[R] Odp: regression with ordered arguments

[Petr PIKAL]

Hi

> Dear R listers,
> 
> I am trying to be a new R user, but life is not that easy.
> My problem is the following one: let's assume to have 3 outcome 
variables
> (y1, y2, y3) and 3 explanatory ones (x1, x2, x3).
> How can I run the following three separate regressions without having to
> repeat the lm command three times?
> 
> fit.1 <- lm(y1 ~ x1)
> fit.2 <- lm(y2 ~ x2)
> fit.3 <- lm(y3 ~ x3)
> 
> 
> Both the y and x variables have been generated extracting random numbers
> from uniform distributions using a command such as:
> 
> y1 <- runif(100, min = 0, max = 1)
> 
> I went to several introductory manuals, the manual R for stata users,
> econometrics in R, Introductory statistics with R and several blogs and 
help
> files, but I didn't find an answer to my question.
> can you please help me? In Stata I wouldn't have any problem  in running
> this as a loop, but I really can't figure out how to do that with R.

You can construct loop with naming through paste, numbers and get in R too 
but you will find your life much easier to use R powerfull list 
operations.

Insted of

y1 <- runif(100, min = 0, max = 1)
...

lll <- vector(mode="list", length=3)
lll <- lapply(1, function(x) runif(100, min = 0, max = 1))

you can use probably mapply for doing your regression.
Or you can easily access part of the list by loop

for (i in 1:3) lm(lll[[i]]~xx[[i]])

(if you have your x's in list xx)

Regards
Petr

> Thanks in advance for all your help.
> Best,
> f.
> 
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> 
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