similar to: Simulating scalar-valued stationary Gaussian processes

Dear R-help, I am looking for a function that will plot error bars in x- or y-direction (or both), the same as the Gnuplot function 'plot' can achieve with: plot "file.dat" with xyerrorbars,... Rsite-searching led me to the functions 'errbar' and 'plotCI' in the Hmisc, gregmisc, and plotrix packages. As I understand the descriptions and examples, none of

Hola a todos, Soy nuevo en R y estoy intentando modelizar una serie de datos no estacionarios usand la distribucion Generalizada de Valores Extremos GEV. ¿Podriais indicarme como se modeliza una tendencia polinómica (cuadrática, por ejemplo) en alguno de los 3 parámetros (situación, escala o forma)? He encontrado documentación a cerca de modelización linear o exponencial, pero no acabo de

LLVM knows what all the types of the intrinsic functions are; I thought, why are users (including llvm-gcc...) required to duplicate all this information in order to use them? I mean in order to call getOrInsertFunction to get declarations for them. So I wrote this patch, which allows all this code to be generated automatically. Is this a good approach? Dan -- Dan Gohman, Cray Inc. <djg at

On Mon, 5 Feb 2007, Dan Gohman wrote: > LLVM knows what all the types of the intrinsic functions are; I thought, > why are users (including llvm-gcc...) required to duplicate all this > information in order to use them? I mean in order to call > getOrInsertFunction to get declarations for them. That is an excellent question! :) In the bad old days, we used to allow intrinsics

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

Hello! I have something like this: test1 <- data.frame(intx=c(4,3,1,1,2,2,3), status=c(1,1,1,0,1,1,0), x1=c(0,2,1,1,1,0,0), x2=c(1,1,0,0,2,2,0), sex=c(0,0,0,0,1,1,1)) and I can easily fit a cox model: library(survival) coxph(Surv(intx,status) ~ x1 + x2 + strata(sex),test1) However, I want to

In IntrinsicEmitter::EmitTypeGenerate, called from IntrinsicEmitter::EmitGenerator, here for (unsigned j = 0; j != N; ++j) { OS << " ArgTys.push_back("; EmitTypeGenerate(OS, ParamTys[j], ArgNo); OS << ");\n"; } I'm hitting this assertion: if (ArgType->isSubClassOf("LLVMMatchType")) { unsigned Number =

Dear R users, I am trying to make this (3 by 10) matrix A --A---------------------------------------------------- 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.5 0.5 0 0 0 0 0 0 0 ------------------------------------------------------- from "mass.func" --mass.func------------------------------------------- > mass.func $`00` prop 5 1 $`10`

This is not exactly an R question but the R code below may make my question more understandable. If one plots sin(x) where x runs from -pi to pi , then the curve hovers around zero obviously. so , in a"stationary in the mean" sense, the series is stationary. But, clearly if one plots the acf, the autocorrelations at lower lags are quite high and, in the "box jenkins"

Hi ! Sorry for my ignorance. I have two questions: 1) which is in R the function to make a covariogram of spatial data ? 2) does anyone know if there exist the possibility in R of performing kriging with an arbitrary covariance function ? If I have well understood the Krig procedure offers only three possibilities (expcov, gauscov and sphercov) that are the common models for stationary and

Hi All, I am a newcomer to R. Could anyone explain me how to define link functions for either mu/sigma to allow for quadratic trends in the same, when fitting non-stationary GEV distributions? Thanks -- View this message in context: http://www.nabble.com/QUADRATIC-TREND-FOR-LINK-FUNCTIONS-ON-NON-STATIONARY-GEV-tp23360751p23360751.html Sent from the R help mailing list archive at Nabble.com.

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

That's a bug. I'm working on a fix.... On Apr 15, 2009, at 10:16 AM, Villmow, Micah wrote: > In IntrinsicEmitter::EmitTypeGenerate, called from > IntrinsicEmitter::EmitGenerator, here > for (unsigned j = 0; j != N; ++j) { > OS << " ArgTys.push_back("; > EmitTypeGenerate(OS, ParamTys[j], ArgNo); > OS << ");\n"; > }

Hi All, I am a newcomer to S/R. Could you please let me know how to model quadratic trends for the mul/sigl link functions when fitting non-stationary GEV distributions using the ismev package? Thanks Best Regards, Mohammad Ashrafuz Zaman PhD Candidate School of Engineering Building XC, Room 1.02 (Kingswood Campus) University of Western Sydney Locked Bag 1797, Penrith South DC NSW 1797

Hi I would like to use arima () to find the best arima model for y time series. The default in arima apparently is to use conditional sum of squares to find the starting values and then ML (as described on the help page). Now using the default may lead to error messages saying: "non-stationary ar part in CSS". When changeing the default to "ML" only the minimization

Newsgroup members, I appreciate the help on this topic. David Duffy provided a solution (below) that was quite helpful, and came close to what I needed. It did a great job creating two vectors of dichotomous variables with a known correlation (what I referred to as a phi-coefficient). My situation is a bit more complicated and I'm not sure it is easily solved. The problem is that I must

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

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

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Hi, ? I am very new to R. So, pardon my dumb question. I was trying to write my own function to run a different model (perform an ordered logistic regression) using the example in website http://pngu.mgh.harvard.edu/~purcell/plink/rfunc.shtml But R returns a error `R Error in eval(expr, envir, enclos) : object 's' not found' when I run it. What am I doing wrong here? Here's