 0.018 0.000 NA NA 0.018 0.018
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.009 0.000 NA NA 0.009 0.009
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.213 0.000 NA NA 0.213 0.213
80 0.061 0.000 NA NA 0.061 0.061
81 0.061 0.000 NA NA 0.061 0.061
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.146 0.000 NA NA 0.146 0.146
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.213 0.000 NA NA 0.213 0.213
89 0.049 0.000 NA NA 0.049 0.049
90 0.003 0.000 NA NA 0.003 0.003
91 0.062 0.000 NA NA 0.062 0.062
92 0.002 0.000 NA NA 0.002 0.002
93 0.005 0.000 NA NA 0.005 0.005
94 1.000 0.000 NA NA 1.000 1.000
95 0.000 0.000 NA NA 0.000 0.000
96 1.262 0.000 NA NA 1.262 1.262
97 1.100 0.000 NA NA 1.100 1.100
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 1.300 0.000 NA NA 1.300 1.300
103 1.175 0.000 NA NA 1.175 1.175
104 1.300 0.000 NA NA 1.300 1.300
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 int Epi 0.000000e+00 0.0000000000
2 a Matsmk ~> Epi -3.256588e-02 -0.0872133262
3 b Matagg ~> Epi -1.446987e-02 -0.0264216647
4 c FamScore ~> Epi 5.638497e-02 0.1240368759
5 d EduPar ~> Epi -3.844428e-02 -0.0539309112
6 e n_trauma ~> Epi 8.486733e-02 0.1163122343
7 Age ~> Epi -9.011778e-02 -0.1187813737
8 int_dis ~> Epi -7.447595e-02 -0.2077303937
9 medication ~> Epi -4.445644e-02 -0.1028146555
10 contraceptives ~> Epi -1.677936e-02 -0.0468014455
11 cigday_1 ~> Epi -1.109172e-01 -0.1663967798
12 V8 ~> Epi -8.888111e-02 -0.0366069081
13 f Matsmk ~> group 4.459326e-02 0.0049084130
14 g Matagg ~> group 1.435870e+00 0.1077612172
15 h FamScore ~> group 4.400941e-01 0.0397910044
16 i EduPar ~> group -3.687103e+00 -0.2125901823
17 j n_trauma ~> group 2.951804e+00 0.1662739888
18 Age ~> group -3.468158e+00 -0.1878834602
19 int_dis ~> group 8.022992e-01 0.0919755115
20 medication ~> group 8.750760e-01 0.0831798224
21 contraceptives ~> group 3.125000e-02 0.0035824979
22 cigday_1 ~> group 9.678493e+00 0.5967682503
23 V8 ~> group 1.269351e+01 0.2148757204
24 z Epi ~> group -6.499590e+00 -0.2671393335
26 Epi <-> Epi 2.334010e-02 0.8538634968
27 group <-> group 1.400533e-02 0.0008655314
28 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
29 Matsmk <-> Matagg 4.936709e-02 0.3693241433
30 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
31 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
32 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
33 Matsmk <-> Age -3.217300e-03 -0.0333441329
34 Matsmk <-> int_dis 2.151899e-02 0.1053910232
35 Matsmk <-> medication 4.113924e-03 0.0242997446
36 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
37 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
38 Matsmk <-> V8 2.928139e-03 0.0971189320
39 Matagg <-> Matagg 9.113924e-02 1.0000000000
40 Matagg <-> FamScore 3.417722e-02 0.3112715087
41 Matagg <-> EduPar -1.656118e-02 -0.2365241196
42 Matagg <-> n_trauma 7.233273e-03 0.1057402114
43 Matagg <-> Age 3.118694e-04 0.0047405101
44 Matagg <-> int_dis 3.291139e-02 0.2364027144
45 Matagg <-> medication 7.594937e-03 0.0657951695
46 Matagg <-> contraceptives 7.594937e-03 0.0545544726
47 Matagg <-> cigday_1 1.018987e-02 0.1360858260
48 Matagg <-> V8 8.272067e-04 0.0402393217
49 FamScore <-> FamScore 1.322785e-01 1.0000000000
50 FamScore <-> EduPar -2.948312e-02 -0.3495149022
51 FamScore <-> n_trauma 2.667269e-02 0.3236534989
52 FamScore <-> Age 3.636947e-03 0.0458878230
53 FamScore <-> int_dis 6.455696e-02 0.3849084009
54 FamScore <-> medication 4.430380e-03 0.0318580293
55 FamScore <-> contraceptives 5.822785e-02 0.3471722832
56 FamScore <-> cigday_1 4.381329e-02 0.4856887960
57 FamScore <-> V8 7.814844e-04 0.0315547719
58 EduPar <-> EduPar 5.379307e-02 1.0000000000
59 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
60 EduPar <-> Age 2.762108e-03 0.0546490350
61 EduPar <-> int_dis -1.909283e-02 -0.1785114035
62 EduPar <-> medication 1.017932e-02 0.1147832062
63 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
64 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
65 EduPar <-> V8 -8.860887e-06 -0.0005610523
66 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
67 n_trauma <-> Age 1.582278e-03 0.0320439451
68 n_trauma <-> int_dis 4.159132e-02 0.3980335009
69 n_trauma <-> medication 1.763110e-02 0.2034979577
70 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
71 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
72 n_trauma <-> V8 -4.694340e-04 -0.0304243917
73 Age <-> Age 4.748866e-02 1.0000000000
74 Age <-> int_dis 8.090259e-03 0.0805056484
75 Age <-> medication -1.655660e-03 -0.0198700345
76 Age <-> contraceptives 3.524124e-02 0.3506833348
77 Age <-> cigday_1 8.542355e-03 0.1580445206
78 Age <-> V8 -1.333633e-03 -0.0898732659
79 int_dis <-> int_dis 2.126582e-01 1.0000000000
80 int_dis <-> medication 6.075949e-02 0.3445843938
81 int_dis <-> contraceptives 6.075949e-02 0.2857142857
82 int_dis <-> cigday_1 4.449367e-02 0.3890038953
83 int_dis <-> V8 5.645344e-03 0.1797788722
84 medication <-> medication 1.462025e-01 1.0000000000
85 medication <-> contraceptives 3.544304e-02 0.2010075631
86 medication <-> cigday_1 3.275316e-03 0.0345360471
87 medication <-> V8 2.084232e-03 0.0800493604
88 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
89 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
90 contraceptives <-> V8 2.688833e-03 0.0856272484
91 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
92 cigday_1 <-> V8 1.509276e-03 0.0893623867
93 V8 <-> V8 4.636832e-03 1.0000000000
94 group <-> group 1.000000e+00 1.0000000000
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 1.262500e+00 2.8513745747
97 int Matagg 1.100000e+00 3.6436779343
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 1.300000e+00 2.8190466136
103 int medication 1.175000e+00 3.0729848569
104 int contraceptives 1.300000e+00 2.8190466136
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 TRUE 0
2 FALSE 1
3 FALSE 2
4 FALSE 3
5 FALSE 4
6 FALSE 5
7 FALSE 6
8 FALSE 7
9 FALSE 8
10 FALSE 9
11 FALSE 10
12 FALSE 11
13 FALSE 12
14 FALSE 13
15 FALSE 14
16 FALSE 15
17 FALSE 16
18 FALSE 17
19 FALSE 18
20 FALSE 19
21 FALSE 20
22 FALSE 21
23 FALSE 22
24 FALSE 23
26 FALSE 25
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 TRUE 0
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_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: exogenous variable(s) declared as ordered in data: Matsmk Matagg
