Hi Arne, thanks for the improvements in the package. I'm using it right now and it's working very well.
Best, *Felipe Nunes* CAPES/Fulbright Fellow PhD Student Political Science - UCLA Web: felipenunes.bol.ucla.edu On Fri, Mar 2, 2012 at 2:13 AM, Arne Henningsen < arne.henning...@googlemail.com> wrote: > Dear Felipe > > On 29 September 2011 14:10, Arne Henningsen > <arne.henning...@googlemail.com> wrote: > > Hi Felipe > > > > On 25 September 2011 00:16, Felipe Nunes <felipnu...@gmail.com> wrote: > >> Hi Arne, > >> my problem persists. I am still using censReg [version - 0.5-7] to run a > >> random effects model in my data (>50,000 cases), but I always get the > >> message. > >> tob7 <- censReg(transfers.cap ~ pt.pt + psdb.pt + pt.opp + pt.coa + > psdb.coa > >> + pib.cap + transfers.cap.lag + pib.cap + ifdm + log(populat) + > >> mayor.vot.per + log(bol.fam.per+0.01) + factor(uf.name) + factor(year) > - 1, > >> left=0, right=Inf, method="BHHH", nGHQ=8, iterlim=10000, data = pdata2) > >> Error in maxNRCompute(fn = logLikAttr, fnOrig = fn, gradOrig = grad, > >> hessOrig = hess, : > >> NA in the initial gradient > >> If I sent you my data set, could you try to help me? I have been > struggling > >> with that for two months now. > > > > Thanks for sending me your data set. With it, I was able to figure > > out, where the NAs in the (initial) gradients come from: when > > calculating the derivatives of the standard normal density function [d > > dnorm(x) / d x = - dnorm(x) * x], values for x that are larger than > > approximately 40 (in absolute terms) result in so small values (in > > absolute terms) that R rounds them to zero. Later, these derivatives > > are multiplied by some other values and then the logarithm is taken > > ... and multiplying any number by zero and taking the logarithms gives > > not a finite number :-( > > > > When *densities* of the standard normal distribution become too small, > > one can use dnorm(x,log=TRUE) and store the logarithm of the small > > number, which is much larger (in absolute terms) than the density and > > hence, is not rounded to zero. However, in the case of the > > *derivative* of the standard normal density function, this is more > > complicated as log( d dnorm(x) / d x ) = log( dnorm(x) ) + log( - x ) > > is not defined if x is positive. I will try to solve this problem by > > case distinction (x>0 vs. x<0). Or does anybody know a better > > solution? > > Finally(!), I have implemented this solution in the censReg() package. > Some initial tests (including your model and data) show that the > revised calculation of the gradient of the random effects panel data > model for censored dependent variables is much more robust to rounding > errors. The improved version of the censReg package is not yet via > CRAN, but it is available at R-Forge: > > https://r-forge.r-project.org/R/?group_id=256 > > If you have further questions or feedback regarding the censReg > package, please use a forum or "tracker" on the R-Forge site of the > sampleSelection project: > > https://r-forge.r-project.org/projects/sampleselection/ > > Best wishes from Copenhagen, > Arne > > -- > Arne Henningsen > http://www.arne-henningsen.name > [[alternative HTML version deleted]] ______________________________________________ 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.
