not exactly sure what the problem is, but -fracpoly- also centers the
transformed variables:
(I'm using Stata 10 by the way)
. sysuse auto.dta, clear
(1978 Automobile Data)
. fracpoly reg price mpg, noscaling
........
-> gen double Impg__1 = mpg^-2-.0022047077 if e(sample)
-> gen double Impg__2 = mpg^-2*ln(mpg)-.0067432752 if e(sample)
Source | SS df MS Number of obs = 74
-------------+------------------------------ F( 2, 71) = 23.76
Model | 254596301 2 127298151 Prob > F = 0.0000
Residual | 380469095 71 5358719.65 R-squared = 0.4009
-------------+------------------------------ Adj R-squared = 0.3840
Total | 635065396 73 8699525.97 Root MSE = 2314.9
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Impg__1 | 7070957 3149820 2.24 0.028 790394.2 1.34e+07
Impg__2 | -2406544 1314637 -1.83 0.071 -5027857 214768.4
_cons | 5281.835 317.7363 16.62 0.000 4648.287 5915.384
------------------------------------------------------------------------------
Deviance: 1353.51. Best powers of mpg among 44 models fit: -2 -2.
. fracpred xhat
. nlcom _b[Impg__1] *((20^-2)-0.0022047077)+
_b[Impg__2]*((20^-2)*(log(20))-0.0067432752)+_b[_cons]
_nl_1: _b[Impg__1] *((20^-2)-0.0022047077)+
_b[Impg__2]*((20^-2)*(log(20))-0.0067432752)+_b[_cons]
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_nl_1 | 5574.419 332.8972 16.75 0.000 4910.641 6238.197
------------------------------------------------------------------------------
. list xhat if mpg==20
+----------+
| xhat |
|----------|
4. | 5574.419 |
8. | 5574.419 |
34. | 5574.419 |
+----------+
.
Tim
On Thu, Mar 12, 2009 at 7:16 PM, Chris Witte <[email protected]> wrote:
> I am using stata 9.2. Here are the coefficient estimates from my _fracpoly_ analysis:
>
> study1twsg Coef. Std. Err. t P>t [95% Conf. Interval]
> Itemp__1 15093.12 4454.121 3.39 0.012 4560.802 25625.45
> Itemp__2 -10666.18 3136.84 -3.40 0.011 -18083.63 -3248.734
> Itemp__3 1945.114 569.9302 3.41 0.011 597.4436 3292.785
> _cons .0053322 .0008244 6.47 0.000 .0033828 .0072815
> Deviance: -113.24. Best powers of temp among 164 models fit: -2 -2 -2.
> Fractional polynomial model comparisons:
> temp df Deviance Res. SD Gain P(term) Powers
> Not in model 0 -101.249 .002545 -- --
> Linear 1 -101.599 .002641 0.000 0.603 1
> m = 1 2 -101.796 .002617 0.197 0.714 3
> m = 2 4 -104.278 .00248 2.679 0.508 3 3
> m = 3 6 -113.237 .001764 11.638 0.196 -2 -2 -2
>
> so, given these coefficients and my 'x' having a value of 25, I think my linear regression equation for predicting y should be:
>
> y=.0053322+(15093.12*25^-2)+(-10666.18*25^-2*log(25))+1945.114*25^-2*(log(25))^2
>
> if this is correct, the equation is not yielding the same estimates as _fracpred_. The above equation returns an answer of 1.467, while _fracpred_ (which I believe is correct) gives 0.003.
>
> What am I doing wrong?
>
> thanks a bunch in advance,
> Chris
>
>
> p.s. I know I'm not supposed to reply to a different thread with a new question, but I've tried to send this message twice as a new message and it has not gotten through.
>
>
>
>
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
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