int_dis medication contraceptives
Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan
WARNING: correlation between variables group and Epi is (nearly) 1.0############################
############################
Epi_all
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 128 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 25
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 0.227 0.227
Degrees of freedom 1 1
P-value (Chi-square) 0.634 0.634
Scaling correction factor 1.000
Shift parameter -0.000
simple second-order correction
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 0.000
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 10.125 9.268 1.092 0.275
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
25.000 0.001
chisq df
0.227 1.000
pvalue chisq.scaled
0.634 0.227
df.scaled pvalue.scaled
1.000 0.634
chisq.scaling.factor baseline.chisq
1.000 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 -0.205
nnfi rfi
-0.205 0.368
nfi pnfi
0.368 0.368
ifi rni
1.000 -0.205
cfi.scaled tli.scaled
1.000 -0.205
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
-0.205 NA
rfi.scaled nfi.scaled
0.368 0.368
ifi.scaled rni.scaled
1.000 -0.205
rni.robust rmsea
NA 0.000
rmsea.ci.lower rmsea.ci.upper
0.000 0.233
rmsea.pvalue rmsea.scaled
0.666 0.000
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.000 0.233
rmsea.pvalue.scaled rmsea.robust
0.666 NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
0.000 NA
rmsea.pvalue.robust rmr
NA 0.037
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
0.459 0.000
crmr crmr_nomean
0.593 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
1340.677 2314.866
gfi agfi
0.995 0.877
pgfi mfi
0.038 1.005
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~ Matsmk f
14 group ~ Matagg g
15 group ~ FamScore h
16 group ~ EduPar i
17 group ~ n_trauma j
18 group ~ Age
19 group ~ int_dis
20 group ~ medication
21 group ~ contraceptives
22 group ~ cigday_1
23 group ~ V8
24 group ~ Epi z
25 group | t1
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 group ~*~ group
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 0.000 0.000 NA NA 0.000 0.000
2 0.014 0.025 0.573 0.567 -0.035 0.064
3 0.023 0.038 0.590 0.555 -0.053 0.098
4 -0.037 0.042 -0.871 0.384 -0.119 0.046
5 -0.090 0.060 -1.514 0.130 -0.208 0.027
6 0.025 0.048 0.523 0.601 -0.069 0.119
7 0.034 0.064 0.534 0.593 -0.091 0.160
8 0.023 0.024 0.934 0.350 -0.025 0.071
9 0.007 0.030 0.248 0.804 -0.051 0.066
10 -0.016 0.027 -0.583 0.560 -0.069 0.037
11 0.022 0.052 0.425 0.671 -0.079 0.123
12 0.060 0.306 0.197 0.844 -0.540 0.660
13 0.078 1.107 0.071 0.944 -2.092 2.248
14 1.249 2.383 0.524 0.600 -3.421 5.919
15 0.526 1.914 0.275 0.783 -3.226 4.278
16 -2.316 2.797 -0.828 0.408 -7.798 3.165
17 2.090 1.387 1.506 0.132 -0.629 4.808
18 -3.306 1.889 -1.750 0.080 -7.007 0.396
19 1.003 0.828 1.212 0.226 -0.619 2.625
20 1.073 0.724 1.482 0.138 -0.346 2.492
21 0.336 0.720 0.466 0.641 -1.076 1.748
22 10.127 7.243 1.398 0.162 -4.070 24.323
23 12.524 14.939 0.838 0.402 -16.756 41.805
24 12.386 20.689 0.599 0.549 -28.163 52.936
25 10.125 9.268 1.092 0.275 -8.040 28.289
26 0.006 0.001 5.129 0.000 0.004 0.009
27 0.010 0.000 NA NA 0.010 0.010
28 0.196 0.000 NA NA 0.196 0.196
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.006 0.000 NA NA -0.006 -0.006
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.022 0.000 NA NA 0.022 0.022
35 0.004 0.000 NA NA 0.004 0.004
36 0.022 0.000 NA NA 0.022 0.022
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.091 0.000 NA NA 0.091 0.091
40 0.034 0.000 NA NA 0.034 0.034
41 -0.017 0.000 NA NA -0.017 -0.017
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.033 0.000 NA NA 0.033 0.033
45 0.008 0.000 NA NA 0.008 0.008
46 0.008 0.000 NA NA 0.008 0.008
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.132 0.000 NA NA 0.132 0.132
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.027 0.000 NA NA 0.027 0.027
52 0.004 0.000 NA NA 0.004 0.004
53 0.065 0.000 NA NA 0.065 0.065
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.044 0.000 NA NA 0.044 0.044
57 0.001 0.000 NA NA 0.001 0.001
58 0.054 0.000 NA NA 0.054 0.054
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.042 0.000 NA NA 0.042 0.042
69 0.018 0.000 NA NA 0.018 0.018
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.009 0.000 NA NA 0.009 0.009
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.213 0.000 NA NA 0.213 0.213
80 0.061 0.000 NA NA 0.061 0.061
81 0.061 0.000 NA NA 0.061 0.061
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.146 0.000 NA NA 0.146 0.146
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.213 0.000 NA NA 0.213 0.213
89 0.049 0.000 NA NA 0.049 0.049
90 0.003 0.000 NA NA 0.003 0.003
91 0.062 0.000 NA NA 0.062 0.062
92 0.002 0.000 NA NA 0.002 0.002
93 0.005 0.000 NA NA 0.005 0.005
94 1.000 0.000 NA NA 1.000 1.000
95 0.000 0.000 NA NA 0.000 0.000
96 1.262 0.000 NA NA 1.262 1.262
97 1.100 0.000 NA NA 1.100 1.100
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 1.300 0.000 NA NA 1.300 1.300
103 1.175 0.000 NA NA 1.175 1.175
104 1.300 0.000 NA NA 1.300 1.300
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 int Epi 0.000000e+00 0.0000000000
2 a Matsmk ~> Epi 1.438673e-02 0.0741179608
3 b Matagg ~> Epi 2.267876e-02 0.0796629353
4 c FamScore ~> Epi -3.652791e-02 -0.1545801451
5 d EduPar ~> Epi -9.048835e-02 -0.2441969196
6 e n_trauma ~> Epi 2.508125e-02 0.0661264965
7 Age ~> Epi 3.417168e-02 0.0866454257
8 int_dis ~> Epi 2.287156e-02 0.1227216492
9 medication ~> Epi 7.364855e-03 0.0327661907
10 contraceptives ~> Epi -1.577718e-02 -0.0846554322
11 cigday_1 ~> Epi 2.200542e-02 0.0635063412
12 V8 ~> Epi 6.028765e-02 0.0477664964
13 f Matsmk ~> group 7.806062e-02 0.0085921909
14 g Matagg ~> group 1.249013e+00 0.0937377367
15 h FamScore ~> group 5.260575e-01 0.0475633670
16 i EduPar ~> group -2.316420e+00 -0.1335596766
17 j n_trauma ~> group 2.089539e+00 0.1177029115
18 Age ~> group -3.305688e+00 -0.1790818579
19 int_dis ~> group 1.003070e+00 0.1149918509
20 medication ~> group 1.072802e+00 0.1019745517
21 contraceptives ~> group 3.357290e-01 0.0384879490
22 cigday_1 ~> group 1.012685e+01 0.6244133483
23 V8 ~> group 1.252446e+01 0.2120139219
24 z Epi ~> group 1.238624e+01 0.2646366933
26 Epi <-> Epi 6.453078e-03 0.8736461948
27 group <-> group 9.975117e-03 0.0006164638
28 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
29 Matsmk <-> Matagg 4.936709e-02 0.3693241433
30 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
31 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
32 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
33 Matsmk <-> Age -3.217300e-03 -0.0333441329
34 Matsmk <-> int_dis 2.151899e-02 0.1053910232
35 Matsmk <-> medication 4.113924e-03 0.0242997446
36 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
37 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
38 Matsmk <-> V8 2.928139e-03 0.0971189320
39 Matagg <-> Matagg 9.113924e-02 1.0000000000
40 Matagg <-> FamScore 3.417722e-02 0.3112715087
41 Matagg <-> EduPar -1.656118e-02 -0.2365241196
42 Matagg <-> n_trauma 7.233273e-03 0.1057402114
43 Matagg <-> Age 3.118694e-04 0.0047405101
44 Matagg <-> int_dis 3.291139e-02 0.2364027144
45 Matagg <-> medication 7.594937e-03 0.0657951695
46 Matagg <-> contraceptives 7.594937e-03 0.0545544726
47 Matagg <-> cigday_1 1.018987e-02 0.1360858260
48 Matagg <-> V8 8.272067e-04 0.0402393217
49 FamScore <-> FamScore 1.322785e-01 1.0000000000
50 FamScore <-> EduPar -2.948312e-02 -0.3495149022
51 FamScore <-> n_trauma 2.667269e-02 0.3236534989
52 FamScore <-> Age 3.636947e-03 0.0458878230
53 FamScore <-> int_dis 6.455696e-02 0.3849084009
54 FamScore <-> medication 4.430380e-03 0.0318580293
55 FamScore <-> contraceptives 5.822785e-02 0.3471722832
56 FamScore <-> cigday_1 4.381329e-02 0.4856887960
57 FamScore <-> V8 7.814844e-04 0.0315547719
58 EduPar <-> EduPar 5.379307e-02 1.0000000000
59 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
60 EduPar <-> Age 2.762108e-03 0.0546490350
61 EduPar <-> int_dis -1.909283e-02 -0.1785114035
62 EduPar <-> medication 1.017932e-02 0.1147832062
63 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
64 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
65 EduPar <-> V8 -8.860887e-06 -0.0005610523
66 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
67 n_trauma <-> Age 1.582278e-03 0.0320439451
68 n_trauma <-> int_dis 4.159132e-02 0.3980335009
69 n_trauma <-> medication 1.763110e-02 0.2034979577
70 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
71 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
72 n_trauma <-> V8 -4.694340e-04 -0.0304243917
73 Age <-> Age 4.748866e-02 1.0000000000
74 Age <-> int_dis 8.090259e-03 0.0805056484
75 Age <-> medication -1.655660e-03 -0.0198700345
76 Age <-> contraceptives 3.524124e-02 0.3506833348
77 Age <-> cigday_1 8.542355e-03 0.1580445206
78 Age <-> V8 -1.333633e-03 -0.0898732659
79 int_dis <-> int_dis 2.126582e-01 1.0000000000
80 int_dis <-> medication 6.075949e-02 0.3445843938
81 int_dis <-> contraceptives 6.075949e-02 0.2857142857
82 int_dis <-> cigday_1 4.449367e-02 0.3890038953
83 int_dis <-> V8 5.645344e-03 0.1797788722
84 medication <-> medication 1.462025e-01 1.0000000000
85 medication <-> contraceptives 3.544304e-02 0.2010075631
86 medication <-> cigday_1 3.275316e-03 0.0345360471
87 medication <-> V8 2.084232e-03 0.0800493604
88 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
89 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
90 contraceptives <-> V8 2.688833e-03 0.0856272484
91 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
92 cigday_1 <-> V8 1.509276e-03 0.0893623867
93 V8 <-> V8 4.636832e-03 1.0000000000
94 group <-> group 1.000000e+00 1.0000000000
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 1.262500e+00 2.8513745747
97 int Matagg 1.100000e+00 3.6436779343
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 1.300000e+00 2.8190466136
103 int medication 1.175000e+00 3.0729848569
104 int contraceptives 1.300000e+00 2.8190466136
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 TRUE 0
2 FALSE 1
3 FALSE 2
4 FALSE 3
5 FALSE 4
6 FALSE 5
7 FALSE 6
8 FALSE 7
9 FALSE 8
10 FALSE 9
11 FALSE 10
12 FALSE 11
13 FALSE 12
14 FALSE 13
15 FALSE 14
16 FALSE 15
17 FALSE 16
18 FALSE 17
19 FALSE 18
20 FALSE 19
21 FALSE 20
22 FALSE 21
23 FALSE 22
24 FALSE 23
26 FALSE 25
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 TRUE 0
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_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: exogenous variable(s) declared as ordered in data: Matsmk Matagg
int_dis medication contraceptives############################
############################
Epi_M2
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 118 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 25
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 26.124 26.124
Degrees of freedom 1 1
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.000
Shift parameter -0.000
simple second-order correction
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 0.000
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 10.125 9.268 1.092 0.275
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
25.000 0.163
chisq df
26.124 1.000
pvalue chisq.scaled
0.000 26.124
df.scaled pvalue.scaled
1.000 0.000
chisq.scaling.factor baseline.chisq
1.000 171.816
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
0.853 0.853
nnfi rfi
0.853 0.848
nfi pnfi
0.848 0.848
ifi rni
0.853 0.853
cfi.scaled tli.scaled
0.853 0.853
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
0.853 NA
rfi.scaled nfi.scaled
0.848 0.848
ifi.scaled rni.scaled
0.853 0.853
rni.robust rmsea
NA 0.564
rmsea.ci.lower rmsea.ci.upper
0.390 0.760
rmsea.pvalue rmsea.scaled
0.000 0.564
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.390 0.760
rmsea.pvalue.scaled rmsea.robust
0.000 NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
NA NA
rmsea.pvalue.robust rmr
NA 0.363
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
3.188 0.000
crmr crmr_nomean
4.116 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
12.617 21.064
gfi agfi
0.902 -1.544
pgfi mfi
0.035 0.853
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~ Matsmk f
14 group ~ Matagg g
15 group ~ FamScore h
16 group ~ EduPar i
17 group ~ n_trauma j
18 group ~ Age
19 group ~ int_dis
20 group ~ medication
21 group ~ contraceptives
22 group ~ cigday_1
23 group ~ V8
24 group ~ Epi z
25 group | t1
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 group ~*~ group
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 0.000 0.000 NA NA 0.000 0.000
2 0.009 0.042 0.217 0.829 -0.073 0.091
