Re: [R] equivalent of stata command in R

From: Joris Meys <jorismeys_at_gmail.com>
Date: Wed, 09 Jun 2010 11:41:18 +0200

It helps if you translate the Stata commands. Not everybody is fluent in those. It would even help more if you would enlight us about the function you used to fit the model. Getting the marginal effects is not that hard at all, but how depends a bit on the function you used to estimate the model.

You can try
predict(your_model,type="terms",terms="the_term_you're_interested_in")

For exact information, look at the respective predict function, eg if you use lme, do ?predict.lme
Be aware of the fact that R normally choses the correct predict function without you having to specify it. predict() works for most model objects. Yet, depending on the model eacht predict function can have different options or different functionality. That information is in the help files of the specific function.

Cheers
Joris

On Wed, Jun 9, 2010 at 11:28 AM, mike mick <saint-filth_at_hotmail.com> wrote:
>
> Dear all,
>
> I need to use R for one estimation, and i have readily available  stata command, but i need also the R version of the same command.
> the estimation in stata is as following:
>   1. Compute mean values of relevant variables
>
>
>
> . sum inno lnE lnM
>
>
>
>    Variable |       Obs        Mean    Std. Dev.       Min        Max
>
> -------------+--------------------------------------------------------
>
>        inno |    146574    .0880374    .2833503          0          1
>
>         lnE |    146353    .9256239    1.732912  -4.473922   10.51298
>
>         lnM |    146209    4.281903    1.862192  -4.847253   13.71969
>
>
>
>        2. Estimate model
>
>
>
> . xi: xtreg lnLP lnC lnL lnE lnM eco inno eco_inno eco_lnE eco_lnM i.year, fe i(stno)
>
> i.year            _Iyear_1997-1999    (naturally coded; _Iyear_1997 omitted)
>
>
>
> Fixed-effects (within) regression               Number of obs      =    146167
>
> Group variable (i): stno                        Number of groups   =     48855
>
>
>
> R-sq:  within  = 0.9908                         Obs per group: min =         1
>
>       between = 0.9122                                        avg =       3.0
>
>       overall = 0.9635                                        max =         3
>
>
>
>                                                F(11,97301)        = 949024.29
>
> corr(u_i, Xb)  = 0.2166                         Prob > F           =    0.0000
>
>
>
> ------------------------------------------------------------------------------
>
>        lnLP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
>
> -------------+----------------------------------------------------------------
>
>         lnC |   .0304896   .0009509    32.06   0.000     .0286258    .0323533
>
>         lnL |  -.9835998   .0006899 -1425.74   0.000     -.984952   -.9822476
>
>         lnE |   .0652658   .0009439    69.14   0.000     .0634158    .0671159
>
>         lnM |   .6729931   .0012158   553.53   0.000       .67061    .6753761
>
>         eco |   .0610348   .0177048     3.45   0.001     .0263336     .095736
>
>        inno |   .0173824   .0058224     2.99   0.003     .0059706    .0287943
>
>    eco_inno |   .0080325   .0110815     0.72   0.469    -.0136872    .0297522
>
>     eco_lnE |   .0276226    .004059     6.81   0.000      .019667    .0355781
>
>     eco_lnM |  -.0214237   .0039927    -5.37   0.000    -.0292494   -.0135981
>
>  _Iyear_1998 |  -.0317684   .0013978   -22.73   0.000     -.034508   -.0290287
>
>  _Iyear_1999 |  -.0647261   .0027674   -23.39   0.000    -.0701501   -.0593021
>
>       _cons |   1.802112    .009304   193.69   0.000     1.783876    1.820348
>
> -------------+----------------------------------------------------------------
>
>     sigma_u |  .38142386
>
>     sigma_e |   .2173114
>
>         rho |  .75494455   (fraction of variance due to u_i)
>
> ------------------------------------------------------------------------------
>
> F test that all u_i=0:     F(48854, 97301) =     3.30        Prob > F = 0.0000
>
>
>
>        3. Compute marginal effect of eco at sample mean
>
>
>
> . nlcom (_b[eco]+_b[inno]*.0880374+_b[eco_lnE]*.9256239+_b[eco_lnM]*4.281903)
>
>
>
>       _nl_1:  _b[eco]+_b[inno]*.0880374+_b[eco_lnE]*.9256239+_b[eco_lnM]*4.281903
>
>
>
> ------------------------------------------------------------------------------
>
>        lnLP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
>
> -------------+----------------------------------------------------------------
>
>       _nl_1 |  -.0036011    .008167    -0.44   0.659    -.0196084    .0124061
>
> ------------------------------------------------------------------------------
>
>
>
> in fact i can find the mean of the variables ( step 1) and extimate the model (step 2) but i couldnt find the equivalent of step 3 (compute marginal effect of eco at sample mean). Can someone help me for this issue?
>

> Cheers!
>
>
> _________________________________________________________________
>
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
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>

-- 
Joris Meys
Statistical consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

tel : +32 9 264 59 87
Joris.Meys_at_Ugent.be
-------------------------------
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Received on Wed 09 Jun 2010 - 09:43:44 GMT

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