I have 11 sleep outcomes - all binary .i.e. initial insomnia? yes/no. I want to see whether the 126 individuals I have cluster onto the different sleep variables. I have tried EFA before, and the structure is proven to be inappropriate for this, so am now trying cluster analysis potentially with Manhattan distance method.
I am not sure how to go about this in STATA and would appreciate the help to be able to see whether my variables are clustering and from there, work these into regressions.
Thanks.
I am not a Stata user so I am not going to be helpul on that. However, I can propose you a strategy for the analysis.
1. Map the patients using multiple correspondence analysis (MCA), i.e. an equivalent (roughly speaking) of principal component analysis for binary variables. You will be able to represent the variables and the individuals on the same plane(s), and thus identify groups of individuals sharing the same covariate pattern.
2. Use individuals' MCA scores to perform an HCA with an Euclidean distance.
3. Project the resulting partition(s) on the previous plane(s).
This can be done in a few lines of R code: let me know if you're interested.
Why dont you use R?
You can use random forests etc for supervised partitioning or PCA based methods for unsupervised clustering.
Thanks very much! I have heard from everyone to use R so that is what I have done! Thanks for your help!
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