Fig. 3: Multivariate regressions of individual FI items to predict age (FRIGHT age).
From: Age and life expectancy clocks based on machine learning analysis of mouse frailty
a–c Median error, r2 values and p values for univariate regression of Frailty Index (FI) score, and multivariate regressions of the individual FI items using either simple least squares (SLS), elastic net (ELN), the Klemera–Doubal method (KDM), or random forest regression (RFR) for chronological age in the mouse training set. All models were tested with bootstrapping with replacement repeated 100 times, and each bootstrapping incidence is plotted as a separate point. ****p < 0.0001 and ***p  < 0.001 compared to FI model with one-way ANOVA. Error bars represent standard error of the mean. d, e Random forest regression of the individual FI items for chronological age on training and testing datasets (data was randomly divided 50:50, separated by mouse rather than by assessment, n = 106 datapoints for training and n = 165 for testing). This model is termed FRIGHT (Frailty Inferred Geriatric Health Timeline) age. Correlation determined by Pearson correlation coefficients. f Importances of top eight items included in the FRIGHT age model. g Residuals of the regression (delta age) plotted against survival for individual ages (as demonstrated by different colors). Regression lines are only graphed for ages where there is an r2 value >0.1. Source data are provided as a Source Data file.
