Hi,

Thanks to the script below (suggested by Athanassios Protopapas and further developed by Paolo Ghisletta, thank you!!!) I was able to obtain a large number of fit indices, but I still don't know how to get (a) the 90% confidence interval for RMSEA, (b) p close (the test of the null hypothesis that RMSEA (in the population) in less than .05), and (c) the residual correlation matrix. Can anyone help me?

If this is not too much to ask, I would also be interested in getting (d) an "effect decomposition" (i.e., a tabular summary of estimated direct, indirect, and total effects, and either their standard error or their significance level) and (e) the proportion of variance explained in each of the endogenous variables.

If there is documentation on how to get this output, please let me know where to find it. Thank you,

-- M

fit.cov
options(scipen=3)

indep
model
deviance
Chi
indep.chi
df
p.Chi
Chi.df
indep.df
N
N.parms
N.manifest
q
N.latent
observed.cov
observed.cor
A
S
F
I
estimate.cov
estimate.cor
Id.manifest
residual.cov
residual.cor
F0
if (F0
NFI
NNFI.TLI
PNFI
RFI
IFI
CFI
if (CFI>1) {CFI=1}

PRATIO
PCFI
NCP
RMSEA
MFI
GH
GFI
AGFI
PGFI
AICchi
AICdev
BCCchi
BCCdev
BICchi
BICdev
CAICchi
CAICdev
ECVIchi
ECVIdev
MECVIchi
MECVIdev
RMR
SRMR
indices
rbind(N,deviance,N.parms,Chi,df,p.Chi,Chi.df,AICchi,AICdev,BCCchi,BCCdev,BICchi,BICdev,CAICchi,CAICdev,RMSEA,SRMR,RMR,GFI,AGFI,PGFI,NFI,RFI,IFI,NNFI.TLI,CFI,PRATIO,PNFI,PCFI,NCP,ECVIchi,ECVIdev,MECVIchi,MECVIdev,MFI,GH)

return(indices)

}

fit.cov(indepfit,modelfit)

P.S.: For information, here is the entire R script in RAM notation I used.

# Prelim stuff, create the covariance matrix

data_apgar
manifests
data_subset
covmatrix

# Run "our" model

apgar_model2
type="RAM",

manifestVars = manifests,

mxPath(from="smokes", to="gestat", values=.1, label="b"),

mxPath(from="wgtgain", to="apgar", values=.1, label="e"),

mxPath(from="gestat", to="apgar", values=.1, label="f"),

mxPath(from="smokes", to="wgtgain", arrows=2, values=.5, label="a"),

mxPath(from=manifests, arrows=2, free=TRUE, values=1, labels=c("da","dg","vars","varw")),

mxCI(c("a", "b", "e", "f", "vars", "varw", "dg", "da")),

mxData(covmatrix, type="cov", numObs = 60)

)

modelfit
summary(modelfit)

# Run the independence model

indep.model
type="RAM",

manifestVars = manifests,

mxPath(from=manifests, arrows=2, free=TRUE, values=1, labels=c("vara","varg","vars","varw")),

mxCI(c("vara","varg","vars","varw")),

mxData(covmatrix, type="cov", numObs = 60)

)

indepfit
summary(indepfit)

# Paolo's script for covariance input

fit.cov
options(scipen=3)

indep
model
deviance
Chi
indep.chi
df
p.Chi
Chi.df
indep.df
N
N.parms
N.manifest
q
N.latent
observed.cov
observed.cor
A
S
F
I
estimate.cov
estimate.cor
Id.manifest
residual.cov
residual.cor
F0
if (F0
NFI
NNFI.TLI
PNFI
RFI
IFI
CFI
if (CFI>1) {CFI=1}

PRATIO
PCFI
NCP
RMSEA
MFI
GH
GFI
AGFI
PGFI
AICchi
AICdev
BCCchi
BCCdev
BICchi
BICdev
CAICchi
CAICdev
ECVIchi
ECVIdev
MECVIchi
MECVIdev
RMR
SRMR
indices
rbind(N,deviance,N.parms,Chi,df,p.Chi,Chi.df,AICchi,AICdev,BCCchi,BCCdev,BICchi,BICdev,CAICchi,CAICdev,RMSEA,SRMR,RMR,GFI,AGFI,PGFI,NFI,RFI,IFI,NNFI.TLI,CFI,PRATIO,PNFI,PCFI,NCP,ECVIchi,ECVIdev,MECVIchi,MECVIdev,MFI,GH)

return(indices)

}

fit.cov(indepfit,modelfit)