BMC Medical Research Methodology

Table 4 Estimated regression coefficients for nine predictors of dependence in daily activities obtained from a subsample of size N = 275 by applying various versions of tuned ridge regression, ridge regression based on informative priors (IP), ridge regression based on weakly informative priors (WP) or Firth’s logistic regression with intercept-correction (FLIC). Tuning criteria: D deviance, GCV, generalized cross-validation, CE, classification error, RCV50, repeated 10-fold cross-validated deviance with θ = 0.5, RCV95, repeated 10-fold cross-validated deviance with θ = 0.95, AIC, Akaike’s information criterion. Calibration slopes were calculated on a validation dataset of size N = 1539

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

Back to article page

ISSN: 1471-2288

Contact us

General enquiries: journalsubmissions@springernature.com