similar to: predict.lda breaks when priors are specified

Full_Name: Ed Borasky Version: R-beta 2.1.0 2005-04-08 OS: Linux 2.6.11 GCC 3.3.5 Submission from: (NULL) (24.21.57.139) I downloaded the latest R-beta tarball and did a build with the default options. OS is Linux 2.6.11 and compiler is GCC 3.3.5. "make check-all" failed with the following message: make[3]: Entering directory `/home/znmeb/R-beta/tests' running code in

Here is an example: The legends (mainly the factor level names) are cut off on the right. Somehow the internal calculation which computes horizontal space for the legend is not flexible enough. ## Call a new graphics window {with default par()s !}: get(getOption("device"))() par(mfrow = c(2,2)) ## part of example(interaction.plot) _improved_ using with() : data(OrchardSprays)

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Full_Name: Axel Benz Version: 1.7.1 OS: Windows Submission from: (NULL) (137.251.33.43) Hello, I guess many people will answer me again that this is a S language feature, but I am only a stupid computer scientist and I simply do not understand this logic, despite of reading a lot about S: > test field tuckey 4 Kreis2 -1 5 Kreis5 -2 9 Metall -3 17

Hi all, If I run LDA on the same data (2-class classification) with default(no priors specified in the lda function) vs. "prior=c(0.5, 0.5)", the results are different. The (0.5, 0.5) priors give better 1-classify-to-1 rate, and the proportional priors(default, no priors specified in the lda function) give better 0-classify-to-0 rate, for both training and testing data sets. However,

Good day My name is Jos? Oscar, I'm from Mexico and I have some questions about foreign in your write.foreig( ) function. We know that this function generates the inputs to be able to run them or execute them in another program like SPSS, SAS or Stata. In these cases, when creating an example file, a matrix and using the function to execute the .sps file directly from SPSS, I don't care or

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

I suggest to add a panel function to levelplot (or perhaps to an other 3d lattice function) which is able to translate the z values into the size of the rectangles. It could be used to display categorical data. I append the proposed code and two examples: - panel.catlevelplot() - example1.catlevelplot.esoph() - example2.catlevelplot.esoph() Wolfram Fischer #------ CODE

1. (Mainly) Statistical issues are generally off topic on this list. You might want to try the r-sig-mixed-models list instead. 2. However, I think a better answer is to seek local statistical expertise in order to have an extended discussion about your research intent in order to avoid producing yet more irreproducible psychological research. Cheers, Bert Bert Gunter "The trouble with

Hi all, I previously emailed about a multinomial model, and after seeking some additional help, realized that since my response/outcome variables are not mutually exclusive, I need to use a multi-response model that is *not* multinomial. I'm now trying to figure out how to specify the priors on the multi-response model. Any help would be much appreciated. My data look like this: X

This arose in testing [.terms and has me confused. data(esoph)?? # use a standard data set t0x <- terms(model.frame( ~ tobgp, data=esoph)) t1 <-? terms(model.frame(ncases ~ agegp + tobgp, data=esoph)) t1x <- (delete.response(t1))[-1] > all.equal(t0x, t1x) [1] TRUE # the above is wrong, because they actually are not the same > all.equal(attr(t0x, 'dataClasses'),

Hi all, I'm hoping I might be able to get some help with some issues specifying priors for MCMCglmm. I'm trying to fit a gaussian glmm using MCMCglmm to a data set with two (correlated) response variables. The response variables are both logit-transformed proportions (there are a few reasons why I've chosen these with gaussian error over binomal glmm, which I won't go into).

Dear List: Most of the analysis I do involves contingency tables. I am migrating to R from Stata and I have a number of questions about using contingency tables in R. I suspect that most of the things I want to do are very short R scripts that people on this list probably have. I wonder if you would be willing to share them. First, the presentation of tables by table() is not

Hi, I have a technical question about rpart: according to Breiman et al. 1984, different costs for misclassification in CART can be modelled either by means of modifying the loss matrix or by means of using different prior probabilities for the classes, which again should have the same effect as using different weights for the response classes. What I tried was this: library(rpart)

Hi Bert, That was distinctly unhelpful, and your outward hostility to a field you obviously don't understand reveals a regrettable level of ignorance. By the way, my research is Anthropology despite my job title. Michelle On Tue, May 1, 2018 at 2:48 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > 1. (Mainly) Statistical issues are generally off topic on this list. > You

I am trying to use t.test on the following data: date type INTERVAL nCASES MTF SDF MTO SDO nFST MF nOBS MO MB BIASCV BIASEV ME MAE RMSE CRCF 2001-06-15 avn GE1.00 4385 0.246 0.300 1.502 0.556 1367 1.373 4385 1.502 1.471 0.285 0.164 -1.256 1.266 1.399 0.056 2001-06-15 avn

On 02/05/18 09:53, Michelle Kline wrote: > Hi Bert, > > That was distinctly unhelpful Not if you actually follow Bert's advice. > and your outward hostility to a field you > obviously don't understand reveals a regrettable level of ignorance. I didn't see any hostility to any field. Bert, like many of us, objects to people blithely and arrogantly applying possibly

I am fairly new at using R/programming in general so I apologize if I am leaving crucial parts of the puzzle out, but here goes. First and foremost this is the error I am receiving: Error in muPriors[priors[, 1:2]] <- priors[, 3] : NAs are not allowed in subscripted assignments This occurs only when I am using observational priors and some number of nominal variables, it does not

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

Hi I would like to fit a non-linear regression to the follwoing data: quantiles<-c(seq(.05,.95,0.05)) slopes<-c( 0.000000e+00, 1.622074e-04 , 3.103918e-03 , 2.169135e-03 , 9.585523e-04 ,1.412327e-03 , 4.288103e-05, -1.351171e-04 , 2.885810e-04 ,-4.574773e-04 , -2.368968e-03, -3.104634e-03, -5.833970e-03, -6.011945e-03, -7.737697e-03 , -8.203058e-03, -7.809603e-03, -6.623985e-03,