In a current analysis I am using multivariate normal model and I would like to make use and derive some interpretation from the fitted covariance matrix. What I have been using so far was looking at the observed correlation first and then derive the absolute change in correlation between two variables (correlation estimated by model - correlation on observed data), the idea being that if we see large drop in the absolute change this is indication that a large portion of the observed correlation was driven by factors included in the model.
To visualize this I have been using the circlize package giving the graphs that I put in attachment below:
Briefly the top graph is the observed correlations between the response variables, red indicate positive values and blue negative ones, solid lining indicate correlation higher than 0.5 and dotted lining correlation higher than 0.25. The bottom graphs are the absolute changes for three different models that we are evaluating.
My question is: is this a relevant way to use the fitted correlation matrix? I have no idea how to include uncertainties in correlation estimates in these type of graphs … Is there some other visualization or additional analysis that could be done to extract additional infos?