Dear Andrew, Thanks for your suggestion. I will indeed have a look at Allison's booklet...
Best, On 7 February 2012 23:39, Andrew Miles <rstuff.mi...@gmail.com> wrote: > Based on Paul Allison's booklet "Fixed Effect Regression Models" (2009), the > FE model can be estimated by person-mean centering all of your variables > (but not the outcome), and then including a random intercept for each > person. The centering gives you the FE model estimates, and the random > intercept adjusts the standard errors for clustering by individuals. Note > that your data must be in person-period (or long) format to do this. > > In case you are unfamiliar with person-mean centering, that simply means > taking the mean of each person's values for a given variable for all of the > periods in your data, and then calculating a deviation from that mean at > each time period. For example, a person's average income over four years > might be $50,000, but in each year their actual income would be slightly > higher or lower than this (these would be the person-mean deviations). In > symbolic form, your code might look something like this: > > library(lme4) > variable_pmcentered = variable - person_mean > mod = lmer(outcome ~ variable_pmcentered + person_mean + other predictors + > (1|personID)) > > The advantage of this method (which Allison calls a "hybrid" method) over > traditional FE models is that you get the benefits of a FE model > (subtracting out time-invariant omitted variables) along with the benefits > of random effect models (e.g., estimating coefficients for time-invariant > variables, estimating interactions with time, letting intercepts and slopes > varying randomly, etc.) See Allison's booklet for more details on this > method. > > Allison, Paul D. 2009. Fixed Effects Regression Models. Los Angeles, C.A.: > Sage. > > > Andrew Miles > > > On Feb 7, 2012, at 5:00 PM, caribou...@gmx.fr wrote: > > Dear R-helpers, > > First of all, sorry for those who have (eventually) already received that > request. > The mail has been bumped several times, so I am not sure the list has > received it... and I need help (if you have time)! ;-) > > I have a very simple question and I really hope that someone could help me > > I would like to estimate a simple fixed effect regression model with > clustered standard errors by individuals. > For those using Stata, the counterpart would be xtreg with the "fe" option, > or areg with the "absorb" option and in both case the clustering is achieved > with "vce(cluster id)" > > My question is : how could I do that with R ? > An important point is that I have too many individuals, therefore I cannot > include dummies and should use the demeaning "usual" procedure. > I tried with the plm package with the "within" option, but R quikcly tells > me that the memory limits are attained (I have over 10go ram!) while the > dataset is only 700mo (about 50 000 individuals, highly unbalanced) > I dont understand... plm do indeed demean the data so the computation should > be fast and light enough... and instead it saturates my memory and do not > converge... > > Do you have an idea ? > Moreover, it is possible to obtain cluster robust standard errors with plm ? > > Are there any other solutions for fixed effects linear models (with the > demean trick in order not to create as many dummies as individuals) ? > Many thanks in advance ! ;) > John > > > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.