OLS R^2 for large datasets inaccurate (lassogof)

Dear community,

I am using 'lassogof' to compare the performance of different Lasso, Elastic Net and standard OLS regressions with a test and training data set.

The data set has 450k observations, and I am using around 50 coefficients. When comparing model performance, I realised that the lassogof table returns OLS R^2 results signifcantly higher, than reported in the OLS itself.

To simplify and show the problem, I used dataex to save a sample of 500, with four random variables. In the simplified example I am getting a training R^2 of 0.0618 (adj. R^2 = 0.0476) in the regression output table when running the OLS, but a R^2 of 0.2946 in the lassogof table. All other models seem to report similar R^2 for the training sample in the model output, and the lassogof table. In my complete model, the discrepancy is even stronger, with lassogof returning an R^2 in the 0.9 for OLS, but the OLS model itself just being around 0.2.

Based on the documentation I assumed I can use lassogof also to compare the performance of Lasso, Elastic Net with standard OLS regressions. Is there a reason the R^2 differ so signifcantly?

Thank you very much for your help,

Andrés

Code I am running:

Code:

splitsample , generate(sample) split(.75 .25) rseed(12345)
regress open n_sib brth_or sex edu if sample == 1
estimates store test_ols
lasso linear open n_sib brth_or sex edu if sample == 1
estimates store test_lasso
lassogof test_ols test_lasso, over(sample) postselection

Dataset:

Code:

