similar to: package "relations" updated

An update on this: I just patched HoltWinters() to use optimize() in the univariate case, and it now computes the correct value. David John Bodley wrote: > Hi, > > Thanks for the quick response. I upgraded by version of R on Windows to the > latest (2.7.0) and re-ran the analysis and get the same result of 48.87989. > > The original time series was a non-regular zoo()

Dear useRs, a new version of the 'relations' package has appeared on CRAN. New features include: o support for fuzzy relations added o support for sets moved to separate 'sets' package o new SD fitters for the S ("symmetric") and M ("matches") families o fitters for Cook-Seiford method and Euclidean consensus added o fitters can now use a

Dear useRs, a new version of the 'relations' package has appeared on CRAN. New features include: o support for fuzzy relations added o support for sets moved to separate 'sets' package o new SD fitters for the S ("symmetric") and M ("matches") families o fitters for Cook-Seiford method and Euclidean consensus added o fitters can now use a

I get John's value (48.8789) in 2.7.0 and R-devel (both on Ubuntu). Really seems to be a numeric issue: > HoltWinters(x, beta = 0, gamma = 0)$alpha alpha 48.87989 > HoltWinters(x * 1.0000000001, beta = 0, gamma = 0)$alpha alpha 0.6881547 > HoltWinters(x * 1.00000000001, beta = 0, gamma = 0)$alpha alpha 48.87989 Providing starting values seems to help, but not

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

I am trying to fit the generalised linear model for the negative binomial, but the results which come out are attached below. When we fit this model using few covariates, the model converge. Does it mean that this family is fitted differently from other glm? or the number of zeros in my response variable has a limiting factor? Thanks Bruno fit <- glm.nb(pfde~SEX+...., data=data1) Warning

Dear useRs, it is our great pleasure to announce the new package "relations" to appear on all CRAN-mirrors soon. This package provides data structures and methods for creating and manipulating relations, relation ensembles, sets, and tuples. The feature list includes: * creation of relations by domain and graph/characteristic function/incidences, * extraction of characteristic

Dear useRs, it is our great pleasure to announce the new package "relations" to appear on all CRAN-mirrors soon. This package provides data structures and methods for creating and manipulating relations, relation ensembles, sets, and tuples. The feature list includes: * creation of relations by domain and graph/characteristic function/incidences, * extraction of characteristic

hi, I applied coxph to my matrix of 300 samples and 215 variables and got the following error Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: Ran out of iterations and did not converge in: fitter(X, Y, strats, offset, init, control, weights = weights, 26% of time data is censored and here

Dear all, I am reading the cch function source code. But I can not understand the following codes. Please help me. What's the fitter here? fitter <- get(method) out <- fitter(tenter = tenter, texit = texit, cc = cc, id = id, X = X, ntot = nn, robust = robust) [[alternative HTML version deleted]]

Hi, I have a script that takes a subset of genes on a microarray and tries to fit a coxph model to the expression values for each gene. This seems to work fine but in some cases it produces warnings and/or errors. For example: Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: In fitter(X, Y,

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

Good day R help list!!! I've been trying to do Bootstrap in R on Censored data. I encountered WARNING/ERROR messages which I could not find explanation. I've been searching on the literature for two days now and still can't find answers. I hope there's anyone out there who can help me with these two questions: 1. If the "Loglik converged before variable..." message

Hi, am very new to R and I've written an optim function, but can't get it to work least.squares.fitter<-function(start.params,gr,low.constraints,high.constraints,model.one.stepper,data,scale,ploton=F) { result<-optim(par=start.params,method=c('Nelder-Mead'),fn=least.squares.fit,lower=low.constraints,upper=high.constraints,data=data,scale=scale,ploton=ploton)

John, You can make a local version of bootcov which either: deletes these arguments from the call to fitter, or modify the switch statement to include rq.fit, the latter would need to also modify rq() to return a fitFunction component, so the first option is simpler. One of these days I'll incorporate clustered se's into summary.rq, but meanwhile this seems to be a good alternative.

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d")

I have a quick question, and I apologize in advance if, in asking, I expose my woeful ignorance of R and its packages. I am trying to use the bootcov function to estimate the standard errors for some regression quantiles using a cluster bootstrap. However, it seems that bootcov passes arguments that rq.fit doesn't like, preventing the command from executing. Here is an example:

R 2.8.1 windows XP I am trying to learn how to use the boot function to perform a bootstrap of a regression. I have written a short trial program, shown below. Clearly I have done something wrong as the output of each of the 100 bootstrap values for the regression are exactly the same - there does not appear to be any bootstrap respampling!. What have I done wrong? # Define function to be run.

Dear R-help folks: Here is my platform: > version platform sparc-sun-solaris2.7 arch sparc os solaris2.7 system sparc, solaris2.7 status major 1 minor 5.0 year 2002 month 04 day 29 language R I have a

Dear useRs, we are organizing the following session Topic: Innovative Tools in Data Analysis Organizers: Achim Zeileis and Bettina Gruen at the First Workshop of the ERCIM Working Group on Computing & Statistics June 19-21, 2008 Neuchatel, Switzerland URL: http://www.dcs.bbk.ac.uk/ercim08 To improve the quality of statistical data analysis the provision of innovative tools which make new