2.7 | Regression analysis
The correlation between methylation rate and age for both gene regions was examined using a single linear regression analysis. The effect of sex and female nursing state on methylation rate in both gene regions was assessed using analysis of covariance (ANCOVA).
To develop the age estimation model, we used the support vector regression (SVR). We constructed three models and assessed their precision and accuracy, as listed below:
Model 1: GRIA2 methylation rate + CDKN2A methylation rate
Model 2: GRIA2 methylation rate + CDKN2A methylation rate + sex
Model 3: GRIA2 methylation rate + CDKN2A methylation rate + female nursing state
Leave-one-out cross-validation (LOOCV) was performed to validate the overfitting of these models. Precision and accuracy were calculated both before and after LOOCV. All computations were performed using R (version 4.0.2) statistical software (R Core Team, 2020). The R package “Pamesures” (Wang & Li, 2018), “e1071” (Meyer et al ., 2022), “MuMIn” (Bartoń, 2022), and “car” (Fox & Waisberg, 2019) were used for the analysis. The output of coefficient of “a ” was carried out using the “nls” command. The two parameters, “cost” and “epsilon” for the SVR models were optimized using the “tune” command with the fixed set of “type = eps-regression, kernel = radial, gamma = 0.5”. The coefficient of determination (R2 ) and mean absolute error (MAE) was used to indicate how well an estimated age fitted the model. Differences were considered significant atp < 0.05 for all analyses.