clear

input byte(n_sib brth_or sex edu) double open byte pick
3 4 0 6 3.7 1
4 1 0 2 3.2 1
2 3 0 .   4 1
0 1 0 . 3.7 1
1 1 0 . 4.9 1
. . 0 4   4 1
2 2 0 . 4.7 1
2 3 0 . 4.2 1
6 2 0 . 4.1 1
2 2 0 1 4.3 1
2 1 0 4 3.5 1
. . 0 . 4.4 1
3 2 0 . 3.9 1
1 1 0 . 4.6 1
2 3 0 4 3.6 1
1 2 0 . 3.7 1
2 1 0 1 3.4 1
2 3 0 . 3.4 1
1 2 0 . 3.6 1
3 1 0 . 4.6 1
2 3 0 5 3.7 1
2 3 0 . 3.1 1
1 1 0 5   3 1
1 1 0 5 4.4 1
2 3 0 5 3.3 1
3 4 0 1 2.6 1
3 4 0 5   4 1
2 1 0 . 4.6 1
3 2 0 4 3.4 1
3 1 0 5 4.2 1
2 1 0 . 3.9 1
2 1 0 .   4 1
2 2 0 2 2.9 1
0 1 0 5 4.2 1
1 1 0 6 3.6 1
1 2 0 6 3.2 1
3 1 0 6 3.5 1
1 2 0 5 4.6 1
2 3 0 6 3.3 1
1 2 0 . 4.8 1
0 1 0 6 3.4 1
1 2 0 . 4.5 1
3 3 0 4   5 1
5 6 0 6 3.6 1
1 1 0 6   4 1
1 1 0 3 3.5 1
2 3 0 5 2.7 1
1 1 0 5 3.2 1
0 1 0 6 3.5 1
2 3 0 6 3.6 1
3 3 0 2 3.6 1
1 1 0 4 4.9 1
1 2 0 4 3.4 1
1 1 0 6 3.9 1
2 1 0 5 3.5 1
1 1 0 6 4.2 1
1 2 0 6 4.2 1
1 1 0 6 4.1 1
1 1 0 5   4 1
1 1 0 5   4 1
2 1 0 6 4.2 1
1 2 0 5 3.5 1
2 1 0 5 3.4 1
1 2 0 4 2.8 1
1 2 0 2 3.4 1
2 2 0 6 3.1 1
1 1 0 6   4 1
2 2 0 2 2.1 1
2 3 0 5 3.3 1
2 2 0 2 3.5 1
2 1 0 6 4.3 1
3 3 0 2 2.2 1
0 1 0 4 4.6 1
6 4 0 3 2.6 1
1 1 0 5 4.3 1
1 1 0 6 3.1 1
1 1 0 6 4.1 1
5 6 0 3 4.8 1
0 1 0 2 4.2 1
1 2 0 6 3.9 1
1 1 0 2 3.1 1
2 2 0 2 3.1 1
2 1 0 2 3.7 1
2 2 0 . 2.6 1
1 1 0 5 3.2 1
2 3 0 5 4.3 1
1 2 0 5 3.9 1
2 1 0 5 3.6 1
1 2 0 2 4.4 1
0 1 0 2 3.9 1
3 4 0 5   4 1
2 3 0 5 2.9 1
2 1 0 4 3.9 1
1 2 0 3 3.7 1
1 2 0 2 2.4 1
4 3 0 6   4 1
1 1 0 2 3.6 1
4 1 0 6 3.9 1
1 2 0 2 3.2 1
0 1 0 4 3.6 1
1 1 0 5 4.1 1
1 2 0 6 3.9 1
0 1 0 2 3.6 1
3 4 0 4 2.7 1
1 1 0 4 4.2 1
0 1 0 6 4.3 1
1 1 0 5 3.1 1
0 1 0 6 4.4 1
1 1 0 2 3.4 1
1 1 0 4 3.7 1
2 3 0 5 4.2 1
5 2 0 5 4.6 1
1 1 0 6 4.2 1
2 2 0 6 4.4 1
1 1 0 6 4.1 1
2 2 0 5 3.5 1
1 2 0 4 2.6 1
2 3 0 5 3.4 1
3 2 0 5 4.4 1
2 2 0 5 4.9 1
2 3 0 5 2.9 1
1 1 0 5   4 1
1 1 0 4 3.3 1
0 1 0 5 3.3 1
1 2 0 6   4 1
6 6 0 2   3 1
1 2 0 5 3.5 1
2 2 0 4 4.2 1
3 3 0 5 4.4 1
2 1 0 2 3.3 1
1 1 0 5 3.8 1
3 3 0 3 4.3 1
1 1 0 5 4.7 1
3 1 0 2   3 1
2 2 0 5 4.2 1
2 2 0 2 3.5 1
2 1 0 5 3.7 1
2 2 0 6   4 1
1 1 0 5 4.2 1
0 1 0 6 3.9 1
0 1 0 6   4 1
3 1 0 1 4.2 1
0 1 0 . 3.3 1
1 2 0 2 3.1 1
1 1 0 2 4.5 1
4 3 0 5 3.6 1
1 1 0 . 3.8 1
2 3 0 . 2.7 1
5 3 0 4 4.8 1
1 2 0 . 4.9 1
1 2 0 5 4.7 1
1 2 0 4 4.3 1
2 3 0 2 3.2 1
1 1 0 5 2.7 1
1 1 0 5 4.7 1
2 3 0 . 3.9 1
6 5 0 . 4.4 1
1 2 0 . 4.9 1
1 2 0 . 4.4 1
0 1 0 5 3.9 1
2 2 0 5 4.6 1
4 3 0 6   5 1
2 2 0 5 3.5 1
4 5 0 . 4.7 1
4 2 0 . 3.1 1
1 2 0 5 3.4 1
1 1 0 5 2.7 1
2 3 0 6 3.9 1
3 3 0 2 2.8 1
3 2 0 2 3.3 1
3 3 0 6 3.6 1
1 1 0 3 3.6 1
1 2 0 6 3.9 1
2 1 0 6 3.5 1
1 1 0 5 3.2 1
1 2 0 5 2.2 1
1 1 0 5   4 1
0 1 0 4 3.8 1
0 1 0 . 2.9 1
1 2 0 5 4.6 1
1 2 0 5 4.1 1
1 2 0 . 4.2 1
1 1 0 5 4.8 1
1 2 0 4 4.8 1
3 4 0 3 4.5 1
1 2 0 6 4.3 1
2 2 0 5 2.9 1
1 2 0 4 3.3 1
3 3 0 1 3.6 1
1 1 0 6 3.1 1
1 2 0 4 3.6 1
6 1 0 . 4.3 1
1 1 0 5 3.6 1
4 1 0 5 4.9 1
1 2 0 4 4.2 1
1 2 0 . 3.6 1
0 1 0 . 4.2 1
2 3 0 . 4.3 1
4 1 0 . 4.6 1
2 3 0 4 3.4 1
3 4 0 .   4 1
2 2 0 . 2.9 1
0 1 0 . 3.2 1
1 2 0 . 3.5 1
1 1 0 .   4 1
2 3 0 . 3.2 1
0 1 0 5 2.7 1
1 1 0 . 3.5 1
2 2 0 . 3.4 1
1 1 0 . 3.5 1
6 6 0 . 4.4 1
2 3 0 . 3.5 1
1 1 0 . 3.6 1
1 2 0 . 2.7 1
1 2 0 4 3.5 1
1 2 0 . 4.3 1
1 1 0 . 3.5 1
1 1 0 . 3.9 1
1 1 0 4 4.6 1
0 1 0 6 4.4 1
2 2 0 5 3.4 1
3 3 0 5 3.9 1
1 1 0 . 4.2 1
0 1 0 . 3.6 1
1 2 0 6 4.6 1
1 1 0 6 2.7 1
2 2 0 5 4.9 1
5 1 0 6 3.8 1
1 2 0 . 4.2 1
2 3 0 6   4 1
3 2 0 3 2.3 1
. . 0 6 3.5 1
2 1 0 . 4.6 1
1 1 0 . 4.7 1
1 1 0 . 3.3 1
0 1 0 . 4.3 1
1 2 0 . 3.7 1
0 1 0 . 3.7 1
1 2 0 4 3.7 1
. . 0 . 3.9 1
0 1 0 . 2.8 1
2 2 0 4 4.2 1
1 2 0 . 4.1 1
1 1 0 . 4.3 1
1 2 0 . 3.6 1
2 3 0 . 3.5 1
2 1 0 . 4.6 1
1 2 0 . 3.3 1
5 4 0 . 3.1 1
0 1 0 . 3.6 1
1 1 0 . 4.7 1
1 2 0 . 4.9 1
2 1 0 4 3.1 1
1 1 0 . 3.5 1
1 1 0 . 3.6 1
. . 0 . 3.8 1
1 1 0 5   5 1
3 2 0 4   3 1
3 4 0 4 4.4 1
. . 0 . 4.8 1
2 3 0 . 3.8 1
2 1 0 . 3.1 1
1 2 0 . 4.5 1
2 2 0 5 3.6 1
5 1 0 4 3.5 1
1 1 0 4 3.8 1
1 1 0 5 4.2 1
2 1 0 5 3.4 1
2 2 0 5 3.5 1
1 2 0 6 3.6 1
2 2 0 2 4.2 1
4 5 0 . 4.1 1
2 3 0 4 4.5 1
1 2 0 5 3.1 1
1 1 0 6 3.3 1
1 1 0 6 3.1 1
6 6 0 4 3.9 1
5 1 0 4   4 1
1 1 0 6 4.1 1
0 1 0 5 4.7 1
1 2 0 6 3.2 1
1 2 0 5 2.7 1
1 2 0 4 3.7 1
1 2 0 5 3.5 1
1 1 0 5 2.6 1
2 3 0 3 2.2 1
. . 0 6 3.8 1
0 1 0 6 4.4 1
6 4 0 4 2.9 1
2 1 0 6 2.9 1
0 1 0 4 2.4 1
2 3 0 5 2.2 1
. . 0 2 4.3 1
0 1 0 4   3 1
