summary(aov2.a)
summary(aov2.b)
##compare the aggregated vs non-aggergated versions:
aov2.a <- aov(rt~cond+Error(subj/cond),data=data2)
aov2.b <- aov(rt~cond+Error(subj/cond),data=data2b)
data2b  <- aggregate(data2$rt,list(subj=data2$subj,cond=data2$cond),mean)
data2b$rt <- data2b$x
##compare the aggregated vs non-aggergated versions:
aov2.a <- aov(rt~cond+Error(subj/cond),data=data2)
aov2.b <- aov(rt~cond+Error(subj/cond),data=data2b)
summary(aov2.a)
summary(aov2.b)
model3<- aov(rt~ group + cond + Error(subj/(cond)),data=data3)
data3 <- data.frame(subj=as.factor(c(subj, as.numeric(subj)+20)),
group = rep(c("Control","Experimental"),each=300),
cond = rep(x1,2),
rt = rep(rt,2) + rnorm(600))
head(data3)
#defining the model is as follows: because group is a between-subject factor, it
#is not nested within the Error() notation, but cond is because it is a within-subject
#variable.
model3<- aov(rt~ group + cond + Error(subj/(cond)),data=data3)
summary(model3)
data3 <- data.frame(subj=as.factor(c(subj, as.numeric(subj)+20)),
group = as.factor(rep(c("Control","Experimental"),each=300)),
cond = rep(x1,2),
rt = rep(rt,2) + rnorm(600))
head(data3)
model3<- aov(rt~ group + cond + Error(subj/(cond)),data=data3)
summary(model3)
c(3500,6000)/8
sqrt(80*.7*.3)
sqrt(80*.7*.3)/80
setwd("~/Dropbox/courses/5220-s2025b/web-5220/psy5220/daily/Day21")
knitr::opts_chunk$set(echo = TRUE)
library(lavaan)  ##needed for holzingerswineford data
data <- dplyr::select(HolzingerSwineford1939,x1:x9)
cc <- cor(data)
pca <- princomp(cc)
loadings(pca)
f <- factanal(data,factors=3)
#f2 <- fa:fa(data,factors=3)
f <- factanal(data,factors=3)
loadings(f)
#f2 <- fa:fa(data,factors=3)
library(lavaan)  ##needed for holzingerswineford data
data <- dplyr::select(HolzingerSwineford1939,x1:x9)
cc <- cor(data)
pca <- princomp(cc)
loadings(pca)
plot(pca)
library(lavaan)  ##needed for holzingerswineford data
data <- dplyr::select(HolzingerSwineford1939,x1:x9)
cc <- cor(data)
pca <- princomp(cc)
loadings(pca)
plot(pca)
library(lavaan)  ##needed for holzingerswineford data
data <- dplyr::select(HolzingerSwineford1939,x1:x9)
cc <- cor(data)
pca <- princomp(cc)
loadings(pca)
plot(pca)
?HolzingerSwineford1939
library(lavaan)  ##needed for holzingerswineford data
data <- dplyr::select(HolzingerSwineford1939,x1:x9)
cc <- cor(data)
pca <- princomp(cc)
loadings(pca)
plot(pca)
f <- factanal(data,factors=3)
loadings(f)
summary(f)
#f2 <- fa:fa(data,factors=3)
f2 <- fa:fa(data,factors=3)
f2 <- psych:fa(data,factors=3)
library(psych)
f2 <- psych:fa(data,factors=3)
f2 <- psych:fa(data,n=3)
psych::fa
f2 <- psych:fa(data,nfactors=3)
data
f2 <- psych:fa(cov(data),nfactors=3)
f2
cor(data)
?fa
f2 <- psych:fa(r=cor(data),nfactors=3)
f2 <- psych:fa(r=cor(data),nfactors=2)
f <- factanal(data,factors=3)
loadings(f)
?GPARotation
??GPArotation
loadings(oblimin(f))
library(GPArotation)
loadings(oblimin(f))
f <- factanal(data,factors=3)
loadings(oblimin(f))
f <- factanal(data,factors=3,rotation="oblimin",scores="regression")
loadings(f)
library(lavaan)
library(semPlot)  #this won't compile for me
example(cfa)
library(lavaan)
library(semPlot)
example(cfa)
HS.model <- ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9 '
fit <- cfa(HS.model,data=HolzingerSwineford1939)
fit
summary(fit,fit.measures=T)
HS.model2 <- ' visual   =~ x1 + x2 + x3 + x4 + x5 + x6
speed    =~ x7 + x8 + x9 '
fit2 <- cfa(HS.model2,data=HolzingerSwineford1939)
fit2
summary(fit2,fit.measures=T)
anova(fit,fit2)
anova(fit2,fit)
partable(fit)
#install.packages("semPlot",dependencies=T)
library(psych)
##library(semPlot) ##this won't install for me!
