[R] linear model coefficients by year and industry, fitted values, residuals, panel data
cecilia.carmo at ua.pt
Thu Apr 4 11:11:49 CEST 2013
Thank you all. I'm very happy with this solution. Just two questions:
I use mutate() with package plyr and it gaves me a error message, is it a new function and my package may be old?
Is there any extractor for the R-squared?
De: Peter Ehlers [ehlers at ucalgary.ca]
Enviado: quarta-feira, 3 de Abril de 2013 19:01
Para: Adams, Jean
Cc: Cecilia Carmo; r-help at r-project.org
Assunto: Re: [R] linear model coefficients by year and industry, fitted values, residuals, panel data
A few minor improvements to Jean's post suggested inline below.
On 2013-04-03 05:41, Adams, Jean wrote:
> Thanks for providing a reproducible example. Excellent.
> You could use the ddply() function in the plyr package to fit the model for
> each industry and year, keep the coefficients, and then estimate the fitted
> and residual values.
> coef <- ddply(final3, .(industry, year), function(dat) lm(Y ~ X + Z,
> names(coef) <- c("industry", "year", "b0", "b1", "b2")
> final4 <- merge(final3, coef)
> newdata1 <- transform(final4, Yhat = b0 + b1*X + b2*Z)
> newdata2 <- transform(newdata1, residual = Y-Yhat)
> plot(as.factor(newdata2$firm), newdata2$residual)
Use the extractor function coef() and also avoid using the name
of an R function as a variable name:
Coef <- ddply(...., function(dat) coef(lm(....)))
Use plyr's mutate() to do both transforms at once:
newdata <- mutate(final4,
Yhat = b0 + b1*X + b2*Z,
residual = Y-Yhat)
[Or you could use within(), but I now find mutate handier, mainly
because it doesn't 'reverse' the order of the new variables.]
Use the 'data=' argument in the plot:
boxplot(residual ~ firm, data = newdata)
> On Wed, Apr 3, 2013 at 3:38 AM, Cecilia Carmo <cecilia.carmo at ua.pt> wrote:
>> Hi R-helpers,
>> My real data is a panel (unbalanced and with gaps in years) of thousands
>> of firms, by year and industry, and with financial information (variables
>> X, Y, Z, for example), the number of firms by year and industry is not
>> always equal, the number of years by industry is not always equal.
>> #reproducible example
>> I need to estimate a linear model Y = b0 + b1X + b2Z by industry and year,
>> to obtain the estimates of b0, b1 and b2 by industry and year (for example
>> I need to have de b0 for industry 20 and year 2000, for industry 20 and
>> year 2001...). Then I need to calculate the fitted values and the residuals
>> by firm so I need to keep b0, b1 and b2 in a way that I could do something
>> or another way to keep Y' and the residuals in a dataframe with the
>> columns firm and year.
>> Until now I have been doing this in very hard way and because I need to do
>> it several times, I need your help to get an easier way.
>> Thank you,
>> Cecília Carmo
>> Universidade de Aveiro
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