Closed TarandeepKang closed 2 years ago
@TarandeepKang, thanks for the request. Any idea how feasible this is @vandenman?
While we used to support partial correlation networks, we removed these because the R package parcor was removed from cran. However, the default estimator EBICglasso
estimates a Lasso regularized partial correlation network. Do you necessarily need unregularized partial correlation networks?
Sorry, I had been looking the correlation/regression module and not the network module. In the best possible way, the software has more features than I can keep up with! It does however look like partial correlation is still supported as an estimator within the network module? As of 0.16.4.
@TarandeepKang Ah yes, you're right I'm confusing two options. adalasso
(a method for adaptive lasso) was removed a while ago, not the partial correlation networks.
Thank you for your help, Don and Julius. Since this was a nonissue, I'll close it now.
Description
Partial correlation networks/Gaussian graphical model
Purpose
Examining relationships between variables and a predictor, when all others are controlled for
Use-case
Situations in which there are a large number of pairs requiring other variables to be controlled for
Is your feature request related to a problem?
Existing correlation options are not suitable when there are multiple pairs each requiring all other variables to controlled for
Describe the solution you would like
The ability to build a partial correlation network in JASP
Describe alternatives that you have considered
Simple partial correlation analysis but this is inappropriate for analyses involving multiple pairs=
Additional context
I myself am new to this method, but the following papers may be useful:= Giraud, C., Huet, S., & Verzelen, N. (2012). Graph Selection with GGMselect. Statistical Applications in Genetics and Molecular Biology, 11(3). https://doi.org/10.1515/1544-6115.1625
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634. https://doi.org/10.1037/met0000167