3 -0.017 0.064 -0.261 0.794 -0.141 0.108
4 -0.054 0.063 -0.853 0.393 -0.177 0.070
5 -0.010 0.092 -0.109 0.913 -0.190 0.170
6 0.018 0.096 0.188 0.851 -0.171 0.207
7 -0.042 0.101 -0.416 0.677 -0.239 0.155
8 -0.010 0.038 -0.255 0.799 -0.083 0.064
9 0.018 0.039 0.453 0.651 -0.060 0.095
10 0.074 0.053 1.390 0.165 -0.030 0.179
11 -0.058 0.091 -0.641 0.522 -0.236 0.120
12 -0.522 0.239 -2.187 0.029 -0.990 -0.054
13 0.335 1.112 0.301 0.764 -1.845 2.515
14 1.386 2.234 0.620 0.535 -2.992 5.765
15 -0.392 1.882 -0.208 0.835 -4.080 3.296
16 -3.524 2.208 -1.596 0.110 -7.851 0.803
17 2.558 1.304 1.962 0.050 0.003 5.113
18 -3.246 2.417 -1.343 0.179 -7.984 1.493
19 1.203 0.762 1.579 0.114 -0.290 2.696
20 1.319 0.808 1.632 0.103 -0.265 2.903
21 0.784 0.728 1.076 0.282 -0.644 2.212
22 9.894 7.152 1.383 0.167 -4.125 23.912
23 8.740 16.605 0.526 0.599 -23.805 41.285
24 -8.681 0.632 -13.736 0.000 -9.920 -7.443
25 10.125 9.268 1.092 0.275 -8.040 28.289
26 0.013 0.002 6.750 0.000 0.009 0.017
27 0.025 0.000 NA NA 0.025 0.025
28 0.196 0.000 NA NA 0.196 0.196
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.006 0.000 NA NA -0.006 -0.006
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.022 0.000 NA NA 0.022 0.022
35 0.004 0.000 NA NA 0.004 0.004
36 0.022 0.000 NA NA 0.022 0.022
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.091 0.000 NA NA 0.091 0.091
40 0.034 0.000 NA NA 0.034 0.034
41 -0.017 0.000 NA NA -0.017 -0.017
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.033 0.000 NA NA 0.033 0.033
45 0.008 0.000 NA NA 0.008 0.008
46 0.008 0.000 NA NA 0.008 0.008
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.132 0.000 NA NA 0.132 0.132
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.027 0.000 NA NA 0.027 0.027
52 0.004 0.000 NA NA 0.004 0.004
53 0.065 0.000 NA NA 0.065 0.065
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.044 0.000 NA NA 0.044 0.044
57 0.001 0.000 NA NA 0.001 0.001
58 0.054 0.000 NA NA 0.054 0.054
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.042 0.000 NA NA 0.042 0.042
69 0.018 0.000 NA NA 0.018 0.018
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.009 0.000 NA NA 0.009 0.009
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.213 0.000 NA NA 0.213 0.213
80 0.061 0.000 NA NA 0.061 0.061
81 0.061 0.000 NA NA 0.061 0.061
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.146 0.000 NA NA 0.146 0.146
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.213 0.000 NA NA 0.213 0.213
89 0.049 0.000 NA NA 0.049 0.049
90 0.003 0.000 NA NA 0.003 0.003
91 0.062 0.000 NA NA 0.062 0.062
92 0.002 0.000 NA NA 0.002 0.002
93 0.005 0.000 NA NA 0.005 0.005
94 1.000 0.000 NA NA 1.000 1.000
95 0.000 0.000 NA NA 0.000 0.000
96 1.262 0.000 NA NA 1.262 1.262
97 1.100 0.000 NA NA 1.100 1.100
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 1.300 0.000 NA NA 1.300 1.300
103 1.175 0.000 NA NA 1.175 1.175
104 1.300 0.000 NA NA 1.300 1.300
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 int Epi 0.000000e+00 0.0000000000
2 a Matsmk ~> Epi 9.021716e-03 0.0322271126
3 b Matagg ~> Epi -1.656132e-02 -0.0403369320
4 c FamScore ~> Epi -5.363064e-02 -0.1573666645
5 d EduPar ~> Epi -1.003048e-02 -0.0187689440
6 e n_trauma ~> Epi 1.817114e-02 0.0332184444
7 Age ~> Epi -4.183865e-02 -0.0735576351
8 int_dis ~> Epi -9.609875e-03 -0.0357531237
9 medication ~> Epi 1.786833e-02 0.0551209296
10 contraceptives ~> Epi 7.415667e-02 0.2758966699
11 cigday_1 ~> Epi -5.823698e-02 -0.1165352164
12 V8 ~> Epi -5.220008e-01 -0.2867722018
13 f Matsmk ~> group 3.345784e-01 0.0368272899
14 g Matagg ~> group 1.386144e+00 0.1040293512
15 h FamScore ~> group -3.919695e-01 -0.0354398363
16 i EduPar ~> group -3.524309e+00 -0.2032038309
17 j n_trauma ~> group 2.557950e+00 0.1440883617
18 Age ~> group -3.245643e+00 -0.1758289829
19 int_dis ~> group 1.202936e+00 0.1379045191
20 medication ~> group 1.319145e+00 0.1253905482
21 contraceptives ~> group 7.840853e-01 0.0898874799
22 cigday_1 ~> group 9.893837e+00 0.6100462324
23 V8 ~> group 8.739548e+00 0.1479430148
24 z Epi ~> group -8.681300e+00 -0.2675003898
26 Epi <-> Epi 1.294098e-02 0.8423209242
27 group <-> group 2.470311e-02 0.0015266558
28 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
29 Matsmk <-> Matagg 4.936709e-02 0.3693241433
30 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
31 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
32 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
33 Matsmk <-> Age -3.217300e-03 -0.0333441329
34 Matsmk <-> int_dis 2.151899e-02 0.1053910232
35 Matsmk <-> medication 4.113924e-03 0.0242997446
36 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
37 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
38 Matsmk <-> V8 2.928139e-03 0.0971189320
39 Matagg <-> Matagg 9.113924e-02 1.0000000000
40 Matagg <-> FamScore 3.417722e-02 0.3112715087
41 Matagg <-> EduPar -1.656118e-02 -0.2365241196
42 Matagg <-> n_trauma 7.233273e-03 0.1057402114
43 Matagg <-> Age 3.118694e-04 0.0047405101
44 Matagg <-> int_dis 3.291139e-02 0.2364027144
45 Matagg <-> medication 7.594937e-03 0.0657951695
46 Matagg <-> contraceptives 7.594937e-03 0.0545544726
47 Matagg <-> cigday_1 1.018987e-02 0.1360858260
48 Matagg <-> V8 8.272067e-04 0.0402393217
49 FamScore <-> FamScore 1.322785e-01 1.0000000000
50 FamScore <-> EduPar -2.948312e-02 -0.3495149022
51 FamScore <-> n_trauma 2.667269e-02 0.3236534989
52 FamScore <-> Age 3.636947e-03 0.0458878230
53 FamScore <-> int_dis 6.455696e-02 0.3849084009
54 FamScore <-> medication 4.430380e-03 0.0318580293
55 FamScore <-> contraceptives 5.822785e-02 0.3471722832
56 FamScore <-> cigday_1 4.381329e-02 0.4856887960
57 FamScore <-> V8 7.814844e-04 0.0315547719
58 EduPar <-> EduPar 5.379307e-02 1.0000000000
59 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
60 EduPar <-> Age 2.762108e-03 0.0546490350
61 EduPar <-> int_dis -1.909283e-02 -0.1785114035
62 EduPar <-> medication 1.017932e-02 0.1147832062
63 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
64 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
65 EduPar <-> V8 -8.860887e-06 -0.0005610523
66 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
67 n_trauma <-> Age 1.582278e-03 0.0320439451
68 n_trauma <-> int_dis 4.159132e-02 0.3980335009
69 n_trauma <-> medication 1.763110e-02 0.2034979577
