I am running a confirmatory factor analysis (CFA) in R, but I keep encountering an error:
Warning message: Could not compute QR decomposition of Hessian. Optimization probably did not converge.
Here’s the R code I’m using:
library(sem)
my_cov_matrix <- cov(na.omit(my_data))
my_model <- specifyModel()
FACTOR1 -> Var1, factor1_1
FACTOR1 -> Var2, factor1_2
FACTOR2 -> Var3, factor2_1
FACTOR2 -> Var4, factor2_2
FACTOR3 -> Var5, factor3_1
FACTOR3 -> Var6, factor3_2
FACTOR4 <-> FACTOR4, NA, 1
Var1 <-> Var1, err1
Var2 <-> Var2, err2
FACTOR1 <-> FACTOR2, cov1
FACTOR1 <-> FACTOR3, cov2
sem_model <- sem(my_model, my_cov_matrix, nrow(my_data))
summary(sem_model, fit.indices = c("RMSEA", "NNFI", "CFI"))
When I use fewer latent variables, the model works fine. It also runs when I remove the last 55 lines of code in specifyModel(). Given the long calculation time and high memory usage, could this be a system memory issue (I have 2GB RAM) or is there another potential cause?
Hey there, WhisperingWind! 
Oof, that error message sounds like a real headache. I’ve run into similar issues before, and it can be super frustrating. Have you considered giving the lavaan package a shot? I’ve found it to be a bit more forgiving with complex models and memory usage.
Just curious, how big is your dataset? With only 2GB of RAM, you might be hitting some limits there. Have you tried running your analysis on a more powerful machine or maybe even looking into some cloud computing options? Could be worth a shot!
Also, I’m wondering about your data prep. Any chance there might be some outliers or missing values throwing things off? Sometimes cleaning up the data a bit can work wonders.
Oh, and here’s a random thought - have you played around with different optimization algorithms? Sometimes switching those up can help with convergence issues.
Let me know if you try any of these! I’d be super interested to hear how it goes. Good luck with your analysis! 
hey whisperingwind, i’ve had similar issues before. with only 2gb ram, you’re probably hitting memory limits. have you tried simplifying your model or using lavaan instead? it’s usually better with complex stuff. also, check your data for outliers or missing values - that can mess things up. good luck with your analysis!
I’ve encountered similar issues with CFA in R, particularly when handling complex models and limited memory. With 2GB of RAM, memory constraints can indeed contribute to convergence errors in your analysis. It might help to simplify your model by starting with fewer latent variables and progressively adding complexity. Also, consider using the lavaan package, which many find to be more efficient under such conditions. Additionally, check your data for potential issues like outliers or missing values, and explore different optimization approaches if the package allows it.