Input
- InputTable: diabetes, as in LAR Example: FitMethod ('lar')
SQL Call
SELECT * FROM LAR (
ON diabetes AS InputTable
OUT TABLE OutputTable (diabetes_lasso)
USING
TargetColumns ('y', 'age', '[2:5]', 'ldl', 'hdl', '[8:10]')
FitMethod ('lasso')
Intercept ('true')
L2Normalization ('true')
MaxIterNum (20)
) AS dt;
Output
message -------------------------------------------------------------------------- Successful. message Result has been stored in the table specified in the argument OutputTable.
SELECT * FROM diabetes_lasso WHERE steps <> 0 ORDER BY steps;
steps var_id var_name max_abs_corr step_length intercept age sex bmi map1 tc ldl hdl tch ltg glu
----- ------ -------- ------------------ ------------------ ------------------ ------------------- ------------------- ------------------ ------------------ ------------------- ------------------ ------------------- ------------------ ------------------ ------------------
1 3 bmi 949.4352416992188 60.11927032470703 152.13348388671875 0.0 0.0 60.11927032470703 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 9 ltg 889.3159790039062 513.2236938476562 152.13348388671875 0.0 0.0 361.8946228027344 0.0 0.0 0.0 0.0 0.0 301.77532958984375 0.0
3 4 map1 452.9009704589844 175.55322265625 152.13348388671875 0.0 0.0 434.7579650878906 79.2364501953125 0.0 0.0 0.0 0.0 374.91583251953125 0.0
4 7 hdl 316.0740661621094 259.3674621582031 152.13348388671875 0.0 0.0 505.6595458984375 191.26988220214844 0.0 0.0 -114.10098266601562 0.0 439.6649475097656 0.0
5 2 sex 130.13084411621094 88.6591567993164 152.13348388671875 0.0 -74.91651153564453 511.34808349609375 234.1546173095703 0.0 0.0 -169.71139526367188 0.0 450.6674499511719 0.0
6 10 glu 88.78243255615234 43.67793273925781 152.13348388671875 0.0 -111.97855377197266 512.0440673828125 252.5270233154297 0.0 0.0 -196.04544067382812 0.0 452.3927307128906 12.078152656555176
7 5 tc 68.96521759033203 135.9840850830078 152.13348388671875 0.0 -197.75650024414062 522.2648315429688 297.15972900390625 -103.94625091552734 0.0 -223.92604064941406 0.0 514.7494506835938 54.76768112182617
8 8 tch 19.98125457763672 54.015602111816406 152.13348388671875 0.0 -226.1336669921875 526.8854370117188 314.3892822265625 -195.1058349609375 0.0 -152.47726440429688 106.34280395507812 529.916015625 64.48741912841797
9 6 ldl 5.47747278213501 5.56723165512085 152.13348388671875 0.0 -227.17579650878906 526.3905639648438 314.9504699707031 -237.34097290039062 33.628273010253906 -134.59934997558594 111.3841323852539 545.4826049804688 64.6066665649414
10 1 age 5.089179039001465 41.999664306640625 152.13348388671875 -5.718947887420654 -234.3976287841797 522.6488037109375 320.3425598144531 -554.266357421875 286.7361755371094 0.0 148.90045166015625 663.0332641601562 66.3309555053711
11 -7 hdl 2.1822497844696045 7.270700931549072 152.13348388671875 -7.011245250701904 -237.1007843017578 521.0751342773438 321.5490417480469 -580.4385986328125 313.86212158203125 0.0 139.8578643798828 674.9366455078125 67.17939758300781
12 7 hdl 1.3104352951049805 27.970022201538086 152.13348388671875 -10.012197494506836 -239.819091796875 519.8397827148438 324.39044189453125 -792.1841430664062 476.745849609375 101.04457092285156 177.06417846679688 751.279296875 67.6253890991211
The following figure represents the results and shows how the standardized coefficients evolved during the model-building process. The x-axis represents the ratio of the norm of the current beta to the full beta. The y-axis represents the standardized coefficients, which are estimated when standardized predictors are used. The numbers on the top of the graph represent the steps of the model-building process. The numbers on the right represent the predictor IDs.
Download a zip file of all examples and a SQL script file that creates their input tables.