Hi, I'm trying to fit a fixed effect (LSDV) panelmodel with R. I have a dataset with y as dependent, x1&x2 as indeps, t as time index and i as an id-variable for each individual. There are three observations for each individual (t=1, t=2, t=3). I want to try a simple regression, but with individual intercepts: ------------------------------------------------- # reading in some data ... mydata <- read.csv(...) attach(mydata) # fit modell mymodel <- lm(y ~ -1 + factor(i) + x1 + x2) summary(mymodel) ------------------------------------------------- Works fine when the size of my dataset doesn't exceed about n=5000 observations, but I have some more. Can I do a partitioned regression with R, are there any other options already implemented in R ? Thanks, Thomas
R-newbie-question, fixed effects panel model, large number of observations
2 messages · Thomas Wilde, ronggui
This is one function I wrote. http://sociology.yculblog.com/post.794856.html 2006/2/12, Thomas Wilde <thomas.wilde at gmx.de>:
Hi, I'm trying to fit a fixed effect (LSDV) panelmodel with R. I have a dataset with y as dependent, x1&x2 as indeps, t as time index and i as an id-variable for each individual. There are three observations for each individual (t=1, t=2, t=3). I want to try a simple regression, but with individual intercepts: ------------------------------------------------- # reading in some data ... mydata <- read.csv(...) attach(mydata) # fit modell mymodel <- lm(y ~ -1 + factor(i) + x1 + x2) summary(mymodel) ------------------------------------------------- Works fine when the size of my dataset doesn't exceed about n=5000 observations, but I have some more. Can I do a partitioned regression with R, are there any other options already implemented in R ? Thanks, Thomas
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