70 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
71 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
72 n_trauma <-> V8 -4.694340e-04 -0.0304243917
73 Age <-> Age 4.748866e-02 1.0000000000
74 Age <-> int_dis 8.090259e-03 0.0805056484
75 Age <-> medication -1.655660e-03 -0.0198700345
76 Age <-> contraceptives 3.524124e-02 0.3506833348
77 Age <-> cigday_1 8.542355e-03 0.1580445206
78 Age <-> V8 -1.333633e-03 -0.0898732659
79 int_dis <-> int_dis 2.126582e-01 1.0000000000
80 int_dis <-> medication 6.075949e-02 0.3445843938
81 int_dis <-> contraceptives 6.075949e-02 0.2857142857
82 int_dis <-> cigday_1 4.449367e-02 0.3890038953
83 int_dis <-> V8 5.645344e-03 0.1797788722
84 medication <-> medication 1.462025e-01 1.0000000000
85 medication <-> contraceptives 3.544304e-02 0.2010075631
86 medication <-> cigday_1 3.275316e-03 0.0345360471
87 medication <-> V8 2.084232e-03 0.0800493604
88 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
89 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
90 contraceptives <-> V8 2.688833e-03 0.0856272484
91 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
92 cigday_1 <-> V8 1.509276e-03 0.0893623867
93 V8 <-> V8 4.636832e-03 1.0000000000
94 group <-> group 1.000000e+00 1.0000000000
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 1.262500e+00 2.8513745747
97 int Matagg 1.100000e+00 3.6436779343
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 1.300000e+00 2.8190466136
103 int medication 1.175000e+00 3.0729848569
104 int contraceptives 1.300000e+00 2.8190466136
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 TRUE 0
2 FALSE 1
3 FALSE 2
4 FALSE 3
5 FALSE 4
6 FALSE 5
7 FALSE 6
8 FALSE 7
9 FALSE 8
10 FALSE 9
11 FALSE 10
12 FALSE 11
13 FALSE 12
14 FALSE 13
15 FALSE 14
16 FALSE 15
17 FALSE 16
18 FALSE 17
19 FALSE 18
20 FALSE 19
21 FALSE 20
22 FALSE 21
23 FALSE 22
24 FALSE 23
26 FALSE 25
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 TRUE 0
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_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: exogenous variable(s) declared as ordered in data: Matsmk Matagg
int_dis medication contraceptives############################
############################
Epi_M15
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 119 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 25
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 11.201 11.201
Degrees of freedom 1 1
P-value (Chi-square) 0.001 0.001
Scaling correction factor 1.000
Shift parameter 0.000
simple second-order correction
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 0.000
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 10.125 9.268 1.092 0.275
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
25.000 0.070
chisq df
11.201 1.000
pvalue chisq.scaled
0.001 11.201
df.scaled pvalue.scaled
1.000 0.001
chisq.scaling.factor baseline.chisq
1.000 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
0.648 0.648
nnfi rfi
0.648 0.627
nfi pnfi
0.627 0.627
ifi rni
0.648 0.648
cfi.scaled tli.scaled
0.648 0.648
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
0.648 NA
rfi.scaled nfi.scaled
0.627 0.627
ifi.scaled rni.scaled
0.648 0.648
rni.robust rmsea
NA 0.359
rmsea.ci.lower rmsea.ci.upper
0.191 0.562
rmsea.pvalue rmsea.scaled
0.002 0.359
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.191 0.562
rmsea.pvalue.scaled rmsea.robust
0.002 NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
NA NA
rmsea.pvalue.robust rmr
NA 0.284
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
1.788 0.000
crmr crmr_nomean
2.308 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
28.094 47.797
gfi agfi
0.887 -1.941
pgfi mfi
0.034 0.937
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~ Matsmk f
14 group ~ Matagg g
15 group ~ FamScore h
16 group ~ EduPar i
17 group ~ n_trauma j
18 group ~ Age
19 group ~ int_dis
20 group ~ medication
21 group ~ contraceptives
22 group ~ cigday_1
23 group ~ V8
24 group ~ Epi z
25 group | t1
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 group ~*~ group
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 0.000 0.000 NA NA 0.000 0.000
2 -0.055 0.057 -0.964 0.335 -0.168 0.057
3 0.074 0.106 0.695 0.487 -0.134 0.282
4 0.113 0.082 1.378 0.168 -0.048 0.273
5 0.229 0.104 2.213 0.027 0.026 0.432
6 -0.062 0.089 -0.701 0.483 -0.236 0.112
7 -0.088 0.121 -0.727 0.467 -0.325 0.149
8 -0.065 0.047 -1.378 0.168 -0.157 0.027
9 -0.024 0.056 -0.433 0.665 -0.133 0.085
10 0.032 0.055 0.579 0.562 -0.076 0.140
11 -0.052 0.113 -0.458 0.647 -0.274 0.170
12 0.007 0.271 0.026 0.979 -0.524 0.539
13 -0.073 1.061 -0.069 0.945 -2.152 2.006
14 1.971 2.351 0.838 0.402 -2.637 6.579
15 0.746 1.901 0.392 0.695 -2.980 4.472
16 -2.071 2.241 -0.924 0.355 -6.462 2.321
17 2.029 1.289 1.574 0.115 -0.498 4.556
18 -3.407 2.353 -1.448 0.148 -8.018 1.204
19 0.899 0.769 1.169 0.242 -0.608 2.406
20 1.021 0.729 1.400 0.161 -0.408 2.449
21 0.331 0.694 0.476 0.634 -1.030 1.691
22 10.090 7.086 1.424 0.154 -3.798 23.979
23 13.314 16.711 0.797 0.426 -19.439 46.066
24 -5.965 1.303 -4.578 0.000 -8.519 -3.411
25 10.125 9.268 1.092 0.275 -8.040 28.289
26 0.025 0.005 5.557 0.000 0.016 0.034
27 0.102 0.000 NA NA 0.102 0.102
28 0.196 0.000 NA NA 0.196 0.196
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.006 0.000 NA NA -0.006 -0.006
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.022 0.000 NA NA 0.022 0.022
35 0.004 0.000 NA NA 0.004 0.004
36 0.022 0.000 NA NA 0.022 0.022
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.091 0.000 NA NA 0.091 0.091
40 0.034 0.000 NA NA 0.034 0.034
41 -0.017 0.000 NA NA -0.017 -0.017
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.033 0.000 NA NA 0.033 0.033
45 0.008 0.000 NA NA 0.008 0.008
46 0.008 0.000 NA NA 0.008 0.008
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.132 0.000 NA NA 0.132 0.132
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.027 0.000 NA NA 0.027 0.027
52 0.004 0.000 NA NA 0.004 0.004
53 0.065 0.000 NA NA 0.065 0.065
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.044 0.000 NA NA 0.044 0.044
57 0.001 0.000 NA NA 0.001 0.001
58 0.054 0.000 NA NA 0.054 0.054
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.042 0.000 NA NA 0.042 0.042
69 0.018 0.000 NA NA 0.018 0.018
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.009 0.000 NA NA 0.009 0.009
