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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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 at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of John Smith > Sent: Monday, April 09, 2007 5:28 PM > To: r-help at 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 > > > > ______________________________________________________________ > ______________________ > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >
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 > > > > ______________________________________________________________ > ______________________ > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >____________________________________________________________________________________ [[alternative HTML version deleted]]
Dear Dimitri, Using the tools provided in the sem package [e.g., the debug argument to sem()], I took a closer look at this convergence problem, which arises because of poor start values selected for the variances of the latent variables. It turns out that you can get a solution for your original model fit to the covariance matrix by setting the argument par.size to "startvalues" (as suggested in ?sem): sem.anxiety <- sem(model, COVAR, N=150, par.size="startvalues") Regards, John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox --------------------------------> -----Original Message----- > From: John Fox [mailto:jfox at mcmaster.ca] > Sent: Monday, April 09, 2007 9:46 PM > To: 'John Smith' > Cc: 'r-help at stat.math.ethz.ch' > 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 at stat.math.ethz.ch > > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of John Smith > > Sent: Monday, April 09, 2007 5:28 PM > > To: r-help at 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 > > > >