similar to: LaTex error when creating DVI version when compiling package

Dear listers, Here we have a strange result we can hardly cope with. We want to compare a null mixed model with a mixed model with one independent variable. > lmmedt1<-lme(mediane~1, random=~1|site, na.action=na.omit, data=bdd2) > lmmedt9<-lme(mediane~log(0.0001+transat), random=~1|site, na.action=na.omit, data=bdd2) Using the Akaike Criterion and selMod of the package pgirmess

Full_Name: Finn Knudsen Version: 1.8.0 OS: windows 2000 Submission from: (NULL) (194.192.22.33) The problem seems to happen when running the GLM. When both multiplicative effects and an offset is present. I experienced this problem on my own dataset when using af Poisson familiy with log link function but the behaviour can be reproduced with the following code. I do not know if it is a bug, but

A querent on StackOverflow asked about the with() function http://stackoverflow.com/questions/42283479/why-when-to-use-with-function#42283479 and asked about the example in ?with library(MASS) with(anorexia, { anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian) summary(anorex.1) }) which saves little or no typing

Hello, I'm a starting user of R. I have installed R 1.9.1 and winedt 5.4 If I run the example from written with winedt. The summary command does not produce any output. It does when I repeat the command manualy in R. Can someone explain me what can be the problem? library(MASS) data(anorexia) anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family =

Dear listers, I was used to package pgirmess under Windows with everything OK, but, for the first time, I had a trial this afternoon on Ubuntu 7.10 gutsy (I have a double boot computer and work more and more under unix) and R 2.7.0. Everything went OK except this: sudo R CMD check pgirmess ..... * checking examples ... ERROR Running examples in 'pgirmess-Ex.R' failed. The error most

Dear all, I am fitting a GLM similar to library(MASS) anorex.1 <- glm(Treat~Postwt+Prewt,family = binomial, data = anorexia) I have found two ways of computing the p-value of the fitted model: pval1 <- 1-pchisq(anorex.1$deviance,anorex.1$df.residual) pval2 <- 1-pchisq(anorex.1$null.deviance - anorex.1$deviance, anorex.1$df.null - anorex.1$df.residual) pval2 is

Hello all, I am newbie to R and have not been able to find too much stuff on a version of VAR(p) I am working on. Would someone be able to tell me if there is a more elegant way of writing A function for the following? Many thanks in advance. Darius I am regressing returns of 8 asset classes on lagged values of 4 state variables and so I have 8 equations like the following: cash_lag1= dynlm

Dear all, After running a glm, I use the summary ( ) function to extract its coefficients and related statistics for further use. Unfortunately, the screen only displays a small (last) part of the results. I tried to overcome the problem by creating/saving an object "coef" for coefficients of the model and export/save it e.g. as a cvs document. While I succed with this operatiion, I do

Dear R users, I am using lmer combined with AIC model selection and averaging (in the MuMIn package) to try and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are

With new "strict" settings in R-devel, the following glm() example > data(anorexia, package = "MASS") > fm <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, data = anorexia) > methods(class = class(fm)) Warning in grep(name, row.names(info)) : argument 'pattern' has length > 1 and only the first element will be used Warning in

I think this would be a good change. I think most users use the 'methods(class = <...>)' function to answer the question, "what methods can I call on objects with these classes?", and in that context I think it would be sensible for the function to accept more than one class. Kevin On Wed, Oct 17, 2018 at 7:15 AM Martin Maechler <maechler at stat.math.ethz.ch>

Hi, I looking for a method that use F or Chisq test instead of AIC in a stepwise modelo selection. I try the grasp package using the grasp.step.anova, but It dont work. > library(grasp) Carregando pacotes exigidos: gam Carregando pacotes exigidos: splines Carregando pacotes exigidos: mda Carregando pacotes exigidos: class > data(anorexia,package="MASS") > > m1 <-

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating

i tried a method to implementa file uploader by using amethod below this is ma controlller method to upload def save_imports Contactslist.save(params[:list]) render :text => "File uploaded successfully" this is ma view file .remote - form_for(@list, :url=> save_imports_list_path(@list, :format => "js"), :html => { :multipart => true,

I tried to use calcMin with a function that uses a number of ... arguments (all args from resid on) besides the vector of parameters being fit. Same idea as optim, nlm, nlminb for which this form of ... syntax works. But with calcMin I get an error regarding unused arguments. No partial matches to previous arguments that I can see. Anybody know the reason or fix for this?

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Hello all, I come arcoss a problem that has to do with graphics display in my machine (Redhat 7.35). This is what I did. I asked R to do multiple linear regression by: amodel<-lm(dat[,4]~dat[,1]+dat[,2]+dat[,3]) plot(amodel) and I got only one figure (residuals vs. the fitted; instead of several plots), and the following message: Error in abline (h=0, lty=3,

I am fitting logistic regression models, by defining my own link function, and would like to get AICc values. Using the glm command gives a value for AIC, but I haven't been able to get R to convert that to AICc. Is there a code that has already been written for this? Right now I am just putting the AIC values into an excel spreadsheet and calculating AICc, likelihood, and AIC

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias