From: To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets
Method | Estimated coefficients | Calibration slope | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
\( {\hat{\beta}}_0 \) | \( {\hat{\beta}}_{\mathrm{age}} \) | \( {\hat{\beta}}_{\mathrm{sex}} \) | \( {\hat{\beta}}_{\mathrm{BMI}} \) | \( {\hat{\beta}}_{\mathrm{APGAR}} \) | \( {\hat{\beta}}_{\mathrm{CD}} \) | \( {\hat{\beta}}_{\mathrm{fall}} \) | \( {\hat{\beta}}_{\mathrm{lonliness}} \) | \( {\hat{\beta}}_{\mathrm{health}} \) | \( {\hat{\beta}}_{\mathrm{pain}} \) | ||
D | −4.79 | 0.05 | −0.43 | −0.25 | 1.25 | 0.57 | 2 | 0.04 | −0.20 | 0.11 | 1.09 |
GCV | −4.53 | 0.05 | −0.35 | −0.22 | 1.14 | 0.54 | 1.90 | 0.05 | −0.18 | 0.10 | 1.16 |
CE | −5.71 | 0.07 | −0.72 | −0.36 | 1.59 | 0.64 | 2.25 | 0.03 | −0.25 | 0.13 | 0.89 |
RCV50 | −4.85 | 0.06 | −0.45 | −0.26 | 1.27 | 0.57 | 2.02 | 0.04 | −0.20 | 0.11 | 1.07 |
RCV95 | −4.53 | 0.05 | −0.35 | −0.22 | 1.14 | 0.54 | 1.90 | 0.05 | −0.18 | 0.10 | 1.16 |
AIC | −5.59 | 0.07 | −0.69 | −0.35 | 1.55 | 0.63 | 2.22 | 0.03 | −0.24 | 0.13 | 0.91 |
IP | −5.33 | 0.07 | −0.61 | −0.32 | 1.46 | 0.61 | 2.16 | 0.03 | −0.23 | 0.12 | 0.96 |
WP | −5.89 | 0.08 | −0.78 | −0.38 | 1.65 | 0.65 | 2.29 | 0.02 | −0.26 | 0.14 | 0.87 |
FLIC | −5.84 | 0.07 | −0.77 | −0.37 | 1.61 | 0.60 | 2.16 | 0.02 | −0.25 | 0.13 | 0.91 |