Applied regression analysis and other multivariable methods
Applied regression analysis and other multivariable methods
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Principal Component Analysis (PCA) can identify key variables among complex data sets, but requires special statistical packages. Alternatively, Discrete and Aggregate Correlate Summation (DCΣ/ACΣ) identifies the most covariant variables relative to mean clustering for grouped and individual data, respectively. We compared these analyses regarding the influence of estrogen and salt on the hypertensive phenotype of the female mRen2.Lewis strain. DCΣx compares changes in correlation between two groups for each variable versus all of the others, relative to mean shift. ACΣx determines which variable has the highest total correlation to the other variables, relative to its mean reduced (normalized) standard deviation (nSD). To compare correlate summation to PCA, the absolute weights of the first principal component (EVEC1x were multiplied by the nSD as in ACΣx. Nine variables including proteinuria, serum ACE, plasma Ang II, renin, heart weight to body weight ratio and systolic blood pressure were analyzed with respect to normal and high salt diets, as well as to estrogen intact and depleted conditions. DCΣx results for both arms of the study were significantly correlated with EVEC1x (r=0.72, px results (DCΣx-cum increased correlation to EVEC1x (r=0.84, px and EVEC1x exhibited the highest correlation (r=0.99, px-cum and ACΣx variable urinary protein (r=0.85, p