Dear Scott, thanks a lot - all your comments are extremely valuable!
One quick follow-up question - the thing is, I was really trying to compare sem and LISREL solutions, which is why I was trying to specify exactly the same model. Hence, I'd prefer to stick to the Var-Covar matrix, rather than Correlation matrix. Also, I really like fixing the metric for latent variables along the metric of one of the observed variables - just for practical and didactical reasons (although I'll try to run your suggestion as well - in both sem and LISREL).
Any advice/guidance on picking the starting values for sem if I still want to estimate my original model or run into a similar problem in the future? I know how to do it (in terms of entering something else but NA in the last column) - but what starting values should I pick?
Thanks a lot again for your help!
Dimitri


----- Original Message ----
From: John Fox <jfox@mcmaster.ca>
To: John Smith <ld7631@yahoo.com>
Cc: r-help@stat.math.ethz.ch
Sent: Monday, April 9, 2007 9:46:03 PM
Subject: RE: [R] sem vs. LISREL: sem fails


Dear Dimitri,

You haven't done anything wrong: Your model is a straightforward
confirmatory factor analysis and it is correctly specified. I suspect that
sem() is picking poor start values.

I can get a solution by specifying two alternative models that are
equivalent to yours:

(1) Rescaling the problem by using correlation-matrix input:

sem.anxiety <- sem(model, KM, N=150)

(2) Fixing the variances of the factors (latent variables) rather than using
reference indicators:

model.2 <- specify.model()
ANXIETY -> a1,      lam_anx_1,  NA
ANXIETY -> a2,      lam_anx_2,  NA
ANXIETY -> a3,      lam_anx_3,  NA
DEPRESS -> d1,      lam_dep_1,  NA
DEPRESS -> d2,      lam_dep_2,  NA
DEPRESS -> d3,      lam_dep_3,  NA
FEAR -> f1,         lam_fear_1, NA
FEAR -> f2,         lam_fear_2, NA
FEAR -> f3,         lam_fear_3, NA
a1 <-> a1,          theta_a1,   NA
a2 <-> a2,          theta_a2,   NA
a3 <-> a3,          theta_a3,   NA
d1 <-> d1,          theta_d1,   NA
d2 <-> d2,          theta_d2,   NA
d3 <-> d3,          theta_d3,   NA
f1 <-> f1,          theta_f1,   NA
f2 <-> f2,          theta_f2,   NA
f3 <-> f3,          theta_f3,   NA
ANXIETY <-> ANXIETY,NA,         1
DEPRESS <-> DEPRESS,NA,         1
FEAR <-> FEAR,      NA,         1
ANXIETY <-> FEAR,   phi_AF,     NA
ANXIETY <-> DEPRESS,phi_AD,     NA
DEPRESS <-> FEAR,   phi_DF,     NA

# Running the estimation using sem:
sem.anxiety.2 <- sem(model.2, COVAR, N=150)


A couple of small points unrelated to the problem you experienced: (1) You
didn't need to load the MASS package, since you didn't appear to use
anything in it; (2) comments in R are prefixed by #, not !.

I hope this helps,
John

--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox 
-------------------------------- 

> -----Original Message-----
> From: r-help-bounces@stat.math.ethz.ch 
> [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of John Smith
> Sent: Monday, April 09, 2007 5:28 PM
> To: r-help@stat.math.ethz.ch
> Subject: [R] sem vs. LISREL: sem fails
> 
> I am new to R. 
> I just tried to recreate in R (using sem package and the 
> identical input data) a solution for a simple measurment 
> model I have found before in LISREL. LISREL had no problems 
> and converged in just 3 iterations. 
> In sem, I got no solution, just the warning message:
> 
> "Could not compute QR decomposition of Hessian.
> Optimization probably did not converge.
>  in: sem.default(ram = ram, S = S, N = N, param.names = pars, 
> var.names = vars, "
> 
> What does it mean? Maybe I am doing something wrong?
> 
> I have 3 latent factors (Anxiety, Depression, and Fear) - 
> each of them has 3 observed indicators (a1, a2, a3; d1, d2, 
> d3, and f1, f2, f3) Below is my script in R:
> 
> ! ANALYSIS OF ANXIETY, DEPRESSION, AND FEAR - LISREL P.31
> 
> ! Creating the ANXIETY, DEPRESSION, AND FEAR intercorrelation 
> matrix (KM):
> KM<-matrix(
> c(1,.8,.83,.2,.21,.19,.18,.18,.18,
> 0,1,.81,.22,.24,.18,.19,.19,.21,
> 0,0,1,.22,.19,.2,.2,.2,.22,
> 0,0,0,1,.84,.82,.22,.22,.21,
> 0,0,0,0,1,.84,.19,.18,.19,
> 0,0,0,0,0,1,.18,.18,.18,
> 0,0,0,0,0,0,1,.84,.82,
> 0,0,0,0,0,0,0,1,.81,
> 0,0,0,0,0,0,0,0,1), 9, 9)
> 
> ! Creating the ANXIETY, DEPRESSION, AND FEAR Standard 
> Deviations vector (SD):
> SD<-c(1.5, 2.4, 3.2, 2.3, 2.3, 2.6, 4.5, 4.7, 5.6)
> 
> ! Calculating the Var-Covar matrix based on correlations and SDs:
> library(MASS)
> COVAR<-outer(SD, SD) * KM
> 
> ! Creating variable names
> rownames(COVAR)<-colnames(COVAR)<-c('a1','a2','a3','d1','d2','
> d3','f1','f2','f3')
> 
> ! Specifying the measurement model to estimate:
> model<-specify.model()
> ANXIETY -> a1,     NA, 1
> ANXIETY -> a2,     lam_anx_2, NA 
> ANXIETY -> a3,     lam_anx_3, NA
> DEPRESS -> d1,     NA, 1
> DEPRESS -> d2,     lam_dep_2, NA
> DEPRESS -> d3,     lam_dep_3, NA
> FEAR -> f1,             NA, 1
> FEAR -> f2,             lam_fear_2, NA
> FEAR -> f3,             lam_fear_3, NA
> a1 <-> a1,                theta_a1, NA
> a2 <-> a2,                theta_a2, NA
> a3 <-> a3,                theta_a3, NA
> d1 <-> d1,               theta_d1, NA
> d2 <-> d2,               theta_d2, NA
> d3 <-> d3,               theta_d3, NA
> f1 <-> f1,                 theta_f1, NA
> f2 <-> f2,                 theta_f2, NA
> f3 <-> f3,                 theta_f3, NA
> ANXIETY <-> ANXIETY,   phi_AA, NA
> DEPRESS <-> DEPRESS,     phi_DD, NA
> FEAR <-> FEAR,                  phi_FF, NA
> ANXIETY <-> FEAR,           phi_AF, NA
> ANXIETY <-> DEPRESS,    phi_AD, NA
> DEPRESS <-> FEAR,            phi_DF, NA
> 
> ! Running the estimation using sem:
> sem.anxiety<-sem(model, COVAR, N=150)
> 
> Thank you very much for your advice!
> Dimitri
> 
> 
>  
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