[Retracted] Predicting Risk of Insulin Resistance in a Chinese Population with Polycystic Ovary Syndrome: Designing and Testing a New Predictive Nomogram

Figure 1

Demographic and clinical feature selection using the LASSO binary logistic regression model. Notes: (a) the optimal parameter (lambda) in the lasso model is selected by the minimum criterion of five crossvalidations. The partial likelihood deviation (binomial deviation) curve and the logarithm (lambda) curve are drawn. Use 1se (1-SE standard) of minimum standard and minimum standard to draw a dashed vertical line at the best value. (b) LASSO coefficient profiles of the 22 features. According to the sequence of the logarithm (lambda), the coefficient profile is drawn. A fivefold crossvalidation is used to draw a vertical line at the selected value, where the optimal lambda produces five nonzero coefficients. Abbreviations: LASSO: least absolute shrinkage and selection operator; SE: standard error.

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