Dear STATALIST community,
Hi,
I am trying to plot secular trends using fractional polynomials. I would like to identify a set of fractional polynomials of the variable named "year5" using the fp command.
THe outcome entitled "explain2" is an indicator variable, 0 vs. 1 (yes).
The issue I'm experiencing is that the fp command does not generate the p values, which should be able to tell me whether having one or more fractional polynomial terms significantly reduces the deviance statistic.
I wanted to see if anybody could tell me why it doesn't generate the p-values in my case.
I also wanted to see if there are ways to say that a set of fractional polynomials are "significantly better" than the others based on the given deviance statistics.
Thank you in advance!
. fp <year5>, powers (-2 -1 -0.5 0 0.5 1 2 3) dimension(3): logit explain2 <year5> [pweight=saqwt]
(fitting 164 models)
(....10%....20%....30%....40%....50%....60%....70% ....80%....90%....100%)
Fractional polynomial comparisons:
--------------------------------------------------------------------
year5 | df Deviance Dev. dif. P(*) Powers
-------------+------------------------------------------------------
omitted | 0 2.971e+09 7236985 --
linear | 1 2.965e+09 1348577 -- 1
m = 1 | 2 2.964e+09 298505 -- 3
m = 2 | 4 2.964e+09 241690 -- 3 3
m = 3 | 6 2.964e+09 0 -- -2 -1 -.5
--------------------------------------------------------------------
(*) deviance difference test not valid with robust variance estimation
Logistic regression Number of obs = 198,962
Wald chi2(3) = 398.37
Prob > chi2 = 0.0000
Log pseudolikelihood = -1.482e+09 Pseudo R2 = 0.0024
------------------------------------------------------------------------------
| Robust
explain2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
year5_1 | -6.831685 .7743655 -8.82 0.000 -8.349413 -5.313956
year5_2 | 23.15228 2.420883 9.56 0.000 18.40744 27.89713
year5_3 | -22.23864 2.18047 -10.20 0.000 -26.51228 -17.96499
_cons | 6.292316 .5311706 11.85 0.000 5.251241 7.333391
------------------------------------------------------------------------------
Hi,
I am trying to plot secular trends using fractional polynomials. I would like to identify a set of fractional polynomials of the variable named "year5" using the fp command.
THe outcome entitled "explain2" is an indicator variable, 0 vs. 1 (yes).
The issue I'm experiencing is that the fp command does not generate the p values, which should be able to tell me whether having one or more fractional polynomial terms significantly reduces the deviance statistic.
I wanted to see if anybody could tell me why it doesn't generate the p-values in my case.
I also wanted to see if there are ways to say that a set of fractional polynomials are "significantly better" than the others based on the given deviance statistics.
Thank you in advance!
. fp <year5>, powers (-2 -1 -0.5 0 0.5 1 2 3) dimension(3): logit explain2 <year5> [pweight=saqwt]
(fitting 164 models)
(....10%....20%....30%....40%....50%....60%....70% ....80%....90%....100%)
Fractional polynomial comparisons:
--------------------------------------------------------------------
year5 | df Deviance Dev. dif. P(*) Powers
-------------+------------------------------------------------------
omitted | 0 2.971e+09 7236985 --
linear | 1 2.965e+09 1348577 -- 1
m = 1 | 2 2.964e+09 298505 -- 3
m = 2 | 4 2.964e+09 241690 -- 3 3
m = 3 | 6 2.964e+09 0 -- -2 -1 -.5
--------------------------------------------------------------------
(*) deviance difference test not valid with robust variance estimation
Logistic regression Number of obs = 198,962
Wald chi2(3) = 398.37
Prob > chi2 = 0.0000
Log pseudolikelihood = -1.482e+09 Pseudo R2 = 0.0024
------------------------------------------------------------------------------
| Robust
explain2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
year5_1 | -6.831685 .7743655 -8.82 0.000 -8.349413 -5.313956
year5_2 | 23.15228 2.420883 9.56 0.000 18.40744 27.89713
year5_3 | -22.23864 2.18047 -10.20 0.000 -26.51228 -17.96499
_cons | 6.292316 .5311706 11.85 0.000 5.251241 7.333391
------------------------------------------------------------------------------
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