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Re: st: FW: Model SS/R-square in nl
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: FW: Model SS/R-square in nl
Date
Thu, 30 Jun 2011 21:04:31 +0100
No, it is not a bug.
Your constant may not be significant by itself, but the model is
different. R-squares for different models are often difficult to
compare effectively.
Plot the fitted curves and the data to see what it is going on.
In my experience, especially with nonlinear models, it is far better
to rely on physical, biological, economic or other scientific
understanding to choose the better model and to compare fitted curves
with the data, rather than to rely blindly on a significance test.
Does it make sense to force the curve through the origin?
Nick
On Thu, Jun 30, 2011 at 6:06 PM, CJ Lan <[email protected]> wrote:
> I was using nl to run a 3-parameter NLS model estimation and got R2=0.28
> (see the first output). Since the parameter b0 is insignificant, I drop
> it and re-estimate it again. This time, I got the wrong R2 (=0.86 in
> the 2nd output). It is apparent that either the "Model SS" or "Total
> SS" is wrongly calculated. Is this bug? Thank you for help.
>
> (1)
> . nl exp3 : passby A in 1/152
> (obs =152)
> Iteration 0: residual SS =3D 29741.65
> Iteration 1: residual SS =3D 28448.53
> Iteration 2: residual SS =3D 28316.37
> Iteration 3: residual SS =3D 28315.61
> Iteration 4: residual SS =3D 28315.6
> Iteration 5: residual SS =3D 28315.6
> Iteration 6: residual SS =3D 28315.6
> Iteration 7: residual SS =3D 28315.6
> Source | SS df MS Number of obs =152
> -------------+------------------------------ F( 2, 149) =29.25
> Model | 11118.3472 2 5559.1736 Prob > F =0.0000
> Residual | 28315.6009 149 190.03759 R-squared =0.2819
> -------------+------------------------------ Adj R-squared =0.2723
> Total | 39433.9482 151 261.151975 Root MSE =13.78541
> Res. dev. =1225.905
> 3-parameter asymptotic regression, passby = b0 + b1*b2^A
> ------------------------------------------------------------------------
> passby | Coef. Std. Err. t P>|t| 95% Conf.Interval]
> -------------+----------------------------------------------------------
> b0 | 11.59292 10.68695 1.08 0.280 -9.52 32.71048
> b1 | 34.10476 9.433555 3.62 0.000 15.4 52.74559
> b2 | .998132 .0011685 854.19 0.000 .995 1.000441
> ------------------------------------------------------------------------
> * Parameter b0 taken as constant term in model & ANOVA table
> (SEs, P values, CIs, and correlations are asymptotic approximations)
>
> (2)
> . nl exp2 : passby A in 1/152
> (obs =3D 152)
> Iteration 0: residual SS =3D 29510.02
> Iteration 1: residual SS =3D 28427.14
> Iteration 2: residual SS =3D 28426.97
> Iteration 3: residual SS =3D 28426.97
> Source | SS df MS Number of obs =152
> -------------+------------------------------ F( 2, 150) =468.32
> Model | 177506.602 2 88753.3012 Prob > F =0.0000
> Residual | 28426.9672 150 189.513115 R-squared =0.8620
> -------------+------------------------------ Adj R-squared =0.8601
> Total | 205933.57 152 1354.82612 Root MSE =13.76638
> Res. dev. =1226.502
> 2-parameter exp. growth curve, passby =3D b1*b2^A
> ------------------------------------------------------------------------
> passby | Coef. Std. Err. t P>|t|[95% Conf.interval]
> -------------+----------------------------------------------------------
> b1 | 44.54536 2.038308 21.85 0.000 40.51785 48.57286
> b2 | .9988862 .0001727 5783.22 0.000 .9985449 .9992275
> ------------------------------------------------------------------------
> (SEs, P values, CIs, and correlations are asymptotic approximations)
>
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