search for: rnbinom

I am trying to reset the default arguments in the rnbinom function with the following example code: params <- c("size" = 1, "mu" = 1) formals(rnbinom)[names(params)] <- params rnbinom(n = 10) It returns the following: Error in rnbinom(n = 10) : argument "prob" is missing, with no default If I set the defaults with t...

I don't want to spoil a nice joke, and recognise it's not a bug as it does have the intended effect, but the error message that is returned if rnbinom is given four parameters is slightly obscure: > rnbinom(0.5,0.5,0.5,0.5) Error in rnbinom(0.5, 0.5, 0.5, 0.5) : couldn't find function "error" > Cheers, Mike. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://w...

The documentation states : An alternative parametrization (often used in ecology) is by the mean ?mu?, and ?size?, the dispersion parameter. However, this fails : > rnbinom(10, mu = 100, size = 0) [1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Warning message: In rnbinom(10, mu = 100, size = 0) : NAs produced For dispersion set to 0, it should work like drawing from a Poisson distribution. It also produces incorrect draws for non-zero dispersions : > rnbinom(10, m...

Just for the records (was on R-help). Uwe Ligges -------- Original Message -------- Subject: Re: [R] minor quirk in rnbinom Date: Fri, 14 Jun 2002 18:47:59 +0200 From: Uwe Ligges <ligges@statistik.uni-dortmund.de> To: Mike Lonergan <mel@mcs.st-and.ac.uk> CC: r-help <r-help@stat.math.ethz.ch> References: <NEBBJLLBCLEDFLCCBKDAEEEKCGAA.mel@mcs.st-andrews.ac.uk> Mike Lonergan wrote: > > I don...

wanfairos wrote: > > Dear members; > > I have one problem to display my simulated data. Basically, I've generated > a set of rnorm (std.norm.mat)and rnbinom (gen.data) data to test on the > performance of FE Poisson model estimates > > ... Long self-contained example shorted > > my.data<-rep(9999,sim.size) > > for(i in 1: sim.size){ > ... > gen.data <- matrix(rnbinom(num.i * num.t, r.param,p.param),ncol=2) > &gt...

...) title(main = list(paste("Normal"), ????????????????????????? col="black", cex = 1.0)) # Plot 2 plot(rpois(10, 4),type="l",col="blue") title(main = list(paste("Poison"), ????????????????????????? col="black", cex = 1.0)) # Plot 3 plot(rnbinom(10, mu = 4, size = 100),type="l",col="green") title(main = list(paste("Binomial"), ????????????????????????? col="black", cex = 1.0)) # Plot 4 plot(runif(10),type="l",col="black") title(main = list(paste("Uniform"), ????????????...

Dear R-helpers, let us assume, that I have the following dataset: a <- rnbinom(200, 1, 0.5) b <- (1:200) c <- (30:229) d <- rep(c("q", "r", "s", "t"), rep(50,4)) data_frame <- data.frame(a,b,c,d) In a first step I run a glm.nb (full code is given at the end of this mail) and want to predict my response variable a. In a seco...

...iven by pnbinom. Shouldn't it be the same? This is only the case, I think, if I use the parameter size = 1. I just tried it for size = 2 and the result seemed correct.. Anyways, this is what I tried: #parameters proba<-0.001039302; s<-1; #random numbers with this distribution dom<-rnbinom(10000,s,proba); #first plot plot(ecdf(dom),do.points=FALSE); #second plot x<-seq(0,8700,0.5); lines(x,pnbinom(x,s,proba),col="red");

...nt, but maybe someone can make an argument for the current behavior). If people think these are really bugs I can submit them, together or separately. 1. rlnorm(n,mean,sd) gives NaN for sd=0, rather than always returning the mean. This is inconsistent with rnorm(), which returns the mean. 2. rnbinom(n,prob,size) gives NaN when prob=1; always returning 0 would be reasonable, and consistent with the output of dnbinom(). [This is equivalent to rnbinom(n,mu=0,size) in the new parameterization.] 3. rgamma(n,shape,scale) happily returns 0 when shape=0 or scale=0. However, dgamma(x,shape=0) [and...

