similar to: gamlss and glm binomial family

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 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

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

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

Dear Nik Try the following code loo_predict.mu <- function(model.obj, input.data) { yhat <- foreach(i = 1 : nrow(input.data), .packages="gamlss", .combine = rbind) %dopar% { updated.model.obj <- update(model.obj, data = input.data[-i, ]) predict(updated.model.obj, what = "mu", data = input.data[-i, ], newdata = input.data[i,], type =

Dear all, I am using gamlss (Package gamlss version 4.0-0, R version 2.10.1, Windows XP Service Pack 3 on a HP EliteBook) to relate bird counts to habit variables. However, most models fail because “the global deviance is increasing” and I am not sure what causes this behaviour. The dataset consists of counts of birds (duck) and 5 habit variables measured in the field (n= 182). The dependent

Hola a tod en s, tengo una duda que la comunidad R me puede ayudar. Estoy trabajando con gamlss, porque tengo una variable respuesta con valores entre 0 y 1 e incluidos estos. La distribución que utilizo com gamlss para este caso es "beta inflated" (Stasinopulos and Rigby 2007. Journal of Statistical Software 23(7)). El modelo que intento correr es: m1<-gamlss(Teleosteos ~

Dear R helpers, I have following loss data and I need to fit LEFT truncated Log Normal distribution to this data which is Truncated at 1000000. dat = c(1333834,5710254,9987567,7809469,6940935,3473671,1270209,1102523,1124002, 5830159,4302300,3925242,2638409,2324421,7238436,9088709,7439250,4976551,4864319, 8741334,1863770,7098310,4942288,4971829,4986372) library(gamlss.tr) gen.trun(5, LOGNO)

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 #

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: 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

Dear All: I have succeeded in fitting a GAMLSS.dist model to growth data I am working with it. My aim is to create a matrix of predicted percentiles and the corresponding the fitted model's sigma mu nu by agebins. Q: How do it generate these parameters as in L M S per Cole and Green 1992? Here are my working codes. Name of fitted model is gamlssfit > Agebin<-seq(6,36,6)

Hi, I'm playing around with gamlss and don't entirely understand the sigma result from an attempted lognormal fit. In the example below, I've created lognormal data with mu=10 and sigma=2. When I try a gamlss fit, I get an estimated mu=9.947 and sigma=0.69 The mu estimate seems in the ballpark, but sigma is very low. I get similar results on repeated trials and with Normal and

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =

Dear Lister, I am facing a strange problem fitting a GLM of the negative binomial family. Actually, I tried to estimate theta (the scale parameter) through glm.nb from MASS and could get convergence only relaxing the convergence tolerance to 1e-3. With warning messages: glm1<-glm.nb(nbcas~.,data=zonesdb4,control=glm.control(epsilon = 1e-3)) There were 25 warnings (use warnings() to see

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

Hi everyone, I''m trying to use function vcov to extract the covariance matrix from a GAMLSS object. But I''m getting some strange errors and I was hoping someone could help me out? Vcov works with the same model for lm and glm objects, but not gamlss objects. I''ve searched various help sites to no avail. Its very possible the reason is that vcov failed though,

Hi, I am running a negative binomial model using Gamlss and when I try to include random effect, I get the following message: Warning messages: 1: In vcov.gamlss(object, "all") : addive terms exists in the mu formula standard errors for the linear terms maybe are not appropriate 2: In vcov.gamlss(object, "all") : addive terms exists in the sigma formula standard

Dear Listers, I got this message when compiling a package: * creating pgirmess-manual.tex ... OK * checking pgirmess-manual.text ... ERROR LaTex errors when creating DVI version. This typically indicates Rd problems. The message is quite explicit but I struggled a lot before understanding that the trouble comes from a single file "selMod.rd" among 44 topics. Even though I have