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.213 0.000 NA NA 0.213 0.213
80 0.061 0.000 NA NA 0.061 0.061
81 0.061 0.000 NA NA 0.061 0.061
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.146 0.000 NA NA 0.146 0.146
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.213 0.000 NA NA 0.213 0.213
89 0.049 0.000 NA NA 0.049 0.049
90 0.003 0.000 NA NA 0.003 0.003
91 0.062 0.000 NA NA 0.062 0.062
92 0.002 0.000 NA NA 0.002 0.002
93 0.005 0.000 NA NA 0.005 0.005
94 1.000 0.000 NA NA 1.000 1.000
95 0.000 0.000 NA NA 0.000 0.000
96 1.262 0.000 NA NA 1.262 1.262
97 1.100 0.000 NA NA 1.100 1.100
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 1.300 0.000 NA NA 1.300 1.300
103 1.175 0.000 NA NA 1.175 1.175
104 1.300 0.000 NA NA 1.300 1.300
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 int Epi 0.000000e+00 0.0000000000
2 a Matsmk ~> Epi -5.522110e-02 -0.1398958703
3 b Matagg ~> Epi 7.387035e-02 0.1275984992
4 c FamScore ~> Epi 1.127425e-01 0.2346145223
5 d EduPar ~> Epi 2.291129e-01 0.3040432826
6 e n_trauma ~> Epi -6.219036e-02 -0.0806283423
7 Age ~> Epi -8.796430e-02 -0.1096791636
8 int_dis ~> Epi -6.492648e-02 -0.1713111729
9 medication ~> Epi -2.404003e-02 -0.0525938818
10 contraceptives ~> Epi 3.188492e-02 0.0841296471
11 cigday_1 ~> Epi -5.184015e-02 -0.0735685414
12 V8 ~> Epi 7.148755e-03 0.0027852478
13 f Matsmk ~> group -7.313450e-02 -0.0080499691
14 g Matagg ~> group 1.970553e+00 0.1478888885
15 h FamScore ~> group 7.461208e-01 0.0674603441
16 i EduPar ~> group -2.070577e+00 -0.1193848885
17 j n_trauma ~> group 2.029237e+00 0.1143061490
18 Age ~> group -3.407134e+00 -0.1845776020
19 int_dis ~> group 8.990774e-01 0.1030701633
20 medication ~> group 1.020626e+00 0.0970150375
21 contraceptives ~> group 3.305018e-01 0.0378887036
22 cigday_1 ~> group 1.009018e+01 0.6221527723
23 V8 ~> group 1.331385e+01 0.2253767836
24 z Epi ~> group -5.964978e+00 -0.2591677296
26 Epi <-> Epi 2.524032e-02 0.8263046858
27 group <-> group 1.019252e-01 0.0062989945
28 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
29 Matsmk <-> Matagg 4.936709e-02 0.3693241433
30 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
31 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
32 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
33 Matsmk <-> Age -3.217300e-03 -0.0333441329
34 Matsmk <-> int_dis 2.151899e-02 0.1053910232
35 Matsmk <-> medication 4.113924e-03 0.0242997446
36 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
37 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
38 Matsmk <-> V8 2.928139e-03 0.0971189320
39 Matagg <-> Matagg 9.113924e-02 1.0000000000
40 Matagg <-> FamScore 3.417722e-02 0.3112715087
41 Matagg <-> EduPar -1.656118e-02 -0.2365241196
42 Matagg <-> n_trauma 7.233273e-03 0.1057402114
43 Matagg <-> Age 3.118694e-04 0.0047405101
44 Matagg <-> int_dis 3.291139e-02 0.2364027144
45 Matagg <-> medication 7.594937e-03 0.0657951695
46 Matagg <-> contraceptives 7.594937e-03 0.0545544726
47 Matagg <-> cigday_1 1.018987e-02 0.1360858260
48 Matagg <-> V8 8.272067e-04 0.0402393217
49 FamScore <-> FamScore 1.322785e-01 1.0000000000
50 FamScore <-> EduPar -2.948312e-02 -0.3495149022
51 FamScore <-> n_trauma 2.667269e-02 0.3236534989
52 FamScore <-> Age 3.636947e-03 0.0458878230
53 FamScore <-> int_dis 6.455696e-02 0.3849084009
54 FamScore <-> medication 4.430380e-03 0.0318580293
55 FamScore <-> contraceptives 5.822785e-02 0.3471722832
56 FamScore <-> cigday_1 4.381329e-02 0.4856887960
57 FamScore <-> V8 7.814844e-04 0.0315547719
58 EduPar <-> EduPar 5.379307e-02 1.0000000000
59 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
60 EduPar <-> Age 2.762108e-03 0.0546490350
61 EduPar <-> int_dis -1.909283e-02 -0.1785114035
62 EduPar <-> medication 1.017932e-02 0.1147832062
63 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
64 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
65 EduPar <-> V8 -8.860887e-06 -0.0005610523
66 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
67 n_trauma <-> Age 1.582278e-03 0.0320439451
68 n_trauma <-> int_dis 4.159132e-02 0.3980335009
69 n_trauma <-> medication 1.763110e-02 0.2034979577
70 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
71 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
72 n_trauma <-> V8 -4.694340e-04 -0.0304243917
73 Age <-> Age 4.748866e-02 1.0000000000
74 Age <-> int_dis 8.090259e-03 0.0805056484
75 Age <-> medication -1.655660e-03 -0.0198700345
76 Age <-> contraceptives 3.524124e-02 0.3506833348
77 Age <-> cigday_1 8.542355e-03 0.1580445206
78 Age <-> V8 -1.333633e-03 -0.0898732659
79 int_dis <-> int_dis 2.126582e-01 1.0000000000
80 int_dis <-> medication 6.075949e-02 0.3445843938
81 int_dis <-> contraceptives 6.075949e-02 0.2857142857
82 int_dis <-> cigday_1 4.449367e-02 0.3890038953
83 int_dis <-> V8 5.645344e-03 0.1797788722
84 medication <-> medication 1.462025e-01 1.0000000000
85 medication <-> contraceptives 3.544304e-02 0.2010075631
86 medication <-> cigday_1 3.275316e-03 0.0345360471
87 medication <-> V8 2.084232e-03 0.0800493604
88 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
89 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
90 contraceptives <-> V8 2.688833e-03 0.0856272484
91 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
92 cigday_1 <-> V8 1.509276e-03 0.0893623867
93 V8 <-> V8 4.636832e-03 1.0000000000
94 group <-> group 1.000000e+00 1.0000000000
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 1.262500e+00 2.8513745747
97 int Matagg 1.100000e+00 3.6436779343
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 1.300000e+00 2.8190466136
103 int medication 1.175000e+00 3.0729848569
104 int contraceptives 1.300000e+00 2.8190466136
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 TRUE 0
2 FALSE 1
3 FALSE 2
4 FALSE 3
5 FALSE 4
6 FALSE 5
7 FALSE 6
8 FALSE 7
9 FALSE 8
10 FALSE 9
11 FALSE 10
12 FALSE 11
13 FALSE 12
14 FALSE 13
15 FALSE 14
16 FALSE 15
17 FALSE 16
18 FALSE 17
19 FALSE 18
20 FALSE 19
21 FALSE 20
22 FALSE 21
23 FALSE 22
24 FALSE 23
26 FALSE 25
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 TRUE 0
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_data_full(data = data, group = group, cluster = cluster, :
lavaan WARNING: exogenous variable(s) declared as ordered in data: Matsmk Matagg
int_dis medication contraceptives
Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan
WARNING: correlation between variables group and Epi is (nearly) 1.0############################
############################
Epi_M_all
############################
############################
##Mediation Model ##
lavaan 0.6-7 ended normally after 123 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 25
Used Total
Number of observations 80 99
Model Test User Model:
Standard Robust
Test Statistic 0.014 0.014
Degrees of freedom 1 1
P-value (Chi-square) 0.906 0.906
Scaling correction factor 1.000
Shift parameter -0.000
simple second-order correction
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 0.000
.group 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|)
group|t1 10.125 9.268 1.092 0.275
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
25.000 0.000
chisq df
0.014 1.000
pvalue chisq.scaled
0.906 0.014
df.scaled pvalue.scaled
1.000 0.906
chisq.scaling.factor baseline.chisq
1.000 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 1.199
nnfi rfi
1.199 0.998
nfi pnfi
0.998 0.998
ifi rni
1.199 1.199
cfi.scaled tli.scaled
1.000 1.199
cfi.robust tli.robust
NA NA
nnfi.scaled nnfi.robust
1.199 NA
rfi.scaled nfi.scaled
0.998 0.998
ifi.scaled rni.scaled
1.199 1.199
rni.robust rmsea
NA 0.000
rmsea.ci.lower rmsea.ci.upper
0.000 0.127
rmsea.pvalue rmsea.scaled
0.915 0.000
rmsea.ci.lower.scaled rmsea.ci.upper.scaled
0.000 0.127
rmsea.pvalue.scaled rmsea.robust
0.915 NA
rmsea.ci.lower.robust rmsea.ci.upper.robust
0.000 NA
rmsea.pvalue.robust rmr
NA 0.031
rmr_nomean srmr
0.000 0.000
srmr_bentler srmr_bentler_nomean
0.122 0.000
crmr crmr_nomean
0.157 0.000
srmr_mplus srmr_mplus_nomean
NA NA
cn_05 cn_01
21767.257 37595.276
gfi agfi
1.000 0.992
pgfi mfi
0.038 1.006
lhs op rhs label
1 Epi ~1
2 Epi ~ Matsmk a
3 Epi ~ Matagg b
4 Epi ~ FamScore c
5 Epi ~ EduPar d
6 Epi ~ n_trauma e
7 Epi ~ Age
8 Epi ~ int_dis
9 Epi ~ medication
10 Epi ~ contraceptives
11 Epi ~ cigday_1
12 Epi ~ V8
13 group ~ Matsmk f
14 group ~ Matagg g
15 group ~ FamScore h
16 group ~ EduPar i
17 group ~ n_trauma j
18 group ~ Age
19 group ~ int_dis
20 group ~ medication
21 group ~ contraceptives
22 group ~ cigday_1
23 group ~ V8
24 group ~ Epi z
25 group | t1
26 Epi ~~ Epi
27 group ~~ group
28 Matsmk ~~ Matsmk
29 Matsmk ~~ Matagg
30 Matsmk ~~ FamScore
31 Matsmk ~~ EduPar
32 Matsmk ~~ n_trauma
33 Matsmk ~~ Age
34 Matsmk ~~ int_dis
35 Matsmk ~~ medication
36 Matsmk ~~ contraceptives
37 Matsmk ~~ cigday_1
38 Matsmk ~~ V8
39 Matagg ~~ Matagg
40 Matagg ~~ FamScore
41 Matagg ~~ EduPar
42 Matagg ~~ n_trauma
43 Matagg ~~ Age
44 Matagg ~~ int_dis
45 Matagg ~~ medication
46 Matagg ~~ contraceptives
47 Matagg ~~ cigday_1
48 Matagg ~~ V8
49 FamScore ~~ FamScore
50 FamScore ~~ EduPar
51 FamScore ~~ n_trauma
52 FamScore ~~ Age
53 FamScore ~~ int_dis
54 FamScore ~~ medication
55 FamScore ~~ contraceptives
56 FamScore ~~ cigday_1
57 FamScore ~~ V8
58 EduPar ~~ EduPar
59 EduPar ~~ n_trauma
60 EduPar ~~ Age
61 EduPar ~~ int_dis
62 EduPar ~~ medication
63 EduPar ~~ contraceptives
64 EduPar ~~ cigday_1
65 EduPar ~~ V8
66 n_trauma ~~ n_trauma
67 n_trauma ~~ Age
68 n_trauma ~~ int_dis
69 n_trauma ~~ medication
70 n_trauma ~~ contraceptives
71 n_trauma ~~ cigday_1
72 n_trauma ~~ V8
73 Age ~~ Age
74 Age ~~ int_dis
75 Age ~~ medication
76 Age ~~ contraceptives
77 Age ~~ cigday_1
78 Age ~~ V8
79 int_dis ~~ int_dis
80 int_dis ~~ medication
81 int_dis ~~ contraceptives
82 int_dis ~~ cigday_1
83 int_dis ~~ V8
84 medication ~~ medication
85 medication ~~ contraceptives
86 medication ~~ cigday_1
87 medication ~~ V8
88 contraceptives ~~ contraceptives
89 contraceptives ~~ cigday_1
90 contraceptives ~~ V8
91 cigday_1 ~~ cigday_1
92 cigday_1 ~~ V8
93 V8 ~~ V8
94 group ~*~ group
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 0.000 0.000 NA NA 0.000 0.000
2 0.025 0.082 0.301 0.764 -0.136 0.185
3 0.132 0.124 1.068 0.285 -0.110 0.374
4 -0.102 0.109 -0.934 0.350 -0.315 0.112
5 -0.281 0.169 -1.658 0.097 -0.613 0.051
6 0.074 0.126 0.589 0.556 -0.173 0.322
7 0.122 0.179 0.679 0.497 -0.230 0.473
8 0.085 0.074 1.148 0.251 -0.060 0.231
9 0.050 0.080 0.623 0.533 -0.107 0.207
10 -0.074 0.084 -0.872 0.383 -0.239 0.092
11 0.168 0.140 1.205 0.228 -0.105 0.442
12 0.285 1.060 0.268 0.788 -1.793 2.362
13 0.161 1.017 0.158 0.874 -1.833 2.155
14 1.018 2.327 0.437 0.662 -3.543 5.578
15 0.468 1.959 0.239 0.811 -3.372 4.307
16 -2.348 2.257 -1.040 0.298 -6.772 2.076
17 2.111 1.316 1.604 0.109 -0.469 4.691
18 -3.354 2.203 -1.523 0.128 -7.671 0.963
19 0.955 0.779 1.227 0.220 -0.571 2.482
20 0.970 0.728 1.333 0.183 -0.457 2.396
21 0.426 0.665 0.641 0.522 -0.877 1.729
22 9.746 7.183 1.357 0.175 -4.333 23.825
23 12.167 14.326 0.849 0.396 -15.911 40.245
24 3.880 1.952 1.988 0.047 0.054 7.706
25 10.125 9.268 1.092 0.275 -8.040 28.289
26 0.065 0.018 3.666 0.000 0.030 0.100
27 0.016 0.000 NA NA 0.016 0.016
28 0.196 0.000 NA NA 0.196 0.196
29 0.049 0.000 NA NA 0.049 0.049
30 -0.009 0.000 NA NA -0.009 -0.009
31 -0.006 0.000 NA NA -0.006 -0.006
32 0.007 0.000 NA NA 0.007 0.007
33 -0.003 0.000 NA NA -0.003 -0.003
34 0.022 0.000 NA NA 0.022 0.022
35 0.004 0.000 NA NA 0.004 0.004
36 0.022 0.000 NA NA 0.022 0.022
37 0.017 0.000 NA NA 0.017 0.017
38 0.003 0.000 NA NA 0.003 0.003
39 0.091 0.000 NA NA 0.091 0.091
40 0.034 0.000 NA NA 0.034 0.034
41 -0.017 0.000 NA NA -0.017 -0.017
42 0.007 0.000 NA NA 0.007 0.007
43 0.000 0.000 NA NA 0.000 0.000
44 0.033 0.000 NA NA 0.033 0.033
45 0.008 0.000 NA NA 0.008 0.008
46 0.008 0.000 NA NA 0.008 0.008
47 0.010 0.000 NA NA 0.010 0.010
48 0.001 0.000 NA NA 0.001 0.001
49 0.132 0.000 NA NA 0.132 0.132
50 -0.029 0.000 NA NA -0.029 -0.029
51 0.027 0.000 NA NA 0.027 0.027
52 0.004 0.000 NA NA 0.004 0.004
53 0.065 0.000 NA NA 0.065 0.065
54 0.004 0.000 NA NA 0.004 0.004
55 0.058 0.000 NA NA 0.058 0.058
56 0.044 0.000 NA NA 0.044 0.044
57 0.001 0.000 NA NA 0.001 0.001
58 0.054 0.000 NA NA 0.054 0.054
59 -0.008 0.000 NA NA -0.008 -0.008
60 0.003 0.000 NA NA 0.003 0.003
61 -0.019 0.000 NA NA -0.019 -0.019
62 0.010 0.000 NA NA 0.010 0.010
63 -0.013 0.000 NA NA -0.013 -0.013
64 -0.015 0.000 NA NA -0.015 -0.015
65 0.000 0.000 NA NA 0.000 0.000
66 0.051 0.000 NA NA 0.051 0.051
67 0.002 0.000 NA NA 0.002 0.002
68 0.042 0.000 NA NA 0.042 0.042
69 0.018 0.000 NA NA 0.018 0.018
70 0.018 0.000 NA NA 0.018 0.018
71 0.021 0.000 NA NA 0.021 0.021
72 0.000 0.000 NA NA 0.000 0.000