##semPaths(fit, "model","est",curvePivot = FALSE,edge.label.cex = 0.5)
lavaan.diagram(fit,cut=.2,digits=2)  ##in psych package
summary(fit,fit.measures=T,standardize=T)
inspect(fit,"rsquare")
semPaths(fit)
library(semPlot) ##this won't install for me!
library(semPlot)
semPaths(fit, "model","est",curvePivot = FALSE,edge.label.cex = 0.5)
lavaan.diagram(fit,cut=.2,digits=2)  ##in psych package
summary(fit,fit.measures=T,standardize=T)
inspect(fit,"rsquare")
semPaths(fit)
partable(fit)
#install.packages("semPlot",dependencies=T)
library(psych)
library(semPlot)
semPaths(fit, "model","est",curvePivot = FALSE,edge.label.cex = 0.5)
lavaan.diagram(fit,cut=.2,digits=2)  ##in psych package
summary(fit,fit.measures=T,standardize=T)
inspect(fit,"rsquare")
semPaths(fit)
HS.model2 <- ' cognitive   =~ x1 + x2 + x3 + x4 + x5 + x6
speed    =~ x7 + x8 + x9 '
fit2 <- cfa(HS.model2,data=HolzingerSwineford1939)
fit2
summary(fit2,fit.measures=T)
semPaths(fit2)
lavaan.diagram(fit2)
anova(fit,fit2)
anova(fit2,fit)
summary(fit,fit.measures=T)
summary(fit2,fit.measures=T)
diagram(fit)
diagram(fit2)
anova(fit,fit2)
HS.model3 <- 'f1  =~ x1 + x2
f2  =~ x3 + x4
f3  =~ x5 + x6
f4  =~ x7 + x8 + x9 '
fit3 <- cfa(HS.model3,data=HolzingerSwineford1939)
fit3
anova(fit,fit3)
#semPlot::semPaths(fit3,title=F,curvePilot=T)
lavaan.diagram(fit3,cut=.2,digits=2)
semPaths(fit3,"par", weighted = TRUE, nCharNodes = 7, shapeMan = "square",
sizeMan = 8, sizeMan2 = 5,layout="tree2")
##Compare to the following model, which has the same structure but it does not have the covariance restrictions
HS.modelG <-  'G =~ x1 + x2 + x3 + x4 + x5 + x6 + x7  + x8 + x9
visual   =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9 '
fitG <- cfa(HS.modelG,data=HolzingerSwineford1939)
summary(fitG,fit.measures=T)
HS.model4 <- 'g=~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
f1  =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3   =~ x7 + x8 + x9
g ~~ 0*f1
g ~~ 0*f2
g ~~ 0*f3
f1~~ 0*f1
f2~~ 0*f2
f3 ~~ 0*f3
'
fit4 <- cfa(HS.model4,data=HolzingerSwineford1939)
#fit4 <- cfa(HS.model4,data=HolzingerSwineford1939,estimator="WLS")
fit4
summary(fit4,fit.measures=T)
semPaths(fit4,"par", weighted = FALSE, nCharNodes = 7, shapeMan = "rectangle",
sizeMan = 8, sizeMan2 = 5)
HS.model4b <- 'g=~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
f1  =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3   =~ x7 + x8
g ~~ 0*f1
g ~~ 0*f2
g ~~ 0*f3
f1~~ 0*f1
f2~~ 0*f2
f3 ~~ 0*f3
'
fit4b <- cfa(HS.model4b,data=HolzingerSwineford1939)
summary(fit4b,fit.measures=T)
anova(fit4,fit4b)
##Compare to the following model, which has the same structure but it does not have the covariance restrictions
HS.modelG <-  'G =~ x1 + x2 + x3 + x4 + x5 + x6 + x7  + x8 + x9
visual   =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9 '
fitG <- cfa(HS.modelG,data=HolzingerSwineford1939)
summary(fitG,fit.measures=T)
anova(fit,fitG)
HS.model4 <- 'g=~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
f1  =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3   =~ x7 + x8 + x9
g ~~ 0*f1
g ~~ 0*f2
g ~~ 0*f3
f1~~ 0*f1
f2~~ 0*f2
f3 ~~ 0*f3
'
fit4 <- cfa(HS.model4,data=HolzingerSwineford1939)
#fit4 <- cfa(HS.model4,data=HolzingerSwineford1939,estimator="WLS")
fit4
