st: predict after Hakulinen-Tenkanen model

[Enzo Coviello <enzo.coviello@alice.it>]

Dear Stata Users

I tried to estimate a relative survival model using Hakulinen-Tenkanen 
model. To this aim an user defined link in glm command is available , i.e. 
relsurv.ado (thanks to Robert Gutierrez).

Data are grouped by intervals. Here ns  means number of survivor in the 
interval, n_prime means effective number at risk and p_star expected 
survivor probability

use grouped,  clear

xi: glm ns i.end i.sex i.year8594 i.agegrp , fam(bin n_prime) link(relsurv 
p_star)

i.end             _Iend_1-5           (naturally coded; _Iend_1 omitted)
i.sex             _Isex_1-2           (naturally coded; _Isex_1 omitted)
i.year8594        _Iyear8594_0-1      (naturally coded; _Iyear8594_0 omitted)
i.agegrp          _Iagegrp_0-3        (naturally coded; _Iagegrp_0 omitted)
note: n_prime has non-integer values
note: ns has non-integer values

Iteration 0:   log likelihood = -251.41742
Iteration 1:   log likelihood = -235.65223
Iteration 2:   log likelihood = -235.57195
Iteration 3:   log likelihood = -235.57191
Iteration 4:   log likelihood = -235.57191

Generalized linear models                          No. of obs      =        80
Optimization     : ML                              Residual df     =        70
                                                   Scale parameter =         1
Deviance         =  116.9152321                    (1/df) Deviance =  1.670218
Pearson          =  115.0068631                    (1/df) Pearson  =  1.642955

Variance function: V(u) = u*(1-u/n_prime)          [Binomial]
Link function    : g(u) = log(-log(u/ps))          [Hakulinen-Tenkanen]

                                                   AIC             =  6.139298
Log likelihood   = -235.5719096                    BIC             = -585.9934

------------------------------------------------------------------------------
             |                 OIM
          ns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     _Iend_2 |   -.178603   .0928487    -1.92   0.054     -.360583     .003377
     _Iend_3 |  -.4246247   .1084161    -3.92   0.000    -.6371162   -.2121331
     _Iend_4 |   -.656375   .1306922    -5.02   0.000    -.9125271    -.400223
     _Iend_5 |  -.7934491   .1507283    -5.26   0.000    -1.088871   -.4980271
     _Isex_2 |  -.0409842   .0770988    -0.53   0.595    -.1920951    .1101267
_Iyear8594_1 |  -.3201563   .0746229    -4.29   0.000    -.4664145   -.1738981
  _Iagegrp_1 |  -.1510256   .1547811    -0.98   0.329    -.4543909    .1523397
  _Iagegrp_2 |   .0473305   .1418327     0.33   0.739    -.2306564    .3253174
  _Iagegrp_3 |   .2958798   .1497402     1.98   0.048     .0023945    .5893651
       _cons |  -2.490132   .1470346   -16.94   0.000    -2.778315    -2.20195
------------------------------------------------------------------------------


In the following predict fails to achieve the requested quantities:

. predict mu
(option mu assumed; predicted mean ns)

. tab mu

  predicted |
    mean ns |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         80      100.00      100.00
------------+-----------------------------------
      Total |         80      100.00




. predict r_sd, deviance standardized
no observations
r(2000);



Is there some problem in this user defined link or in glm command, or 
did  I miss something in predict after glm?

Thanks for your attention.

Enzo



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