similar to: . Package gamlss used inside foreach() and %dopar% fails to find an object (Nik Tuzov)

Hello Mikis: Thanks a lot, it worked. Could you tell me what the problem was? Regards, Nik ----- Original Message ----- From: r-help-request at r-project.org To: "r-help" Sent: Sunday, March 11, 2018 6:00:02 AM Subject: R-help Digest, Vol 181, Issue 11 Send R-help mailing list submissions to r-help at r-project.org To subscribe or unsubscribe via the World Wide Web, visit

If the code you are running in parallel is complicated, maybe foreach is not sophisticated enough to find all the variables you refer to. Maybe use parallel::clusterExport yourself? But be a aware that passing parameters is much safer than directly accessing globals in parallel processing, so this might just be your warning to not do that anyway. -- Sent from my phone. Please excuse my brevity.

Hello all: Please help me with this "can't find object" issue. I'm trying to get leave-one-out predicted values for Beta-binomial regression. It may be the gamlss issue because the code seems to work when %do% is used. I have searched for similar issues, but haven't managed to figure it out. This is on Windows 10 platform. Thanks in advance, Nik #

Dear all I am using NAMESPACE in my package but I would like the user to be able to overwrite four functions: own.linkfun, own.linkinv, own.mu.eta and own.valideta. These are used to defined "own" link functions. Is there any way of doing that without getting the when I am checking the package? This is what I am getting: make.link.gamlss : linkfun: no visible binding for global

Hi, I'm a bit puzzled by the gamlss fitting of exponential and lognormal data. Gamlss seems to think that exponentially distributed data fits better with a lognormal distribution, and vice versa. For example, X <- rexp(1000) Gexp <- gamlss(X~1,family=EXP) # X~1 is X tilde 1 GAMLSS-RS iteration 1: Global Deviance = 2037.825 GAMLSS-RS iteration 2: Global Deviance = 2037.825 Glno

Dear all and Mikis I have the opportunity to compare fits with the 'classical' glm and gamlss and no smoother of any kind just the same model formula (both with the binomial family). I get exactly the same coefficients but very different residuals, gamlss giving residuals which are extremely close to 'normal' and glm very far... How can this be ? Thanks in advance for

Dear all I am trying to create a package in R 1.9.0 and I a getting an error message which I do not understand. (I am using R in Windows XP and 2000) For example the following works well in 1.8.1 C:\Program Files\R\rw1081\src\gnuwin32>make pkg-gamlss ---------- Making package gamlss ------------ adding build stamp to DESCRIPTION installing inst files installing indices not zipping

Dear all I am trying to build a package in Windows. I use the following command (which it used to work with previous versions ) and I am getting the following error #-------------------------------------------------------------------------------------------------------------- C:\PROGRA~1\R\rw2000\bin>Rcmd build --binary --use-zip C:\PROGRA~1\R\rw2000\src\library\gamlss * checking for

Dear R-List member, tried implement the foreach loop. It works fine, when I'm using %do%, but not when I'm using %dopar%. I always receive one of the following error messages: error in { : task 1 failed - "Objekt 'S3' not found" - could the .export be an solution for this?? Any help is much appreciated! The Code hast the following structure: ########## dft3 =

Dear R-SIG-Debian, I am using R version 3.1.2 with rstudio-server 0.98.113 on debian build 3.2.0-4-amd64 #1 SMP Debian 3.2.68-1+deb7u1 x86_64 GNU/Linux. I often use the %dopar% operator in from the foreach package to run code in parallel. However, the only other use on the box seemingly installed a few items and suddenly %dopar% will use far more than the number of cores I am specifying and

Hello: I'd like to know how and if the GLM convergence problems are addressed in gamlss. For simplicity, let's focus on Normal and Negative Binomial with log link. The convergence issues of the glm() function were alleviated in 2011 when glm2 package was released. Package gamlss was released in 2012, so it might still use the glm-like solution or call glm() directly. Is that the case or

Hi everyone, I'm working on a script trying to use foreach %dopar% but without success, so I manage to run the code with foreach %do% and looks like this: The code is part of a MCMC model for projects valuation, returning the most important results (VPN, TIR, EVA, etc.) of the simulation. foreach (simx = NsimT, .combine=cbind, .inorder=FALSE, .verbose=TRUE) %do% { MCPVMPA = MCVAMPA[simx]

I have a little problem with cs2 and filters from nik software(colour efex and silver efex) i linux mint 10 . when I installed the filters, who by the way works fine, cs2 started to hang when using any key like alt(I cant use the clone tool) or backslash. A message apear that says dll wrong format. after that another that says that an aplication library failed to load and that the product cannot

Re: Heteroscedasticity and mgcv. (Collin Lynch) The GAMLSS package can model heterogeneity in the scale parameter (e.g. standard deviastion) [and also heterogeity in skewness and kurtosis parameters].of the response variable distribution. For parametric models a generalized likelihood ratio test can be used to test whether the heterogeity is needed. Alternatively a generalized Akaike

Hi guys, I was wondering why this piece of code doesn't work: foreach (i = c(1.25,1.50)) %dopar% { assign(paste("test_",i,sep=""),i) } but, this does: foreach (i = c(1.25,1.50)) %do% { assign(paste("test_",i,sep=""),i) } Obviously, the difference is %dopar% vs. %do%. If I use %do%, I get objects test_1.25 and test_1.50, but I don't get these

Hello! I've just build one-inflated beta regression model using package GAMLSS. It all worked very nicely but now I want to make prediction using it. I use typical function predict() give all necessary arguments (my new data is in data frame and all relevant columns have same names as before). Unfortunately it ends with following error: Error in nrow(x) : (subscript) logical subscript too

Dear R Help, I'd like to be able to use gamlss to generate distributions that are both truncated and have censoring. It doesn't look as though it is possible to do this at the moment: > gen.trun(par=c(0),family="NO",name="tr",type="left") A truncated family of distributions from NO has been generated and saved under the names: dNOtr pNOtr qNOtr rNOtr

Hi, I'm not sure how use curve(dexGAUS(… None of the following four works: rt<- rexGAUS(100, mu=300, nu=100, sigma=35) m1<-gamlss(rt~1, family=exGAUS) curve(dexGAUS(rt=x, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300 ,sigma=35,nu=100") curve(dexGAUS(x=rt, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300

Dear All, I'd like to set up a loop whereby successive parameter values are used as start values in gamlss (yes I know this isn't usually necessary ! - unfortunately for my truncated data it is), to return the estimated parameters etc. giving the lowest AIC value. I notice that mu.start can take a vector of values - but I can't find any information as to what the function actually

Hi all. In a GLM in which g(mu) = b0 + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 + b7X7, if I want to test if b1 + b5 = b2 + b6, I can use the contrast package or multicomp package. How can I do a similar test if I am fitting a GAMLSS using the gamlss package? Thank you for your help. Gustavo