similar to: update on dnbinom with large "size"

This came up on r-help but indicates a bug. dnbinom(x,n,p) calls dbinom_raw(n-1,...) which returns 0 for n<1. -thomas ---------- Forwarded message ---------- Date: Thu, 08 Feb 2001 17:10:23 +0000 From: Yudi Pawitan <yudi@stat.ucc.ie> To: Mark Myatt <mark@myatt.demon.co.uk> Cc: R-Help <r-help@stat.math.ethz.ch> Subject: Re: [R] Goodness of fit to Poisson / NegBinomial

Full_Name: Andrey Pavlov Version: 2.7.1 (2008-06-23) OS: Windows Vista Submission from: (NULL) (67.193.233.43) Dear developers, I discovered an issue with the dnbinom function while fitting a negative binomial model to my data. I was using the size and mu parameterization. When the size gets large enough, the function begins to return 1, while it should instead return the respective Poisson

Hi, The code below is giving me this error message: Error in while (err > eps) { : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In dnbinom(x, size, prob, log) : NaNs produced 2: In dnbinom(x, size, prob, log) : NaNs produced I know from the help files that for dnbinom "Invalid size or prob will result in return value NaN, with a warning", but I am not able

Dear R-help gurus (and T.Yee, the VGAM maintainer) - I've been banging my head against the keyboard for too long now, hopefully someone can pick up on the errors of my ways... I am trying to use optim to fit a zero-inflated negative binomial distribution. No matter what I try I can't get optim to recognize my initial parameters. I think the problem is that dnbinom allows either

my current code for dnbinom.c has prob = dbinom_raw(n,x+n,p,1-p,give_log); f = ((double)n)/((double)(n+x)); return((give_log) ? log(f) + prob : f*prob); which should work for x>=0, n>=0, 0<=p<=1. dbinom.raw(x,n,p) is intended to work for x>=0, n>=0, 0<=p<=1, without integer checks. When necessary (ie. for x in the dnbinom case) the checks should be done in the

Hello, After putting together interaction code that worked for a single pair of interactions, when I try to evaluate two pairs of interactions( flowers*gopher, flowers*rockiness) my computer runs out of memory, and the larger desktop I use just doesn't go anywhere after about 20 minutes. Is it really that big a calculation? to start: mle2(minuslogl = Lily_sum$seedlings ~ dnbinom(mu = a,

Dear R developers, I tried to use nlminb instead of optim for a current problem (fitting parameters of a differential equation model). The PORT algorithm converged much better than any of optim's methods and the identified parameters are plausible. However, it took me a while before spotting the reason of a technical problem that nlminb, in contrast to optim, does not pass names of the

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

Dear list, I am just starting on analysis of count data in R 3.4.0. My dataset was obtained from counting particles on a surface before andd after a cleaning process. The sampling positions on the surface are pre-defined and are the same before and after cleaning. I have ~20% of 0's. I want to know if the cleaning process was useful at reducing the number of particles. I first fit a

> On Oct 2, 2017, at 2:05 AM, David <dasolexa at hotmail.com> wrote: > > Dear list, > > > I am just starting on analysis of count data in R 3.4.0. My dataset was obtained from counting particles on a surface before andd after a cleaning process. The sampling positions on the surface are pre-defined and are the same before and after cleaning. I have ~20% of 0's. I

I am having trouble with optim. It claims to have converged to a minimum, yet it has in the course of the optimization visited many points which are closer to optimal. I would be grateful for any explanation of this behaviour. I'm trying to estimate the parameters in the model X ~ Binomial(1,p) * NegBin(mu,theta). So I define a log likelihood function, and invoke optim thus: o <- optim

Dear David, thanks ever so much for your answer. Do you mean predicting the original values based on the fitted model and then comparing observed vs. predicted by, for example, a scatterplot? Thanks, David ________________________________ De: David Winsemius <dwinsemius at comcast.net> Enviado: lunes, 2 de octubre de 2017 18:18:36 Para: David Cc: R-help Asunto: Re: [R] Help on

Hey, So I have a scatter plot and I am trying to plot a curve to fit the data based on a Holling Type III functional response. My function is this: nll2<-function(a,b) { conefun<-(a*DBH^2)/(b^2+DBH^2) nlls2<-dnbinom(x=cones ,size=DBH, mu=conefun,log=TRUE) -sum(nlls) } and my plot is this: plot (DBH,cones) DBH is on the x-axis and cones is on the y-axis. How do I get the curve

Hello I want to use the nlminb function but I have the objective function like characters. I can summarize the problem using the first example in the nlminb documentation. x <- rnbinom(100, mu = 10, size = 10) hdev <- function(par) -sum(dnbinom(x, mu = par[1], size = par[2], log = TRUE)) nlminb(c(9, 12), objective=hdev) With the last instructions we obtain appropriate results. If I have

Hi, dear R experts , I am new. I met this problem when I am trying to learn how to use the nlminb() function. I tried the example which the document provides ( as the following code ) and R gives no response . I don't know whether it is running or not and it takes a very long time but still output nothing so I just close the session window. my question is is there any method

All, I have some data on parasites on apple leaves and want to do a goodness of fit test to a Poisson distribution. This seems to do it: mites <- c(rep(0,70), rep(1,38), rep(2,17), rep(3,10), rep(4,9), rep(5,3), rep(6,2), rep(7,1)) tab <- table(mites) NSU <- length(mites) N <-

Dear Experts, Probably trivial, but I am struggling to get what I want: I need to know how the number of required trials to get a certain number of successes. By example: How many trials do I need to have 98% probability of 50 successes, when the a priory probability is 0.1 per trial. The Negative binomial function may do the job (not sure): NegBinomial {stats} The Negative Binomial

Full_Name: Chanseok Park Version: R 2.2.1 OS: RedHat EL4 Submission from: (NULL) (130.127.112.89) pbinom(any negative value, size, prob) should be zero. But I got the following results. I mean, if a negative value is close to zero, then pbinom() calculate pbinom(0, size, prob). dbinom() also behaves similarly. > pbinom( -2.220446e-22, 3,.1) [1] 0.729 > pbinom( -2.220446e-8, 3,.1)

Dear R users, I try to compute this summation, http://www.nabble.com/file/p25054272/dd.jpg where f(y|x) = Negative Binomial(y, mu=exp(x' beta), size=1/alp) http://www.nabble.com/file/p25054272/aa.jpg http://www.nabble.com/file/p25054272/cc.jpg In fact, I tried to use "do.call" function to compute each u(y,x) before the summation, but I got an error, "Error in X[i, ]

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik