Help needed: Unexpected lavaan CFA() warning message

CFA
1

I’m struggling with a CFA using lavaan. My code worked fine before, but now I’m getting this weird warning:

lavaan WARNING:
    the optimizer (NLMINB) claimed the model converged, but not all
    elements of the gradient are (near) zero; the optimizer may not
    have found a local solution use check.gradient = FALSE to skip
    this check

My model is pretty big (N = 327) with lots of variables. I’m using the standard cfa() function with std.lv = F. The fit takes forever and then throws this warning. When I try to get the summary, it mentions that the model didn’t end normally after many iterations and that the estimates might be unreliable. Any idea why this is happening and how to fix it? I’m really confused and any insight would help!

2

I’ve encountered similar issues with lavaan before. One approach that might help is to increase the number of iterations allowed for the optimizer. You can do this by adding ‘control = list(iter.max = 1000)’ to your cfa() function call.

Another potential solution is to provide better starting values for your parameters. If you have theoretical or empirical grounds for estimating initial values, you can specify these in your model syntax.

It’s also worth considering whether your model might be misspecified. The warning about the gradient suggests the optimizer is struggling to find a solution. This could indicate an identification problem or that your model doesn’t fit the data well.

Lastly, you might want to examine your data for multicollinearity among your variables. High correlations between predictors can sometimes lead to convergence issues in CFA models.

If these suggestions don’t resolve the issue, you may need to consult with a statistical expert or consider alternative modeling approaches.

3

Hey Oliver63, that’s a tricky situation you’ve got there! :thinking:

I’ve run into similar issues with lavaan before, and it can be super frustrating. Have you tried playing around with different estimators or optimization methods? Sometimes switching from the default ML to WLSMV or using a different optimizer like ‘em’ instead of ‘nlminb’ can help.

Also, how complex is your model? With a big sample size and lots of variables, you might be pushing the limits of what CFA can handle. Maybe try simplifying your model a bit or breaking it down into smaller parts?

Oh, and have you checked for any outliers or weird distributions in your data? Sometimes those can throw a wrench in the works.

What kind of fit indices are you getting despite the warning? Sometimes the model can still be usable even with convergence issues.

Curious to hear more about your model and what you’ve tried so far! Hang in there – CFA can be a real pain sometimes, but we’ll figure it out! :blush: