How is the LM test calculated for Gretl for just a standard model.

I just ran a model

Model 5: OLS, using observations 1-2017

Dependent variable: nettfa

             coefficient   std. error   t-ratio    p-value

  ---------------------------------------------------------

  const      -20.9850      2.47202      -8.489    3.98e-017 ***

  inc          0.770583    0.0614520    12.54     8.73e-035 ***

  age25        0.0251267   0.00259339    9.689    9.96e-022 ***

  male         2.47793     2.04778       1.210    0.2264  

  e401k        6.88622     2.12327       3.243    0.0012    ***

Mean dependent var   13.59498   S.D. dependent var   47.59058

Sum squared resid     3982124   S.E. of regression   44.48805

R-squared            0.127868   Adjusted R-squared   0.126134

F(4, 2012)           73.74763   P-value(F)           2.18e-58

Log-likelihood      -10514.46   Akaike criterion     21038.91

Schwarz criterion    21066.96   Hannan-Quinn         21049.21

When I save the residuals squared to run the Pagan test I get this

Model 6: OLS, using observations 1-2017

Dependent variable: usq5

	Coefficient	Std. Error	t-ratio	p-value
const	-4573.55	1848.7	-2.4739	0.01345	**
inc	112.358	45.9568	2.4449	0.01458	**
age25	4.84866	1.93946	2.5000	0.01250	**
male	2331.25	1531.43	1.5223	0.12810
e401k	1164.83	1587.89	0.7336	0.46330

Mean dependent var	1974.280	S.D. dependent var	33367.52
Sum squared resid	2.23e+12	S.E. of regression	33270.33
R-squared	0.007789	Adjusted R-squared	0.005817
F(4, 2012)	3.948695	P-value(F)	0.003387
Log-likelihood	-23861.35	Akaike criterion	47732.70
Schwarz criterion	47760.75	Hannan-Quinn	47742.99

The LM test from my understanding is n*r^2, which here would be 15.71

Using gretl’s built in test I get the following:

Breusch-Pagan test for heteroskedasticity

OLS, using observations 1-2017

Dependent variable: scaled uhat^2

             coefficient   std. error    t-ratio   p-value

  --------------------------------------------------------

  const      -2.31657      0.936391      -2.474    0.0134  **

  inc         0.0569109    0.0232777      2.445    0.0146  **

  age25       0.00245591   0.000982363    2.500    0.0125  **

  male        1.18081      0.775689       1.522    0.1281

  e401k       0.590001     0.804287       0.7336   0.4633

  Explained sum of squares = 4485.49

Test statistic: LM = 2242.746588,

with p-value = P(Chi-square(4) > 2242.746588) = 0.000000

How did the LM test become so big for this model?