# Re: [R] equivalent of stata command in R‏

From: mike mick <saint-filth_at_hotmail.com>
Date: Wed, 09 Jun 2010 09:54:58 +0000

OK! sorry thats my fault,

here the translations of the stata commands 1st step is to get the mean values of the variables, well that doesnt need explanation i guess,

2nd step is to estimate the model on panel data estimation method which is:
mdl<-plm(lnLP~lnC+lnL+lnM+lnE+Eco+Inno+Eco*Inno+Eco*lnM+Eco*lnE+year,data=newdata,model="within") and basically i need to get the marginal effect of variable "Eco" at the sample mean (step 3) but i am not that good in R so any additional help is wlcome!
Thanks
From: saint-filth_at_hotmail.com
To: r-help_at_r-project.org
Subject:
Date: Wed, 9 Jun 2010 09:45:16 +0000

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

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!
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