Table 2.

Comparison of results when basing variable selection on the FARMS algorithm or common strategies. The number of covariates included in the final model excludes the “forced in” covariates. FARMS parameters used in this case are number of adding covariates = 8, number of starting covariates = 10, and the selecting criteria for both best subset and best model was the BIC. (All runs were executed on an Intel Xeon x5680 machine with 6 CPU cores and 95 GB RAM memory under a Linux Suse 11.0 OS).

		Time (seconds)*   Mean (IQR)	Number of vars.**	BIC	AIC	R 2	Adj. R 2
HLA	FARMS	1.27  (1.00; 1.40)	9	2235.4	2183.03	11.67%	10.74%
	All subsets	>1 month	—	—	—	—	—
	Forward selection1	3.84 (3.13; 3.52)	17	2259.4	2168.86	14.69%	12.98%
	Forward stepwise1	4.32 (3.51; 3.90)	17	2259.4	2168.86	14.69%	12.98%
	Forward selection2	2.01 (1.62; 1.88)	10	2235.45	2178.27	12.36%	11.33%
	Forward stepwise2	2.35 (1.89; 2.18)	9	2235.44	2183.03	11.67%	10.74%
	Forward selection3	1.99 (1.61; 1.88)	10	2235.45	2178.27	12.36%	11.33%
	Forward stepwise3	2.38 (1.92; 2.22)	9	2235.44	2183.03	11.67%	10.74%
	Backward stepwise3	13 s	10	2236.52	2174.58	12.93%	11.81%
OLP	FARMS	33.4    (19.47; 37.95)	12	2224.8	2158.11	14.77%	13.57%
	All subsets	>1 month	—	—	—	—	—
	Forward selection1	393.2 (324.40; 336.60)	79	2396.53	2010.56	38.40%	32.22%
	Forward stepwise1	545.9 (451.70; 469.70)	83	2415.46	2010.53	38.97%	32.51%
	Forward selection2	401.7 (329.50; 343.40)	80	2403.3	2012.56	38.40%	32.13%
	Forward stepwise2	462.4 (382.50; 401.50)	76	2385.18	2013.51	37.76%	31.77%
	Forward selection3	38.09 (31.34; 33.11)	12	2224.82	2158.11	14.77%	13.57%
	Forward stepwise3	38.31 (31.34; 33.43)	12	2224.82	2158.11	14.77%	13.57%
	Backward stepwise3	>12 hours	23	2232.63	2108.63	21.68%	19.45%

  ¹Selection by AIC and base model with intercept-only.

²Selection by AIC and base model with forced-in variables.

³Selection by BIC and base model with forced-in variables.

^*Time obtained after 100 executions for each scenario.

^**Including forced-in variables.