similar to: Sign differences amoung QR solutions.

*(note*: This is an R community question, not a statistical nor coding question. Since this is my first time writing such a post, I hope no one will take offence of it.) Hello all, I will be attending useR 2009 next week, and was wondering if there are any of you who are *bloggers *intending to participate and report on useR 2009? If so - I would love to know your blogs URL so as to follow you.

I tried optim using the SANN algoithm. To start things out I tried the example of solving the "traveling salesman" problem as given in the documentation. The example works just fine. But if I comment out the line: set.seed(123) # chosen to get a good soln relatively quickly More often than not it doesn't converge to the optimum solution as shown in the example. Alos with trace on

Dear All, I have to use loop over an array so I am using following procedure count<-1 repeat{ count<-count + 1 c(g[count],1:i[count]) ->qw if(count>5)break } as a result qw is [1] 0.9643836 1.0000000 2.0000000 3.0000000 4.0000000 5.0000000 [7] 6.0000000 7.0000000 8.0000000 9.0000000 10.0000000 11.0000000 [13] 12.0000000 13.0000000 14.0000000 15.0000000 16.0000000

I'm sory for my weak english. I need to analyze this subject : x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 y 0 0 1 0 0 1 0 0 1 0 czarne 1 1 0 0 0 0 1 0 0 0 rude 0 0 1 0 0 1 1 0 0 0 braz 0 0 1 0 1 0 1 0 0 0 blond 1 0 0 0 0 1 0 0 0 1 rude 1 1 0 0 0 0 0 0 0 1 blond 0 0 1 1 0 0 0 0 1 0 czarne 1 0 0 1 0 0 1 0 0 0 blond 0 0 1 0 0 1 1 0 0 0 blond 1 0 0 0 0 1 1 0 0 0 czarne 0 0 1 0 0 1 0 0 0 1 czarne 1 0 1 0 0 0

Dear R-helpers, I can not invoke maxent() in Mac OSX. Could you give me any directions on that? Thank you in advance. Here is my info: # (1) the error > me <- maxent(predictors, occtrain, factors='biome') me <- maxent(predictors, occtrain, factors='biome') Error in .jcall(mxe, "S", "fit", c("autorun", "-e", afn, "-o",

Dear R-helpers and Dr. Hijmans, I can not invoke maxent() in GNU Linux. Could you give me any directions on that? There is maxent.jar in version 3.3.3e in the right folder. ######################################## R: > jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='') > jar [1] "/ebio/abt6/jmao/rpacks/dismo/java/maxent.jar"

I was looking at how the model.frame method for lm works and comparing it to my own for coxph. The big difference is that I try to retain xlevels and predvars information for a new model frame, and lm does not. I use a call to model.frame in predict.coxph, which is why I went that route, but never noted the difference till now (preparing for my course in Nashville). Could someone shed light

Hi, Suppose i have generated an object using the following : fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) And when i print fit, i get the following : n= 81 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 81 17 absent (0.7901235 0.2098765) 2) Start>=8.5 62 6 absent (0.9032258 0.0967742) 4) Start>=14.5 29 0 absent (1.0000000

Hi, Using package gclus in R, I have created some graphs that show the trends within subgroups of data and correlations among 9 variables (v1-v9). Being interested for more details on these data I have produced also the var-covar matrices. Question: From a pair of two subsets of data (with 9 variables each, I have two var-covar matrices for each subgroup, that differ for a treatment on one

Forum, I am attempting to plot weekly horizontal histograms, something very similar to: http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=109 The part of the code used to create this graphic that interest me: # Draw the histograms for (i in 1:length(ages)){ par(new = TRUE) xmin <- -ages[i] + xlim[1] xmax <- xlim[2] - ages[i] ser <- freqs[, i+1] ser <-