similar to: Read dataset in R language

Dear Helen, bootstrapped standard errors are currently not supported in 'plm'. Cheers, Giovanni ------------------------------ Original Message: Date: Wed, 22 Apr 2009 23:23:26 -0700 (PDT) From: Helen Chen <96258011 at nccu.edu.tw> Subject: [R] question of plm package To: r-help at r-project.org Message-ID: <23190943.post at talk.nabble.com> Content-Type: text/plain;

Hello Cecilia, nice hearing from you again. I must restate a couple of my old hints, though ;^) 1) please always put the authors c/c, as we are not guaranteed to browse through the r-help every day 2) please provide reproducible examples. As example(pooltest) keeps working fine, as do some other cases I tried (Grunfeld data etc.), I don't know what the problem is but evidently your data are

Dear Limin, might be just about anything. Could you please provide a reproducible example? Best, Giovanni ----------------- Original message ---------------------- Message: 51 Date: Mon, 17 May 2010 10:36:03 +0800 (CST) From: ??? <dlmsos at 163.com> To: r-help at r-project.org Subject: [R] pgmm function Message-ID: <b2cba0.35fc.128a41e3684.Coremail.dlmsos at 163.com> Content-Type:

Hello. Not an easy question at all, and it has little to do with software, alas! Veeeeeery loosely speaking: if the homogeneity hypothesis is rejected, then, depending on data availability, you may still be able to treat the data like a panel by: a) ignoring the results of the poolability test b) allowing the coefficients to vary. Of course, a) requires some courage while b) requires more

Dear list, dear Cecilia and Daniel, sorry for coming in ten days late, I've been very busy lately so I came across this email only today. This is just to make some points clearer re: fixed effects and r2 in package 'plm', to both you and the list. In particular, to make you aware of some additional features. Please see my comments below, with '##'. Best, Giovanni

Dear Carlos, please refrain from posting the same question umpteen times. Please consider that code is hard to read and people might not have the time to run your simulation etc. etc.. As I told you privately in response to your message on 18/1, > Re: generating correlated effects, I tried this only once, but I > didn't get it right. Simulations using this are, e.g., Hansen (2007)

Hello. Just a quick follow-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,

Dear Philipp, this is just a tentative answer because debugging is really not possible without a reproducible example (or, at a very bare minimum, the output from traceback()). Anyway, thank you for reporting this interesting numerical issue; I'll try to replicate some similar behaviour on a similarly dimensioned artificial dataset when I have some time (which might not be soon). As for now,

Thomas, may I also suggest, from the Documentation>Contributed section of CRAN, "Econometrics in R" by Grant Farnsworth http://cran.at.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf (see the chapter on Time series) and, in case you can read Italian, "Analisi delle serie storiche con R" by Vito Ricci http://cran.at.r-project.org/doc/contrib/Ricci-ts-italian.pdf

Matteo, I fully agree with David: please read the posting guide. Anyway, the error message says it all: "the estimated variance of the individual effect is negative". See e.g. the "basic panel" chapter (10 or 11) in Wooldridge's "Econometric Analysis of XS and Panel Data" to understand why this may happen. Stata's behaviour is (as far as I remember) to

Dear Ivo, dear list, (see: Message: 70 Date: Thu, 26 Mar 2009 21:39:19 +0000 From: ivowel at gmail.com Subject: [R] pgmm (Blundell-Bond) sample needed) I think I finally figured out how to replicate your supersimple GMM example with pgmm() so as to get the very same results as Stata. Having no other regressors in the formula initially drove me crazy. This was a case where simpler models are

Dear Hao-pang, it is impossible to really tell the problem without a reproducible example. Just guessing: this looks like you have too many regressors. In GMM, lags of variables are used as instruments, so you might have more regressors than observations. Try reducing the 'lag' argument (which, by default, uses all lags available). Of course, the first observation to make would be that

Dear List, I am using the output of a ML estimation on a random effects model with first-order autocorrelation to make a further conditional test. My model is much like this (which reproduces the method on the famous Grunfeld data, for the econometricians out there it is Table 5.2 in Baltagi): library(Ecdat) library(nlme) data(Grunfeld)

Dear all, I just went through the process of installing R on an eeePC 900 running Linux. As a Windows useR utterly ignorant about Linux, I'd never have done it without reading your posts and the R Wiki, so first of all: thank you! Next, taking up your thread from some weeks ago, I thought this could be useful for somebody else too, so here's what I did: 1) I followed wolfgang's

Dear List, I just installed R 2.6.1 (on Win2K) and I get a strange error in functions min() and max(): > min(1:3) Errore in .Internal(min(..., na.rm = na.rm)) : nessuna funzione interna "min" which, as you may have guessed, means 'no internal function "min" '. The same happens for max(). Maybe this is a bug in the new release, or maybe I'm missing

Hi everyone, Can anyone please tell whether there is a difference between the code for using svm in regression and code for using svm in classification? This is my code for regression, should I change it to do classification?: train <- read.table("trainingset.txt",sep=";") test <- read.table("testset.txt",sep=";") svmmodelfitness <-

I am looking for ways to add points to three different plots in parallel. I generate three scatter plots and name them as s3d1, s3d2 and s3d3 s3d1<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20) s3d2<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20)

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest

I am trying to plot a graph but the points on the graph should be different symbols and colors. It should represent what is in the legend. I tried using the points command but this does not work. Is there another command in R that would allow me to use different symbols and colors for the points? Thank you kindly. data(mtcars) plot(mtcars$wt,mtcars$mpg,xlab= "Weight(lbs/1000)",

This syntax is already implemented in the {purrr} package, more or less -- you need to add a tilde before your function call for it to work exactly as written: purrr::map_dbl(split(mtcars, mtcars$cyl), ~ summary(lm(wt ~ mpg, .))$r.squared) is equivalent to sapply(split(mtcars, mtcars$cyl), function(d) summary(lm(mpg ~ wt, d))$r.squared) Seems like using this package is probably an easier