[R] summary many regressions
David Winsemius
dwinsemius at comcast.net
Tue Nov 26 02:14:34 CET 2013
On Nov 25, 2013, at 3:35 PM, Gary Dong wrote:
> Dear R users,
>
> I have a large data set which includes data from 300 cities. I want to run
> a biviriate regression for each city and record the coefficient and the
> adjusted R square.
>
> For example, in the following, I have 10 cities represented by numbers from
> 1 to 10:
>
> x = cumsum(c(0, runif(999, -1, +1)))
> y = cumsum(c(0, runif(999, -1, +1)))
> city = rep(1:10,each=100)
> data<-data.frame(cbind(x,y,city))
>
> I can manually run regressions for each city:
> fit_city1 <- lm(y ~ x,data=subset(data,data$city==1))
> summary(fit_city1)
>
> Obvious, it is very tedious to run 300 regressions. I wonder if there is a
> quicker way to do this. Use for loop? what I want to see is something like
> this:
>
> City Coefficient Adjusted R square
> 1 -0.05 0.36
> 2 -0.12 0.20
> 3 -0.05 0.32
> .....
>
The way to get the most rapid response from this list is to post a dataset that represents the complexity of the problem. Presumably this large dataset is either a dataframe with a column of city entries or a list of dataframes. Why not post dput() applied to an extract of three of the cities and include sufficient rows to allow a regression?
>
> [[alternative HTML version deleted]]
>
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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--
David Winsemius
Alameda, CA, USA
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