similar to: Bug in model.matrix.default (PR#1100)

On Tue, 25 Sep 2001 12:27:27 GMT, Jorge Luis Ojeda Cabrera <jojeda@posta.unizar.es> wrote in message <20010925.12272700@macdesp8.unizar.es>: >When fitting model 'y~1', the 'lm' response is ok, but it is not possible >to get predictions using 'predict'. May be this is a bug, at least i >think it is somehow >inconsistent. It still appears in 1.3.1,

I would like you consider that the function ecdf could be extended in the following way to handle weights when computing Empirical distribution Functions. There exist particular cases that supports this kind of extension, see for example: Rao, C. R., 1997. Statistic and True. Putting chance to work. World Scientific Publishing. Cox, D. R., 1969. Some Sampling Problems in Technology. New

When fitting model 'y~1', the 'lm' response is ok, but it is not possible to get predictions using 'predict'. May be this is a bug, at least i think it is somehow inconsistent. > rm(list=ls()) > d <- data.frame(x=runif(50),y=rnorm(50)) > plot(d) > d.lm <- lm(y~1,data=d) > d.lm Call: lm(formula = y ~ 1, data = d) Coefficients: (Intercept)

I have a Debian printer server implemented with SAMBA and CUPS. The total amount of printing jobs is high (about 600 jobs per hour). The SAMBA configuration is standard. The main problem is that the smbd processes which are created for the printing requests collapse the processor, provoking a slow printing service. ?Could anybody help me about which are the reasons of this behaviour? Thanks

I am not sure if this is a bug or not but it seemed estrange to me. Following code works ok. > cat("Hello my body is: ");body(cat) Hello my body is: .Internal(cat(list(...), file, sep, fill, labels, append)) > cat("Hello my body is: ");body(cat);cat("\n") Hello my body is: .Internal(cat(list(...), file, sep, fill, labels, append)) > But when I

Dear Dimitris, I have exactly the same problem than you, Do you get some solution? Thanks, Lola Lola Gadea Profesora titular de Economía Aplicada/Lecturer in Applied Economics Universidad de Zaragoza/University of Zaragoza (Spain) lgadea@unizar.es <http://estructuraehistoria.unizar.es/personal/lgadea/index.html>http://estructuraehistoria.unizar.es/personal/lgadea/index.html Grupo de

Dear Dimitris, I have exactly the same problem than you...Do you have some any solution? Thanks, Lola Lola Gadea Profesora titular de Economía Aplicada/Lecturer in Applied Economics Universidad de Zaragoza/University of Zaragoza (Spain) lgadea@unizar.es <http://estructuraehistoria.unizar.es/personal/lgadea/index.html>http://estructuraehistoria.unizar.es/personal/lgadea/index.html Grupo

Many thanks for your very useful comments and suggestions. Renaud 2006/5/30, Prof Brian Ripley <ripley at stats.ox.ac.uk>: > On Tue, 30 May 2006, Prof Brian Ripley wrote: > > > This is not really a bug. See > > > > http://developer.r-project.org/model-fitting-functions.txt > > > > for how this is handled in other packages. All model-fitting in R used =

Congratulations for the baby !! I would like to thank those lots of people who have made such a nice work and who have been so generous with your efforts. Jorge Luis Ojeda Cabrera Dep. M?todosEstad?sticos Fac. De Ciencias, U. de Zaragoza Pedro Crbuna,12 50009 Zaragoza Espa?a -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

One of the reports recently was of predict.lm misbehaving if newdata=data.frame(x=rep(NA, 10)) was given a logical column when it had been fitted as a numeric one. The exact problem was because model.matrix was trying to handle a 0-level factor (which is what that logical column got converted to by contrasts<-). However, the problem is more general and I have added to R-devel a layer of

Dear R- Community, to learn i reanalysed some data provided and analysed by Zuur et. al. in their book "Mixed effect models and Extensions in Ecology with R". When i run the last command i get a warning message i dont understand. Loyn<- read.table(file = "loyn.txt",header = TRUE) Loyn$L.AREA<- log10(Loyn$AREA) fGRAZE <-factor(Loyn$GRAZE) M0<- lm(ABUND~ L.AREA

Technically it is used as a predictor in the model. The information is contained in terms : > terms(x ~ . - z, data = d) x ~ (y + z) - z attr(,"variables") list(x, y, z) attr(,"factors") y x 0 y 1 z 0 attr(,"term.labels") [1] "y" attr(,"order") [1] 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1

Dear R community, I have recently discovered the package oblique.tree and I must admit that it was a nice surprise for me, since I have actually made my own version of a kind of a classifier which uses the idea of oblique splits (splits by means of hyperplanes). So I am now interested in comparing these two classifiers. But what I do not seem to understand is why the function

a closer look to the help on predict.glm will reveal that the function accepts a 'type' argument. In you case 'type = response' will give you the results in probabilities (that it seems to be what you are looking for). There also is an example on use of the 'type' argument at the end of the page. Stefano -----Messaggio originale----- Da: r-help-bounces at r-project.org

Full_Name: Renaud Lancelot Version: Version 2.3.0 (2006-04-24) OS: MS Windows XP Pro SP2 Submission from: (NULL) (82.239.219.108) I think there is a bug in predict.lme, when a polynomial generated by poly() is used as an explanatory variable, and a new data.frame is used for predictions. I guess this is related to * not * using, for predictions, the coefs used in constructing the orthogonal

On Fri, Mar 18, 2011 at 4:23 PM, Nicolas Ojeda Bar <nojb at math.harvard.edu> wrote: > Hi Eli, > > I'm using 2.8. Ouch; too late to fix 2.8 :(. If you can, use trunk or the 2.9 branch instead. Otherwise, passing -mattr=-avx to llc should do the trick. -Eli > On Mar 18, 2011, at 6:50 PM, Eli Friedman wrote: > >> On Fri, Mar 18, 2011 at 2:56 PM, Nicolas Ojeda Bar

I strongly advise you not to use SjLj exception handling. Use zero-cost-exceptions via DWARF instead. If you really want to pursue sjlj anyway, look at the ARM backend. It uses them for darwin targets. -Jim On Jul 12, 2013, at 9:09 AM, Nicolas Ojeda Bar <N.Ojeda.Bar at dpmms.cam.ac.uk> wrote: > Dear list, > > I want to add SJLJ exception handling to my frontend. Unfortunately,

Dear R-help I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect. For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't

Beatriz, Prueba legend("topleft",0,substitute(chi)). En mi PC funciona! En cuanto a LATEX, tienes razón, excede el tema de este foro, con lo que me reservo (lamentablemente) mis sugerencias. Saludos, Jose -----Original Message----- From: r-help-es-bounces en r-project.org [mailto:r-help-es-bounces en r-project.org] On Behalf Of Beatriz Lacruz Sent: 20 November 2009 15:13 To: Jorge Ivan

> On Jul 20, 2018, at 3:05 PM, Lenth, Russell V <russell-lenth at uiowa.edu> wrote: > > Dear R-Devel, > > I seem to no longer be able to access the bug-reporting system, so am doing this by e-mail. > > My report concerns models where variables are explicitly referenced (or is it "dereferenced"?), such as: > > cars.lm <- lm(mtcars[[1]] ~