similar to: Bug in model.matrix.default (was: [R] predict incosistency ?) (PR#1100)

There's more to it than that (which was already fixed in R-devel). The internal code expects there to be columns in the model frame. I have already fixed this in R-devel (by faking an unused column). On Tue, 25 Sep 2001 murdoch@stats.uwo.ca wrote: > On Tue, 25 Sep 2001 12:27:27 GMT, Jorge Luis Ojeda Cabrera > <jojeda@posta.unizar.es> wrote in message >

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 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

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

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

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

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

Dear All, I am attempting to make predictions based on a survreg() model with some censoring and a frailty term, as below: predict works fine on the original data, but not if I specify newdata. # a model with groups as fixed effect model1 <- survreg(Surv(y,cens)~ x1 + x2 + groups, dist = "gaussian") # and with groups as a random effect fr <- frailty(groups,

I am struggling to get GAM model predictions from the top models calculated using model.avg in the package "MuMIn". My model looks something like the following: gamp <- gam(log10(y)~s(x1,bs="tp",k=3)+s(x2,bs="tp",k=3)+ s(x3,bs="tp",k=3)+s(x4,bs="tp",k=3)+s(x5,bs="tp",k=3)+ s(x6,bs="tp",k=3)+x7,data=dat,

Hello, I am using R to train a logistic regression model and save the resulting model to disk. I am then subsequently reloading these saved objects, and using predict.glm on them in order to make predictions about single-row data frames that are generated in real-time from requests arriving at an HTTP server. The following code demonstrates the sort of R calls that I have in mind: > cases

Hi Everyone, I often use the 'safe prediction' feature available through glm(). Now, I'm at a situation where I must use biglm:::bigglm. ## begin example library(splines) library(biglm) ff <- log(Volume)~ns(log(Girth), df=5) fit.glm <- glm(ff, data=trees) fit.biglm <- bigglm(ff, data=trees) predict(fit.glm, newdata=data.frame(Girth=2:5)) ## -1.3161465 -0.2975659

predict.warn() -- a function to display factor levels in new data for linear model prediction that do not exist in the estimating data. Date: 2005-8-24 From: John C. Nash (with thanks to Uwe Ligges for suggestions) nashjc at uottawa.ca Motivation: In computing predictions from a linear model using factors, it is possible to introduce new factor levels. This was encountered on a practical

Dear all, the help file for predict.gbm states that "The predictions from gbm do not include the offset term. The user may add the value of the offset to the predicted value if desired." I am just not sure how exactly, especially for a Poisson model, where I believe the offset is multiplicative ? For example: library(MASS) fit1 <- glm(Claims ~ District + Group + Age +

Hi, I have run into another problem using offsets, this time with the predict function, where there seems to be a contradiction again between the behavior and the help page. On the man page for predict.lm, it says Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be. While it indicates nothings about

I am fitting a cox PH model w/ 2 predictors, x1 = 0/1 treatment variable and x2=continuous variable. I am using natural splines (ns) to model the effect of x2. I would like to examine the estimated effect of x2 on the hazard. I have tried various approaches (below; let model.fit= fitted model using coxph in survival library): 1. The simplest method appears to be using termplot(model.fit).

After fitting and pruning an rpart model, it is often the case that one or more of the original predictors is not used by any of the splits of the final tree. It seems logical, therefore, that values for these "unused" predictors would not be needed for prediction. But when predict() is called on such models, all predictors seem to be required. Why is that, and can it be easily