similar to: R crashes on quantreg

Dear User, I got the following error running a regression quantile: > rq1<-rq(dep ~ ., model=TRUE, data=exo, tau=0.5 ); > summary(rq1) Erro em rq.fit.fnb(x, y, tau = tau + h) : Error info = 75 in stepy: singular design Any hint about the problem? Thanks a lot, ________________________________________ Ricardo Gon?alves Silva, M. Sc. Apoio aos Processos de Modelagem Matem?tica

Does any have installation instructions for this? When I run install.packages('quantreg') I get: gcc -std=gnu99 -shared -o quantreg.so akj.o boot.o brute.o chlfct.o cholesky.o combos.o crq.o crqfnb.o dsel05.o etime.o extract.o idmin.o iswap.o kuantile.o mcmb.o penalty.o powell.o rls.o rq0.o rq1.o rqbr.o rqfn.o rqfnb.o rqfnc.o sparskit2.o srqfn.o srqfnc.o srtpai.o -llapack -lblas

Hi, I'm wondering if it is possible to install a package for R 2.0 on R 1.9.1 on Mac OS X? I'm getting this error which seems to be known issue: library("quantreg") Error in firstlib(which.lib.loc, package) : couldn't find function "lazyLoad" In addition: Warning message: package quantreg was built under R version 2.0.1 Error in

All: How do I obtain one step ahead out-of-sample forecasts from a model using "dyn" or "dynlm" ? Thanks! Best, John [[alternative HTML version deleted]]

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),

Dear R-devel: The Cluster administrators at KU got enthusiastic about testing R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below I forward a performance report, which is encouraging, and thought you would like to know the numbers. Appears to my untrained eye there are some extraordinary speedups on Cholesky decomposition, determinants, and matrix inversion. They had

Hi All, I'm still having significantly difficulty getting the quantreg library running in R. I'm running R on MEPIS using the debs created by Dirk Eddelbuettel and placed in apt testing. When I try to install quantreg using the install.packages() function it fails with: /usr/bin/ld: cannot find -lblas-3 Dirk was nice enough to send me a .deb for quantreg which installs without

Hi all, I'm having some troubles with the Quantreg package. I am using R version 2.10.0, and have downloaded the most recent version of Quantreg (4.44) and SparseM (0.83 - required package). However, when I try to run an analysis (e.g. fit1<-rq(y~x, tau=0.5)) I get an error message saying that the function "rq" could not be found. I get the same message when I try to search

Hi I am using the quantreg package for median regression for a large series of subsets of data. It works fabulously for all but one subset. When it reaches this subset, R takes the command and never responds. I end up having to kill R and restart it. It appears to be something with the particular data subset, but I can't pinpoint the problem. Here are some details Operating system:

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

Hi All, I'm trying to install the quantreg library on my MEPIS box (debian-based linux). When I try >install.packages("quantreg") it fails to loads and errors with: /usr/bin/ld: cannot find -lblas-3. I tried installing a ton of packages to fix the missing part but the only thing I manage to do it cause problems with another shared library and had to re-install a couple of

Hi All, I'm trying to install the quantreg library on my MEPIS box (debian-based linux). When I try >install.packages("quantreg") it fails to loads and errors with: /usr/bin/ld: cannot find -lblas-3. I tried installing a ton of packages to fix the missing part but the only thing I manage to do it cause problems with another shared library and had to re-install a couple of

He folks=) I want to check whether a coefficient has an impact on a quantile regression (by applying the sup-wald test for a given quantile range [0.05,0.95]. Therefore I am doing the following calculations: a=0; for (i in 5:95/100){ fitrestricted=rq(Y~X1+X2,tau=i) tifunrestrited=rq(Y~X1+X2+X3,tau=i) a[i]=anova(fitrestricted,fitunrestricted)$table$Tn) #gives the Test-Value } supW=max(a) As anova

Dear R-mailing list, I would like to overlay to two quantreg coefficients plots. I have plot(summary(rq(ff~tipo,tau = 1:49/50,data=Spilldata))) plot(summary(rq(ff~tipo,tau = 1:49/50,data=Spilldata1))) Is there a possibility to display the two in the same graph? Thank you so much!!! Christian [[alternative HTML version deleted]]

Hello all, I would like to compute a quantile regression using rq (from the quantreg package), while keeping one of the coefficients fixed. Is it possible to set an offset for rq in quantreg? (I wasn't able to make it to work) Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me: Tal.Galili at gmail.com |? 972-52-7275845 Read me:

I am trying to implement an ANOVA on a pair of quantile regression models in R. The anova.rq() function performs a basic check to see whether the models are nested, but I think this check is failing in my case. I think my models are nested despite the anova.rqlist() function saying otherwise. Here is an example where the GLM ANOVA regards the models as nested, but the quantile regression ANOVA

Hi, I am trying to use quantile regression to perform weighted-comparisons of the median across groups. This works most of the time, however I am seeing some odd output in summary(rq()): Call: rq(formula = sand ~ method, tau = 0.5, data = x, weights = area_fraction) Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 45.44262 3.64706 12.46007

Quantreg package allows to plot the summary of models derived by quantile regression at different taus. The plot shows the parameters variation by varying taus: intercept and slope (for a linear model). Together with these values even confidence intervals may be plotted, based on the threshold given within the summary (e.g. alpha=0.01 equals 99% CI). However the graphic even plots the mean of

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program