Table 2.

Comparison of estimators applied to ABIDE dataset

					Training set (n = 175)				Test set (n = 44)
Method	α	γ	Estimator	* Optimal λ	CVMSE	MSE	r	p	MSE	r	p
Glmnet	0.0		Ridge	2627	1646.7	954.3	0.879	< 0.001	1325.8	0.285	0.060
	0.01		Elastic Net	289	1661.2	935.7	0.883	< 0.001	1305.4	0.320	0.034
FSGL	1.0	1.0	Lasso	1848	1674.2	1689.1	0.159	0.036	1426.7	0.035	0.821
	0.2	1.0	Sparse Group Lasso	521	1674.4	1572.0	0.383	< 0.001	1427.8	0.069	0.654
	0.2	0.8	Fused Sparse Group Lasso	604	1673.9	1633.2	0.254	< 0.001	1434.3	0.038	0.805
	0.0	0.8	Fused Group Lasso	604	1674.3	1641.2	0.232	0.002	1435.4	0.032	0.838
Adaptive FSGL	1.0	1.0	Adaptive Lasso	814	1368.1	120.1	0.986	< 0.001	1193.1	0.406	0.006
	0.2	1.0	Adaptive Sparse Group Lasso	4041	1373.2	129.1	0.985	< 0.001	1203.1	0.397	0.008
	0.2	0.8	Adaptive Fused Sparse Group Lasso	1097	1338.9	168.6	0.977	< 0.001	1165.2	0.437	0.003
	0.0	0.8	Adaptive Fused Group Lasso	1424	1477.2	144.2	0.981	< 0.001	1211.6	0.394	0.008

^*Note: λ for glmnet R package is scaled by factor n⁻¹.

Mean total sum of squares for training set = 1697.5; Mean total sum of squares for test set = 1428.0. ABIDE: Autism Brain Imaging Data Exchange; FSGL: fused sparse group lasso; CVMSE: cross-validation mean squared error; MSE: mean squared error; r: Pearson correlation.