2 1 0 5 3.3 1
1 2 0 2 3.6 1
4 1 0 2 4.5 1
1 2 0 6 4.3 1
1 1 0 5 3.6 1
1 1 0 6 3.9 1
0 1 0 6 3.7 1
1 2 0 . 4.1 1
3 2 0 2 3.3 1
0 1 0 5 2.9 1
1 1 0 3 4.1 1
2 1 0 2 3.2 1
2 1 0 2 3.2 1
1 2 0 5 3.2 1
2 2 0 2 3.6 1
2 2 0 2 3.3 1
3 3 0 6 3.3 1
1 2 0 6 3.6 1
1 1 0 2 2.7 1
. . 0 2 3.9 1
1 1 0 2 3.5 1
2 3 0 2 2.7 1
. . 0 5 3.6 1
1 1 0 2   4 1
3 4 0 4 4.4 1
1 2 0 5 2.2 1
0 2 0 5 2.6 1
2 3 0 6 2.9 1
2 1 0 5 4.8 1
2 1 0 3   3 1
. . 0 2 3.8 1
1 2 0 5   4 1
2 1 0 2 3.8 1
3 2 0 1 4.3 1
3 4 0 2 4.2 1
. . 0 5 3.6 1
2 1 0 2   3 1
2 1 0 2 4.3 1
3 1 0 2 3.7 1
0 1 0 5 4.7 1
2 1 0 1 3.4 1
. . 0 5   4 1
2 2 0 3 3.5 1
3 1 0 1 3.4 1
5 1 0 1 3.2 1
4 1 1 . 3.6 1
1 2 1 2 3.6 1
1 1 1 . 3.7 1
1 1 1 5 3.7 1
3 1 1 5   3 1
6 5 1 2 3.8 1
2 3 1 . 4.3 1
3 3 1 3 3.7 1
1 2 1 4 3.8 1
3 1 1 4   3 1
0 1 1 5 4.7 1
3 1 1 . 3.6 1
2 2 1 5 3.6 1
3 3 1 . 2.6 1
0 1 1 4   4 1
1 1 1 4 4.4 1
1 1 1 3 3.5 1
3 3 1 3 3.9 1
4 4 1 1 3.8 1
1 1 1 2 3.1 1
0 3 1 . 4.4 1
2 1 1 . 3.9 1
2 2 1 5 3.2 1
1 1 1 5 3.1 1
2 2 1 5 4.3 1
2 1 1 5 3.1 1
1 1 1 5 3.1 1
1 2 1 6 3.9 1
2 2 1 5 4.2 1
1 2 1 6 4.1 1
0 1 1 2   3 1
2 2 1 6   3 1
2 3 1 2 3.7 1
1 2 1 5 3.3 1
1 1 1 5 3.7 1
2 1 1 5 2.1 1
1 1 1 5   4 1
2 3 1 2 4.2 1
0 1 1 2 2.6 1
3 4 1 6 4.3 1
5 1 1 3 3.4 1
1 2 1 5 4.6 1
2 1 1 4 3.8 1
0 1 1 2   4 1
2 3 1 2 3.5 1
3 1 1 4 3.7 1
2 2 1 6 3.9 1
2 3 1 2   4 1
1 1 1 4 4.6 1
. . 1 2 3.7 1
0 1 1 1 2.9 1
3 3 1 3 4.6 1
2 1 1 5 2.9 1
2 1 1 5 2.6 1
1 2 1 6 3.7 1
3 4 1 2 3.9 1
2 2 1 6 4.6 1
1 1 1 . 2.7 1
1 1 1 . 3.9 1
2 1 1 . 3.6 1
2 2 1 2 3.3 1
1 2 1 . 4.3 1
1 2 1 . 3.7 1
3 4 1 5   4 1
0 1 1 6   5 1
0 1 1 5 3.8 1
2 6 1 5 4.1 1
5 6 1 5 2.9 1
2 1 1 6 4.1 1
0 1 1 2 3.5 1
1 2 1 2 3.5 1
3 2 1 4 3.7 1
2 2 1 5 3.6 1
1 1 1 . 4.6 1
6 4 1 6 4.4 1
5 5 1 2 2.2 1
2 2 1 6 4.6 1
5 5 1 1 4.4 1
1 1 1 6 4.1 1
6 1 1 3 2.2 1
2 2 1 . 4.2 1
0 1 1 . 4.4 1
0 1 1 4 4.6 1
3 2 1 . 4.1 1
1 1 1 .   4 1
6 1 1 .   3 1
2 3 1 . 3.6 1
3 2 1 4 3.9 1
1 1 1 . 2.9 1
2 1 1 .   4 1
1 1 1 5 4.2 1
1 2 1 . 3.9 1
3 1 1 1 3.4 1
1 2 1 4 4.3 1
3 2 1 6 3.5 1
0 1 1 . 2.9 1
1 1 1 . 3.7 1
3 2 1 4 4.1 1
4 3 1 . 2.8 1
0 1 1 4 3.6 1
1 1 1 5 3.5 1
1 1 1 4 3.4 1
5 4 1 5 4.2 1
1 1 1 5 3.9 1