summary(fit4,fit.measures=T)
semPaths(fit4,"par", weighted = FALSE, nCharNodes = 7, shapeMan = "rectangle",
sizeMan = 8, sizeMan2 = 5)
HS.model4b <- 'g=~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
f1  =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3   =~ x7 + x8
g ~~ 0*f1
g ~~ 0*f2
g ~~ 0*f3
f1~~ 0*f1
f2~~ 0*f2
f3 ~~ 0*f3
'
fit4b <- cfa(HS.model4b,data=HolzingerSwineford1939)
summary(fit4b,fit.measures=T)
anova(fit4,fit4b)
HS.model4 <- 'g=~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
f1  =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3   =~ x7 + x8 + x9
g ~~ 0*f1
g ~~ 0*f2
g ~~ 0*f3
f1~~ 0*f1
f2~~ 0*f2
f3 ~~ 0*f3
'
fit4 <- cfa(HS.model4,data=HolzingerSwineford1939)
#fit4 <- cfa(HS.model4,data=HolzingerSwineford1939,estimator="WLS")
fit4
summary(fit4,fit.measures=T)
semPaths(fit4,"par", weighted = FALSE, nCharNodes = 7, shapeMan = "rectangle",
sizeMan = 8, sizeMan2 = 5)
HS.model4b <- 'g=~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9
f1  =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3   =~ x7 + x8
g ~~ 0*f1
g ~~ 0*f2
g ~~ 0*f3
f1~~ 0*f1
f2~~ 0*f2
f3 ~~ 0*f3
'
fit4b <- cfa(HS.model4b,data=HolzingerSwineford1939)
summary(fit4b,fit.measures=T)
anova(fit4,fit4b)
semPaths(fit4,"model","est",curvePivot = FALSE,edge.label.cex = 0.5)
semPaths(fit4,"model","est",layout="circle",curvePivot = FALSE,edge.label.cex = 0.5)
semPaths(fit4,"model","est",layout="tree",curvePivot = FALSE,edge.label.cex = 0.5)
lavaan.diagram(fit4,cut=.2,digits=2)
semPaths(fit4,"par", weighted = FALSE, nCharNodes = 7, shapeMan = "rectangle",
sizeMan = 8, sizeMan2 = 5)
anova(fit4,fit)
anova(fit4,fit)
anova(fit4,fit)
semPaths(fit4,"model","est",curvePivot = FALSE,edge.label.cex = 0.5)
semPaths(fit4,"model","est",layout="circle",curvePivot = FALSE,edge.label.cex = 0.5)
semPaths(fit4,"model","est",layout="tree",curvePivot = FALSE,edge.label.cex = 0.5)
lavaan.diagram(fit4,cut=.2,digits=2)
semPaths(fit4,"par", weighted = FALSE, nCharNodes = 7, shapeMan = "rectangle",
sizeMan = 8, sizeMan2 = 5)
fit5 <- cfa(HS.model4,data=HolzingerSwineford1939,orthogonal=TRUE)
summary(fit5,fit.measures=T)
semPaths(fit5,"model","est",curvePivot = FALSE,edge.label.cex = 0.5)
residuals(fit5,type="cor")
pathAn1 <-'x1 ~ x2 + x3 + x4
x2 ~ x4
x3 ~ x4
x1 + x2 +x4 ~ x5
x5 ~~ x5'
paModel <- sem(pathAn1,data=HolzingerSwineford1939)
summary(paModel,fit.measures=T)
semPaths(paModel,"par", weighted = FALSE, nCharNodes = 7, layout = "spring",
sizeMan = 8, sizeMan2 = 5)
pathAn2 <- 'x1 ~ x2 + x3 + x4
x2 + x3 + x4 ~ x5
x5 ~~ x5'
paModel2 <- sem(pathAn2,data=HolzingerSwineford1939)
summary(paModel2,fit.measures=T)
semPaths(paModel2,"par", weighted = FALSE, nCharNodes = 7, layout = "spring",
sizeMan = 8, sizeMan2 = 5,edge.label.cex=1)
anova(paModel,paModel2)
pathAn1 <-'x1 ~ x2 + x3 + x4
x2 ~ x4
x3 ~ x4
x1 + x2 +x4 ~ x5'
#x5 ~~ x5'
paModel <- sem(pathAn1,data=HolzingerSwineford1939)
summary(paModel,fit.measures=T)
semPaths(paModel,"par", weighted = FALSE, nCharNodes = 7, layout = "spring",
sizeMan = 8, sizeMan2 = 5)
pathAn2 <- 'x1 ~ x2 + x3 + x4
x2 + x3 + x4 ~ x5
x5 ~~ x5'
paModel2 <- sem(pathAn2,data=HolzingerSwineford1939)
summary(paModel2,fit.measures=T)
semPaths(paModel2,"par", weighted = FALSE, nCharNodes = 7, layout = "spring",
