From: mike mick <saint-filth_at_hotmail.com>

Date: Wed, 09 Jun 2010 11:27:44 +0000

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 09 Jun 2010 - 12:01:56 GMT

Date: Wed, 09 Jun 2010 11:27:44 +0000

Thanx for your response,

yeah, i know i didnst specified the indexes
when i wrote the 2nd mail, in fact in the 1st mail i wrote already that
i dont have problem with the estimation of the model... thats the
reason why i didnt write in fact since the issue is not to estimate the
model but to get the marginal effect,

anyway, i figured out that predict(), doesnt work for panel data...
and

well, my problem is that contrary to your guess, i couldnt figure out
the rest of the calculations... since i am not that experienced in R.
one last help of yours would be quite helpful to get rid of this silly problem!
Thanx again...

*> Date: Wed, 9 Jun 2010 12:40:42 +0200
**> Subject: Re: [R] equivalent of stata command in R
**> From: jorismeys_at_gmail.com
**> To: saint-filth_at_hotmail.com
**> CC: r-help_at_r-project.org
**>
**> plm does not have a predict function, so forget my former mail. To get
**> to the coefficients, you just :
**> coef(mdl)
**>
**> The rest of the calculations you can figure out I guess.
**>
**> I'm also not sure if you're doing what you think you're doing. You
**> never specified the index stno in your pml call. Read the help files
**> again. And while you're at it, read the posting guide for the list as
**> well:
**> http://www.R-project.org/posting-guide.html
**>
**> Cheers
**> Joris
**>
**>
**> On Wed, Jun 9, 2010 at 11:54 AM, mike mick <saint-filth_at_hotmail.com> wrote:
**> >
**> >
**> >
**> >
**> >
**> >
**> >
**> >
**> > From: saint-filth_at_hotmail.com
**> > To: saint-filth_at_hotmail.com
**> > Subject: RE:
*

> > Date: Wed, 9 Jun 2010 09:53:20 +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!

*> >
**> >
**> >
**> > ______________________________________________
**> > R-help_at_r-project.org mailing list
**> > https://stat.ethz.ch/mailman/listinfo/r-help
**> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
**> > and provide commented, minimal, self-contained, reproducible code.
**> >
**> >
**>
**>
**>
**> --
**> 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
**> -------------------------------
**> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
*

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