[R] summary many regressions

[David Winsemius]

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.

This is a plain text list.

-- 

David Winsemius
Alameda, CA, USA