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...ow-up, for other 'plm' users on the list: - the problem turned out to be logs of zero values hidden in the big dataset - trying log(xx) would not reveal the problem, because log(0)=-Inf is a valid result in log() while it is an invalid input to plm() --> it is always advisable to try lm(yourformula, yourdata) as a first diagnostic check when plm(yourformula, yourdata, ...) fails: the result should be the same as plm(yourformula, yourdata, model="pooling"); or, for more complicated instances, a more specific "computationally robust" counterpart is lme() from 'nlme'....

Hi everyone, Is there a way to take an lm() model and strip it to a minimal form (or convert it to another type of object) that can still used to predict the dependent variable? Background: I have a series of 6 lm() models, each of which are being run on the same data frame of approximately 500,000 rows. I eventually want to predict all 6 of the dependent variables on a new data frame,

When using the PLM package (version 1.2-8), I encounter the probem that calling the FGLS estimator evokes strange behavior, when choosing the "random" effects model. After calling the PGGLS function to estimate FGLS, PLM gives me a warning, stating that the "random" model has been replaced with the "pooling" model. I would, however, really like to estimate the random

Hello, I am trying to run a fixed effects panel regression on data containing 5 columns and 1,494 rows. I read the data in as follows: >drugsXX<-read.csv(file="C:\\Folder\\vX.X\\Drugs\\drugsXX_panel.csv", head=TRUE, sep=",") Verified it read in correctly and had a good data.frame: >dim(drugsXX) [1] 1494 5 >drugs XX produce expected data with correct column