73 0.047 0.000 NA NA 0.047 0.047
74 0.008 0.000 NA NA 0.008 0.008
75 -0.002 0.000 NA NA -0.002 -0.002
76 0.035 0.000 NA NA 0.035 0.035
77 0.009 0.000 NA NA 0.009 0.009
78 -0.001 0.000 NA NA -0.001 -0.001
79 0.213 0.000 NA NA 0.213 0.213
80 0.061 0.000 NA NA 0.061 0.061
81 0.061 0.000 NA NA 0.061 0.061
82 0.044 0.000 NA NA 0.044 0.044
83 0.006 0.000 NA NA 0.006 0.006
84 0.146 0.000 NA NA 0.146 0.146
85 0.035 0.000 NA NA 0.035 0.035
86 0.003 0.000 NA NA 0.003 0.003
87 0.002 0.000 NA NA 0.002 0.002
88 0.213 0.000 NA NA 0.213 0.213
89 0.049 0.000 NA NA 0.049 0.049
90 0.003 0.000 NA NA 0.003 0.003
91 0.062 0.000 NA NA 0.062 0.062
92 0.002 0.000 NA NA 0.002 0.002
93 0.005 0.000 NA NA 0.005 0.005
94 1.000 0.000 NA NA 1.000 1.000
95 0.000 0.000 NA NA 0.000 0.000
96 1.262 0.000 NA NA 1.262 1.262
97 1.100 0.000 NA NA 1.100 1.100
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 1.300 0.000 NA NA 1.300 1.300
103 1.175 0.000 NA NA 1.175 1.175
104 1.300 0.000 NA NA 1.300 1.300
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 int Epi 0.000000e+00 0.0000000000
2 a Matsmk ~> Epi 2.458187e-02 0.0385922837
3 b Matagg ~> Epi 1.320288e-01 0.1413284916
4 c FamScore ~> Epi -1.016065e-01 -0.1310311343
5 d EduPar ~> Epi -2.807576e-01 -0.2308889336
6 e n_trauma ~> Epi 7.446007e-02 0.0598237809
7 Age ~> Epi 1.216014e-01 0.0939597536
8 int_dis ~> Epi 8.529375e-02 0.1394654858
9 medication ~> Epi 5.004596e-02 0.0678507995
10 contraceptives ~> Epi -7.362480e-02 -0.1203853560
11 cigday_1 ~> Epi 1.683722e-01 0.1480750999
12 V8 ~> Epi 2.846111e-01 0.0687180767
13 f Matsmk ~> group 1.608808e-01 0.0177082746
14 g Matagg ~> group 1.017648e+00 0.0763739246
15 h FamScore ~> group 4.678460e-01 0.0423001889
16 i EduPar ~> group -2.347896e+00 -0.1353744724
17 j n_trauma ~> group 2.111297e+00 0.1189285662
18 Age ~> group -3.354240e+00 -0.1817121386
19 int_dis ~> group 9.554240e-01 0.1095297317
20 medication ~> group 9.698471e-01 0.0921882418
21 contraceptives ~> group 4.259721e-01 0.0488334122
22 cigday_1 ~> group 9.746128e+00 0.6009386188
23 V8 ~> group 1.216691e+01 0.2059613011
24 z Epi ~> group 3.879985e+00 0.2720297660
26 Epi <-> Epi 6.534223e-02 0.8215062284
27 group <-> group 1.631936e-02 0.0010085389
28 Matsmk <-> Matsmk 1.960443e-01 1.0000000000
29 Matsmk <-> Matagg 4.936709e-02 0.3693241433
30 Matsmk <-> FamScore -9.177215e-03 -0.0569887592
31 Matsmk <-> EduPar -5.564346e-03 -0.0541843459
32 Matsmk <-> n_trauma 7.459313e-03 0.0743497863
33 Matsmk <-> Age -3.217300e-03 -0.0333441329
34 Matsmk <-> int_dis 2.151899e-02 0.1053910232
35 Matsmk <-> medication 4.113924e-03 0.0242997446
36 Matsmk <-> contraceptives 2.151899e-02 0.1053910232
37 Matsmk <-> cigday_1 1.693829e-02 0.1542372547
38 Matsmk <-> V8 2.928139e-03 0.0971189320
39 Matagg <-> Matagg 9.113924e-02 1.0000000000
40 Matagg <-> FamScore 3.417722e-02 0.3112715087
41 Matagg <-> EduPar -1.656118e-02 -0.2365241196
42 Matagg <-> n_trauma 7.233273e-03 0.1057402114
43 Matagg <-> Age 3.118694e-04 0.0047405101
44 Matagg <-> int_dis 3.291139e-02 0.2364027144
45 Matagg <-> medication 7.594937e-03 0.0657951695
46 Matagg <-> contraceptives 7.594937e-03 0.0545544726
47 Matagg <-> cigday_1 1.018987e-02 0.1360858260
48 Matagg <-> V8 8.272067e-04 0.0402393217
49 FamScore <-> FamScore 1.322785e-01 1.0000000000
50 FamScore <-> EduPar -2.948312e-02 -0.3495149022
51 FamScore <-> n_trauma 2.667269e-02 0.3236534989
52 FamScore <-> Age 3.636947e-03 0.0458878230
53 FamScore <-> int_dis 6.455696e-02 0.3849084009
54 FamScore <-> medication 4.430380e-03 0.0318580293
55 FamScore <-> contraceptives 5.822785e-02 0.3471722832
56 FamScore <-> cigday_1 4.381329e-02 0.4856887960
57 FamScore <-> V8 7.814844e-04 0.0315547719
58 EduPar <-> EduPar 5.379307e-02 1.0000000000
59 EduPar <-> n_trauma -8.024412e-03 -0.1526891136
60 EduPar <-> Age 2.762108e-03 0.0546490350
61 EduPar <-> int_dis -1.909283e-02 -0.1785114035
62 EduPar <-> medication 1.017932e-02 0.1147832062
63 EduPar <-> contraceptives -1.329114e-02 -0.1242676068
64 EduPar <-> cigday_1 -1.493803e-02 -0.2596730517
65 EduPar <-> V8 -8.860887e-06 -0.0005610523
66 n_trauma <-> n_trauma 5.134332e-02 1.0000000000
67 n_trauma <-> Age 1.582278e-03 0.0320439451
68 n_trauma <-> int_dis 4.159132e-02 0.3980335009
69 n_trauma <-> medication 1.763110e-02 0.2034979577
70 n_trauma <-> contraceptives 1.808318e-02 0.1730580439
71 n_trauma <-> cigday_1 2.128165e-02 0.3786692420
72 n_trauma <-> V8 -4.694340e-04 -0.0304243917
73 Age <-> Age 4.748866e-02 1.0000000000
74 Age <-> int_dis 8.090259e-03 0.0805056484
75 Age <-> medication -1.655660e-03 -0.0198700345
76 Age <-> contraceptives 3.524124e-02 0.3506833348
77 Age <-> cigday_1 8.542355e-03 0.1580445206
78 Age <-> V8 -1.333633e-03 -0.0898732659
79 int_dis <-> int_dis 2.126582e-01 1.0000000000
80 int_dis <-> medication 6.075949e-02 0.3445843938
81 int_dis <-> contraceptives 6.075949e-02 0.2857142857
82 int_dis <-> cigday_1 4.449367e-02 0.3890038953
83 int_dis <-> V8 5.645344e-03 0.1797788722
84 medication <-> medication 1.462025e-01 1.0000000000
85 medication <-> contraceptives 3.544304e-02 0.2010075631
86 medication <-> cigday_1 3.275316e-03 0.0345360471
87 medication <-> V8 2.084232e-03 0.0800493604
88 contraceptives <-> contraceptives 2.126582e-01 1.0000000000
89 contraceptives <-> cigday_1 4.892405e-02 0.4277382803
90 contraceptives <-> V8 2.688833e-03 0.0856272484
91 cigday_1 <-> cigday_1 6.151859e-02 1.0000000000
92 cigday_1 <-> V8 1.509276e-03 0.0893623867
93 V8 <-> V8 4.636832e-03 1.0000000000
94 group <-> group 1.000000e+00 1.0000000000
95 int group 0.000000e+00 0.0000000000
96 int Matsmk 1.262500e+00 2.8513745747
97 int Matagg 1.100000e+00 3.6436779343
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 1.300000e+00 2.8190466136
103 int medication 1.175000e+00 3.0729848569
104 int contraceptives 1.300000e+00 2.8190466136
105 int cigday_1 1.243750e-01 0.5014526157
106 int V8 5.286908e-01 7.7640983340
fixed par
1 TRUE 0
2 FALSE 1
3 FALSE 2
4 FALSE 3
5 FALSE 4
6 FALSE 5
7 FALSE 6
8 FALSE 7
9 FALSE 8
10 FALSE 9
11 FALSE 10
12 FALSE 11
13 FALSE 12
14 FALSE 13
15 FALSE 14
16 FALSE 15
17 FALSE 16
18 FALSE 17
19 FALSE 18
20 FALSE 19
21 FALSE 20
22 FALSE 21
23 FALSE 22
24 FALSE 23
26 FALSE 25
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 TRUE 0
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