similar to: lme: NaNs in hierarchical model

I'm trying to fit a Weibull distribution to some data via maximum likelihood estimation. I'm following the procedure described by Doug Bates in his "Using Open Source Software to Teach Mathematical Statistics" but I keep getting warnings about NaNs being converted to maximum positive value: > llfunc <- function (x) { -sum(dweibull(AM,shape=x[1],scale=x[2], log=TRUE))} >

Hello! I am trying to generate a sample from a truncated multivariate normal distribution using rtmvnorm. I am using Gibbs because my alpha (line below) is teeny-tiny ( 4.083475e-64 ) alpha = pmvnorm(lower=lower, upper=upper, mean=btilde, sigma=MyVarCovar). When I try my Gibbs run, it takes quite a long time (with 10,000 iterations and 5,000 iterations burn-in) ~ 22 sec. And then what I get is

Hello! How can I test if floating-point value is NaN or positive/negative infinity?

I forgot to forward to the rest of the list... -------- Original Message -------- Subject: Re: [LLVMdev] NaNs and Infinities Date: Mon, 19 Apr 2010 23:41:43 -0700 From: Javier Martinez <javier at jmartinez.org> To: lost <lostfreeman at gmail.com> Hi, If it's an immediate you can cast the SDValue to a ConstantFP and use the isNaN() or isInfinity() member functions (see below).

Full_Name: Steve Taylor Version: rw1071 OS: Windows NT 4 Submission from: (NULL) (203.98.50.22) eigen(matrix(NaN,2,2)) causes R to lock up and stop responding

I get this (with R-2.12 and R-2.13, didn't try with earlier versions): > max(c(NaN, NA)) [1] NA > max(c(NA, NaN)) [1] NaN I get the same thing with min(). The fact that the result of 'max(x)' or 'min(x)' depends on the order of the elements in 'x' is surprising. It also seems to contradict the man page: By definition the min/max of a numeric vector

Hi friends, Any idea why do i get this warning?And also why all computed p-values are NaN. Have shown below what i did in Windows r-console.:-- > df c1 c2 1 1 50 2 NA NA 3 4 NA 4 7 6 5 NA 7 6 10 10 > r<-cor(x=df,y=NULL,use="complete.obs",method=c("pearson")) > r c1 c2 c1 1.0000000 -0.9148074 c2 -0.9148074 1.0000000 > cor.p.values<-

Hello, I am working with the nls() function and inserting a formula into it that uses the pweibull function. However the pweibull function is annoyingly producing NaNs, which nls() refuses to handle. I have put a sample of the code below. Is there a way to prevent these NaNs from interfering, for example a method to catch them? I get the following error when I try to run the code: res.nls <-

I would presume this is another manifestation of what I reported (reproduced below) on 2003-12-01. cajo.terbraak at wur.nl wrote: >Full_Name: cajo ter Braak >Version: 2.1.1 >OS: Windows >Submission from: (NULL) (137.224.10.105) > > ># I would like to attach the matrix C in the Rdata file; it is 50x50 and comes >from a geostatistical problem (spherical covariogram) >

Hi R-devel, I have a question about the differentiation between NA and NaN values as implemented in R. In arithmetic.c, we have int R_IsNA(double x) { if (isnan(x)) { ieee_double y; y.value = x; return (y.word[lw] == 1954); } return 0; } ieee_double is just used for type punning so we can check the final bits and see if they're equal to 1954; if they are, x is NA, if they're

Hi, I'm using R to do a series of calculation and I have gotten several warnings that say "NaNS produced". Whatever I could read on line gives me an idea that this warning is produced when the number is use is a negative log or otherwise mathematically problematic. I'm getting this error while using factorial() and gamma () on strictly positive numbers (always greater than zero).

In r187314, APFloat multiplication by with NaNs was made to always yield a positive NaN. I am wondering whether that was the correct decision. It is of course true that the result of a multiplication is undefined in IEEE, however, we were using multiplication by -1.0 to implement IEEE negate, which is defined to preserve the sign bit. r210428 made 0-NaN have IEEE negate behavior, which is good

FYI, I was looking at the SSE/AVX codegen here: http://llvm.org/bugs/show_bug.cgi?id=20578 If LLVM starts caring about FP exceptions, even this won't be possible. Is there a way of doing an IEEE-754 fneg in C/C++? Ie, there's no fneg() in libm, so any C method we choose could cause an exception, and that's not allowed by the IEEE definition of fneg. On Fri, Aug 8, 2014 at 12:29 PM,

On 7 August 2014 20:52, Keno Fischer <kfischer at college.harvard.edu> wrote: > One more update: Since the code generated by the bitcast wasn't ideal > and we were afraid to loose vectorization, etc., we ended up going > with fsub -0.0, x, which for some reason unlike fsub 0.0, x, seems to > be have appropriately at all optimization levels. That's because "fsub 0.0,

Hi, sorry for repeating the question but this is kind of important to me and i don't know whom should i ask. So as noted before when I do a parameter fit to the beta distr i get: fitdist(vectNorm,"beta"); Fitting of the distribution ' beta ' by maximum likelihood Parameters: estimate Std. Error shape1 2.148779 0.1458042 shape2 810.067515 61.8608126 Warning

Full_Name: cajo ter Braak Version: 2.1.1 OS: Windows Submission from: (NULL) (137.224.10.105) # I would like to attach the matrix C in the Rdata file; it is 50x50 and comes from a geostatistical problem (spherical covariogram) > rm(list=ls(all=TRUE)) > load(file= "test.eigen.Rdata") > ls() [1] "C" "eW" > > sym.check = max(abs(C - t(C))) # should

Dear R-list, *When comparing two logistic regression models with the anova CHi test, I obtain the following error: (there are no NA's in the time series). How can this be solved such that I can compare two models on the same dataset were different explanatory variables are used? l.KBDI <- glm(zna.arson2 ~ zna.KBDI,family = binomial) l.NDWI <- glm(zna.arson2 ~ zna.NDWI,family

Guys, I'm having an error when I use the command: library(MASS)> dados<-read.table("inverno.txt",header=FALSE)> vento50<-fitdistr(dados[[1]],densfun="weibull")Mensagens de aviso perdidas:1: In dweibull(x, shape, scale, log) : NaNs produzidos2: In dweibull(x, shape, scale, log) : NaNs produzidos3: In dweibull(x, shape, scale, log) : NaNs produzidos4: In

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I

Ok. That you for clarifying the point for me. I was primed for a regression because this behavior changed over llvm versions and was causing my tests to fail ;). I'm now doing bitcasting to int, xoring with the signbit and bitcasting back. On Thu, Aug 7, 2014 at 2:59 AM, Owen Anderson <resistor at mac.com> wrote: > Subtraction is also not a correct implementation of negation, for