I'm running a factor analysis, which I have done before using EQS, with OpenMx and finding an error. The code and the output are below. I'd appreciate any diagnostics and directions!

Regards,

Carlos.

# load library

require(OpenMx)

# data

AttractivenessDataRaw <- read.table("/home/pos/carlosdenner/Desktop/logattractiveness.txt",header=TRUE)

names(AttractivenessDataRaw)

# model specification

AttractivenessFactorModel<-mxModel("Attractiveness Factor Model",

type="RAM",

mxData(

observed=AttractivenessDataRaw,

type="raw"),

manifestVars=c("pageviews","downloads","members"),

latentVars="attractiveness",

# residual variances

mxPath(from=c("pageviews","downloads","members"),

arrows=2,

free=TRUE,

values=c(1,1,1),

labels=c("e1","e2","e3")

),

# latent variance

mxPath(from="attractiveness",

arrows=2,

free=TRUE,

values=1,

labels ="varAttractiveness"

),

# factor loadings

mxPath(from="attractiveness",

to=c("pageviews","downloads","members"),

arrows=1,

free=c(FALSE,TRUE,TRUE),

values=c(1,1,1),

labels =c("l1","l2","l3")

),

# means

mxPath(from="one",

to=c("pageviews","downloads","members","attractiveness"),

arrows=1,

free=c(TRUE,TRUE,TRUE,FALSE),

values=c(1,1,1,0),

labels =c("meanpageviews","meandownloads","meanmembers",NA)

)

) # close model

AttractivenessFactorFit <- mxRun(AttractivenessFactorModel)

Output:

Running Attractiveness Factor Model

Warning message:

In model 'Attractiveness Factor Model' NPSOL returned a non-zero status code 6. model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED)

Code 6 is returned from the optimizer. Frequently a code 6 can be due to problems with starting values.

Note that you have started all of your variances and factor loadings at 1. Your starting values are likely resulting in a singular matrix.

I'd suggest starting values of something smaller for the free factor loadings, perhaps 0.2.

Also something smaller than 1.0 for the factor variance, say perhaps 0.8. You can probably get away with residual variances as 1.0, but I usually start them at 0.8 as well.

I had to change most of the starting values, but it worked! Thanks, Steve!

Would you mind posting the data and mplus code to allow me to make an example for mplus users, and for others hitting your roadblock?

Good idea. We can simulate the data if this particular set can't be posted online.