1 2 1 1 3.7 1
2 1 1 4 4.9 1
1 2 1 . 3.6 1
1 1 1 4 4.1 1
3 2 1 4 3.8 1
1 1 1 . 3.4 1
. . 1 4 3.8 1
3 2 1 5 4.2 1
2 2 1 . 4.3 1
2 2 1 . 4.1 1
3 1 1 . 4.6 1
1 1 1 . 2.5 1
1 1 1 . 2.9 1
0 1 1 5 2.6 1
1 1 1 5 4.1 1
1 2 1 4 3.9 1
2 1 1 5 3.1 1
1 2 1 2 3.7 1
4 3 1 . 3.8 1
3 1 1 2 3.6 1
1 1 1 . 4.3 1
5 6 1 6 3.7 1
4 5 1 6 3.8 1
1 4 1 5 4.8 1
1 2 1 5 2.2 1
1 2 1 6 3.8 1
0 1 1 . 4.3 1
6 6 1 . 3.7 1
1 1 1 5 4.6 1
1 2 1 3   4 1
. . 1 5 3.8 1
. . 1 6   3 1
0 1 1 5 4.6 1
1 1 1 6 3.4 1
1 2 1 6 3.5 1
2 3 1 6 4.3 1
0 1 1 5 3.4 1
0 2 1 1 2.9 1
4 2 1 6 4.3 1
1 2 1 2 2.6 1
2 2 1 5 4.2 1
2 2 1 2 3.4 1
3 3 1 2 4.3 1
1 2 1 2 2.2 1
2 2 1 6 4.9 1
1 2 1 5 4.1 1
1 2 1 1 2.8 1
3 1 1 5 4.5 1
2 2 1 4 3.5 1
1 2 1 6 4.5 1
3 3 1 3 3.3 1
4 4 1 2 3.3 1
1 1 1 5   5 1
2 1 1 6 4.6 1
3 1 1 1 2.8 1
1 2 1 2 3.2 1
2 2 1 . 4.1 1
end
label values n_sib N_SIB
label def N_SIB 0 " 0", modify
label def N_SIB 1 " 1", modify
label def N_SIB 2 " 2", modify
label def N_SIB 3 " 3", modify
label def N_SIB 4 " 4", modify
label def N_SIB 5 " 5", modify
label def N_SIB 6 " 6 or more", modify
label values brth_or BRTH_OR
label def BRTH_OR 1 " First born", modify
label def BRTH_OR 2 " Second born", modify
label def BRTH_OR 3 " Third born", modify
label def BRTH_OR 4 " Fourth born", modify
label def BRTH_OR 5 " Fifth born", modify
label def BRTH_OR 6 " Sixth or subsequent born", modify
label values sex SEX
label def SEX 0 "female", modify
label def SEX 1 "male", modify
label values edu EDU
label def EDU 1 " Did not complete GCSE / CSE / O-Levels", modify
label def EDU 2 " Completed GCSE / CSE / O-Levels", modify
label def EDU 3 " Completed post-16 vocational course", modify
label def EDU 4 " A-Levels", modify
label def EDU 5 " Undergraduate degree", modify
label def EDU 6 " Postgraduate degree", modify

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OLS R^2 for large datasets inaccurate (lassogof)

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Home / Data Cleaning / Data management / Data Processing / OLS R^2 for large datasets inaccurate (lassogof)
OLS R^2 for large datasets inaccurate (lassogof)