Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

Table 3 Illustrative computation times

	Linear model without interaction terms				Linear model with interaction terms
	d_max= 2	d_max= 3	d_max= 4	d_max= 5	d_max= 2	d_max= 3	d_max= 4	d_max= 5
p=30	0.1	1.1	8.7	9.5	0.4	4.7	38.6	374.6
p=60	0.5	10.5	114.3	−	1.8	39.4	661.6	−
p=120	2.8	116.3	−	−	8.2	350.1	−	−
p=500	150.3	−	−	−	238.7	−	−	−

Computation times (in seconds) for proposed Bayesian variable selection procedure, using empirical Bayes to select between two priors (M = 2) and to set the prior strength parameter λ (optimisation performed over ten values of λ). Results shown for varying values of d_max(maximum number of predictors allowed in a model) and p (total number of predictors), for both a linear model without interaction terms and a linear model with interaction terms. (Data and model priors generated using random numbers and results are averages over three iterations. Computation performed on a standard single-core personal computer; 1.6GHz, 2GB RAM. ‘-’ denotes a (p,d_max) regime where the procedure failed due to insufficient memory).

ISSN: 1471-2105