Re: [R] Dummy variables model

From: Tobias Muhlhofer <t.muhlhofer_at_lse.ac.uk>
Date: Tue 06 Sep 2005 - 05:11:09 EST

Dang! That's awesome!!!!!

Being at the end of an empirical PhD in which all the econometrics was done in R, I was already a longtime R enthusiast, but you never stop learning more neat features!!!

YAY to everyone involved in R's development!!!!

Toby

Adaikalavan Ramasamy wrote:
> You will need to ensure that firm is a factor and not numerical (i.e.
> continuous). Here is an example
>
>
> firm <- factor( sample(1:3, 20, replace=T) )
> x1 <- runif(20)
> y <- rnorm(20)
>
> summary( fit <- lm( y ~ -1 + x1 + firm ) )
> ...
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> x1 -0.04964 0.74861 -0.066 0.948
> firm1 0.10732 0.48269 0.222 0.827
> firm2 0.27548 0.48781 0.565 0.580
> firm3 -0.07651 0.53384 -0.143 0.888
>
> NB : The "-1" in the formula forces each firm to have its own intercept.
>
>
> Use model.matrix, you will see the dummy variables created within lm().
>
> model.matrix( fit )
> x1 firm1 firm2 firm3
> 1 0.6641647 0 1 0
> 2 0.5142712 1 0 0
> 3 0.2197956 1 0 0
> 4 0.3211675 0 1 0
> 5 0.1892449 1 0 0
> 6 0.7740754 0 0 1
> 7 0.3486932 0 1 0
> 8 0.2116816 0 0 1
> 9 0.2426825 0 1 0
> 10 0.2219768 1 0 0
> 11 0.9328514 1 0 0
> 12 0.7880405 0 0 1
> 13 0.8673492 0 1 0
> 14 0.1777998 0 1 0
> 15 0.3178498 1 0 0
> 16 0.3379726 0 0 1
> 17 0.9193359 1 0 0
> 18 0.6998152 0 1 0
> 19 0.2825702 0 0 1
> 20 0.6139586 1 0 0
>
> Regards, Adai
>
>
>
> On Mon, 2005-09-05 at 15:53 +0100, Tobias Muhlhofer wrote:
>

>>So are you guys saying to me that if I have variable firm which is the 
>>factor of all firm identifiers, I could just go
>>
>>lm(y ~ x + firm)
>>
>>and that will implicitly include a dummy for each level of factor firm, 
>>thus making this a fixed effects (aka LSDV) model?
>>
>>T
>>
>>
>>Jean Eid wrote:
>>
>>>You can turn the identity vector of the firms into a factor and do lm ....
>>>
>>>Jean
>>>
>>>On Mon, 5 Sep 2005, Tobias Muhlhofer wrote:
>>>
>>>
>>>
>>>>Hi, all!
>>>>
>>>>Anyone know an easy way to specify the following model.
>>>>
>>>>Panel dataset, with stock through time, by firm.
>>>>
>>>>I want to run a model of y on a bunch of explanatory variables, and one
>>>>dummy for each firm, which is 1 for observations that come from firm i,
>>>>and 0 everywhere else. I have over 200 firms (and a factor variable that
>>>> contains a firm identifier).
>>>>
>>>>Any easy way of going about this, without having to define all these
>>>>dummies? I checked lme() with random = ~ 1|firm, but the problem is that
>>>>these are random effects, i.e. that there are firm-by-firm disturbance
>>>>terms and overall disturbance terms, whereas I want just overall
>>>>disturbance terms. This is generally called a "fixed effects" model,
>>>>although it seems like the term "fixed effects" is being used somewhat
>>>>differently in the context of the nlme package.
>>>>
>>>>Toby
>>>>
>>>>--
>>>>**************************************************************************
>>>>When Thomas Edison invented the light bulb he tried over 2000
>>>>experiments before he got it to work. A young reporter asked
>>>>him how it felt to have failed so many times. He said
>>>>"I never failed once. I invented the light bulb.
>>>>It just happened to be a 2000-step process."
>>>>
>>>>______________________________________________
>>>>R-help@stat.math.ethz.ch mailing list
>>>>https://stat.ethz.ch/mailman/listinfo/r-help
>>>>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
>>>>
>>>
>>>
>>>

>
>
-- 
**************************************************************************
When Thomas Edison invented the light bulb he tried over 2000
experiments before he got it to work. A young reporter asked
him how it felt to have failed so many times. He said
"I never failed once. I invented the light bulb.
It just happened to be a 2000-step process."

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Received on Tue Sep 06 05:23:44 2005

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