sem
function (from the sem package; Fox, 2010) and outputs a multi-page pdf file containing goodness of fit indices, path diagram, graphs of the parameter estimates and graphs of the implied and observed covariance and correlation matrices.qgraph.sem(res, layout="circle", ..., vsize.man=3, vsize.lat=6, filename="qgraph",
filetype="pdf", residuals=TRUE, panels=2, include=1:12, latres=TRUE,
curve=0, residSize=0.2, onefile=TRUE, width = 7, height = 7)
qgraph
. This is both for the path diagram and for the correlation/covariance plots.qgraph
qgraph.cfa
qgraph.semModel
sem
require('sem')
### This example is taken from the examples of the sem function.
### Only names were changed to better suit the path diagram.
# ----------------------- Thurstone data ---------------------------------------
# Second-order confirmatory factor analysis, from the SAS manual for PROC CALIS
R.thur <- readMoments(diag=FALSE, names=c('Sen','Voc',
'SC','FL','4LW','Suf',
'LS','Ped', 'LG'))
.828
.776 .779
.439 .493 .46
.432 .464 .425 .674
.447 .489 .443 .59 .541
.447 .432 .401 .381 .402 .288
.541 .537 .534 .35 .367 .32 .555
.38 .358 .359 .424 .446 .325 .598 .452
model.thur <- specifyModel()
F1 -> Sen, *l11, NA
F1 -> Voc, *l21, NA
F1 -> SC, *l31, NA
F2 -> FL, *l41, NA
F2 -> 4LW, *l52, NA
F2 -> Suf, *l62, NA
F3 -> LS, *l73, NA
F3 -> Ped, *l83, NA
F3 -> LG, *l93, NA
F4 -> F1, *g1, NA
F4 -> F2, *g2, NA
F4 -> F3, *g3, NA
Sen <-> Sen, q*1, NA
Voc<-> Voc, q*2, NA
SC <-> SC, q*3, NA
FL <-> FL, q*4, NA
4LW <-> 4LW, q*5, NA
Suf<-> Suf, q*6, NA
LS <-> LS, q*7, NA
Ped<-> Ped, q*8, NA
LG <-> LG, q*9, NA
F1 <-> F1, NA, 1
F2 <-> F2, NA, 1
F3 <-> F3, NA, 1
F4 <-> F4, NA, 1
sem.thur <- sem(model.thur, R.thur, 213)
# Run qgraph:
qgraph.sem(sem.thur,filename="Thurstone tree",layout="tree",edge.label.cex=0.6,curve=0.4,width=8,height=5)
# Spring layout:
qgraph.sem(sem.thur,filename="Thurstone spring",layout="spring",residuals=FALSE,width=5,height=5)
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