sizeMan = 8, sizeMan2 = 5,edge.label.cex=1)
anova(paModel,paModel2)
pathAn1 <-'x1 ~ x2 + x3 + x4
x2 ~ x4
x3 ~ x4
x1 + x2 +x4 ~ x5
x5 ~~ x5'
paModel <- sem(pathAn1,data=HolzingerSwineford1939)
summary(paModel,fit.measures=T)
semPaths(paModel,"par", weighted = FALSE, nCharNodes = 7, layout = "spring",
sizeMan = 8, sizeMan2 = 5)
pathAn2 <- 'x1 ~ x2 + x3 + x4
x2 + x3 + x4 ~ x5
x5 ~~ x5'
paModel2 <- sem(pathAn2,data=HolzingerSwineford1939)
summary(paModel2,fit.measures=T)
semPaths(paModel2,"par", weighted = FALSE, nCharNodes = 7, layout = "spring",
sizeMan = 8, sizeMan2 = 5,edge.label.cex=1)
anova(paModel,paModel2)
HS.sem <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9 '
fit_sem <- sem(HS.sem,data=HolzingerSwineford1939)
summary(fit_sem,fit.measures=T, standardized=T)
semPaths(fit_sem, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "spring",  nCharNodes = 7, edge.label.cex=.9,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
semPaths(fit, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "spring",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
HS.sem2 <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9
textual ~ visual + speed
visual ~  speed
'
fit_sem2 <- sem(HS.sem2,data=HolzingerSwineford1939)
summary(fit_sem2,fit.measures=T, standardized=T)
semPaths(fit_sem2, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree2",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
HS.sem2 <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9
textual ~ visual + speed
visual ~  speed
'
fit_sem2 <- sem(HS.sem2,data=HolzingerSwineford1939)
summary(fit_sem2,fit.measures=T, standardized=T)
semPaths(fit_sem2, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree2",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 10, sizeMan2 = 5)
anova(fit_sem,fit_sem2)
HS.sem <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9 '
fit_sem <- sem(HS.sem,data=HolzingerSwineford1939)
summary(fit_sem,fit.measures=T, standardized=T)
semPaths(fit_sem, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "spring",  nCharNodes = 7, edge.label.cex=.9,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
semPaths(fit, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "spring",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
HS.sem2 <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9
textual ~ visual + speed
visual ~  speed
'
fit_sem2 <- sem(HS.sem2,data=HolzingerSwineford1939)
summary(fit_sem2,fit.measures=T, standardized=T)
semPaths(fit_sem2, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree2",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 10, sizeMan2 = 5)
anova(fit_sem,fit_sem2)
fit_sem
fit_sem2
summary(fit_sem)
summary(fit_sem2)
HS.sem <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9 '
fit_sem <- sem(HS.sem,data=HolzingerSwineford1939)
summary(fit_sem,fit.measures=T, standardized=T)
fit_sem <- 0
HS.sem
fit_sem <- sem(HS.sem,data=HolzingerSwineford1939)
fit_sem
summary(fit_sem)
HS.sem <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9
x5~~x5'
fit_sem <- sem(HS.sem,data=HolzingerSwineford1939)
summary(fit_sem,fit.measures=T, standardized=T)
HS.sem <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9 '
fit_sem <- sem(HS.sem,data=HolzingerSwineford1939)
summary(fit_sem,fit.measures=T, standardized=T)
semPaths(fit, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "spring",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
HS.sem2 <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9
textual ~ visual
visual ~  speed
'
fit_sem2 <- sem(HS.sem2,data=HolzingerSwineford1939)
summary(fit_sem2,fit.measures=T, standardized=T)
semPaths(fit_sem2, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree2",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 10, sizeMan2 = 5)
anova(fit_sem,fit_sem2)
HS.sem3a <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9
textual ~ a*visual + b*speed
visual ~  c*speed
indirect := a*b
total := indirect + c
'
fit_sem3a <- sem(HS.sem3a,data=HolzingerSwineford1939)
summary(fit_sem3a,fit.measures=T, standardized=T)
semPaths(fit_sem3a, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
anova(fit_sem3a,fit_sem2)
HS.sem3 <-  ' visual   =~ x2 + x1 + x3
textual  =~ x4 + x5 + x6
speed    =~ x7 + x8 + x9
textual ~ visual
visual ~  speed
'
fit_sem3 <- sem(HS.sem3,data=HolzingerSwineford1939)
summary(fit_sem3,fit.measures=T, standardized=T)
semPaths(fit_sem3, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
anova(fit_sem3,fit_sem2)
iphone <- read.csv("data_study1.csv")
iphone$phone <- as.numeric(iphone$Smartphone=="iPhone")
iphone$Gender <- iphone$Gender=="female"
iphone.base <-  ' personality  =~ Honesty.Humility+ Emotionality+Extraversion+Agreeableness + Conscientiousness+Openness
social =~ Avoidance.Similarity+Phone.as.status.object+Social.Economic.Status
individ =~ Gender+Age+Time.owned.current.phone
phone ~personality+social+individ
'
iphone.simple <-  'social =~ Avoidance.Similarity+Phone.as.status.object+Social.Economic.Status
individ =~ Gender+Age+Time.owned.current.phone+Honesty.Humility+ Emotionality+Extraversion+Agreeableness + Conscientiousness+Openness
phone ~social+individ
'
iphone.simple2 <-  'social =~ Avoidance.Similarity+Phone.as.status.object+Social.Economic.Status
individ =~ Gender+Age+Time.owned.current.phone+Honesty.Humility+ Agreeableness + Conscientiousness+Openness
phone ~social+individ+Emotionality+Extraversion
'
fit_phone1 <- sem(iphone.base,data=iphone,optim.force.converged=T)
summary(fit_phone1,fit.measures=T, standardized=T)
fit_phone2 <- sem(iphone.simple,data=iphone,optim.force.converged=T)
summary(fit_phone2,fit.measures=T, standardized=T)
fit_phone3 <- sem(iphone.simple2,data=iphone,optim.force.converged=T)
summary(fit_phone3,fit.measures=T, standardized=T)
semPaths(fit_phone1, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
semPaths(fit_phone2, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
semPaths(fit_phone3, what = "col", whatLabels = "std", style = "mx",
rotation =1 , layout = "tree",  nCharNodes = 7,
shapeMan = "rectangle", sizeMan = 8, sizeMan2 = 5)
anova(fit_phone1,fit_phone2)
anova(fit_phone1,fit_phone3)
BIC(fit_phone1,fit_phone2,fit_phone3)
partable(fit)
#install.packages("semPlot",dependencies=T)
library(psych)
library(semPlot)
semPaths(fit, "model","est",curvePivot = FALSE,edge.label.cex = 0.5)
lavaan.diagram(fit,cut=.2,digits=2)  ##in psych package
summary(fit,fit.measures=T,standardize=T)
inspect(fit,"rsquare")
semPaths(fit)