Hi, I make some simulations with rnbinom and try to test with glm.nb. But in some data set the glm.nb fail. Look: pop <- rnbinom(n=1000,size=1,mu=0.05) > glm.nb(pop~1,maxit=1000) Error in while ((it <- it + 1) < limit && abs(del) > eps) { : missing value where TRUE/FALSE needed look some pop charactetistics: &...

Hi R-Users! I fitted a negative binomial distribution to my count data using the function glm.nb() and obtained the calculated parameters theta (dispersion) and mu. I would like to simulate values from this negative binomial distribution. Looking at the function rnbinom() I was looking at the relationship between the two possible parametrizations of the negative binomial and found that for this fuction I must use: prob = theta/(theta+mu) and size = theta Theta, however, is not an integer. So how can size (which is the number of successes) equal theta?...

...bayes) old.parms=new.parms new.parms=c(k1,mu1,k2,mu2,prob) err=max(abs((old.parms-new.parms)/new.parms)) iter=iter+1 } lines(xvals,prob*dnbinom(xvals,size=k1,mu=mu1)+ (1-prob)*dnbinom(xvals,size=k2,mu=mu2),col="red") print(list(k1=k1,mu1=mu1,k2=k2,mu2=mu2,p=prob,iter=iter,err=err)) } y=c(rnbinom(900,size=10,mu=5),rnbinom(100,size=15,mu=10)) vals=mixnbinom(y,10,7,20,12,0.8) -- View this message in context: http://www.nabble.com/How-to-avoid-the-NaN-errors-in-dnbinom--tf4680211.html#a13373226 Sent from the R help mailing list archive at Nabble.com.

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 the name of the objective function stored in the element name, how can modify the next t...

...R gives no response??? thank you in advance. here is my sessionInfo i386-apple-darwin8.10.1 locale: zh_CN.UTF-8/zh_CN.UTF-8/C/C/zh_CN.UTF-8/zh_CN.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] mclust_3.1-5 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), hdev) ----------------------------------------------- Peng Jiang 江鹏 ,Ph.D. Candidate Antai College of Economics & Management 安泰经济管理学院 Department of Mathematics 数学...

...work with family=negbin() definition? It seems to me that the values returned by type="response" are far off the observed values. Here is an example output from the negbin examples: > set.seed(3) > n<-400 > dat<-gamSim(1,n=n) > g<-exp(dat$f/5) > dat$y<-rnbinom(g,size=3,mu=g) > b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=negbin(3),data=dat) > summary(y) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.6061 1.6340 2.8120 2.7970 3.9250 4.9830 > summary(predict(b,type="response")) Min. 1st Qu. Median Mean 3rd Qu. Max....

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

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

On 1/20/20 4:26 AM, Martin Maechler wrote: > Coming late here -- after enjoying a proper weekend ;-) -- > I have been agreeing (with Spencer, IIUC) on this for a long > time (~ 3 yrs, or more?), namely that I've come to see it as a > "design bug" that rpois() {and similar} must return return typeof() "integer". > > More strongly, I'm actually pretty

Can anyone explain to me why the AIC values are so different when using glm.nb and glm with a negative.binomial family, from the MASS library? I'm using R 1.8.1 with Mac 0S 10.3.4. >library(MASS) > dfr <- data.frame(c=rnbinom(100,size=2,mu=rep(c(10,20,100,1000),rep(25,4))), + f=factor(rep(seq(1,4),rep(25,4)))) > AIC(nb1 <- glm.nb(c~f, data=dfr)) [1] 1047 > AIC(glm(c~f, family=negative.binomial(nb1$theta), data=dfr)) [1] -431804 Actually, the difference is already apparent with the function lo...

...I often would like the estimate for overdispersion to depend on the covariates in the same manner as the mean. For example, #either library(mgcv) or library(gam): x <- seq(0,1,length = 100)*2*pi mu <- 4+ 2*sin(x) size <- 4 + 2*cos(x) data <- cbind.data.frame(x<- rep(x,10), y = rnbinom(10*100,mu=rep(mu,10),size=rep(size,10))) x.gam <- gam(y~s(x), data=data,family=quasipoisson()) plot(x.gam) summary(x.gam) How would I get a smooth estimate of the overdispersion ? Thanks, Markus [[alternative HTML version deleted]]