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Hi, Interpretation problem ! so what i did is by using the: >fit1 <- fitdist(vectNorm,"beta") Warning messages: 1: In dbeta(x, shape1, shape2, log) : NaNs produced 2: In dbeta(x, shape1, shape2, log) : NaNs produced 3: In dbeta(x, shape1, shape2, log) : NaNs produced 4: In dbeta(x, shape1, shape2, log) : NaNs produced 5: In dbeta(x, shape1, shape2, log) : NaNs produced 6: In

I've discovered an infelicity (I guess) in qbeta(): it's not a bug, since there's a clear warning about lack of convergence of the numerical algorithm ("full precision may not have been achieved"). I can work around this, but I'm curious why it happens and whether there's a better workaround -- it doesn't seem to be in a particularly extreme corner of parameter

Hi folks, I have a question about how to efficiently produce random numbers from Beta and Binomial distributions. For Beta distribution, suppose we have two shape vectors shape1 and shape2. I hope to generate a 10000 x 2 matrix X whose i th rwo is a sample from reta(2,shape1[i]mshape2[i]). Of course this can be done via loops: for(i in 1:10000) { X[i,]=rbeta(2,shape1[i],shape2[i]) } However,

Good-day Sir, I am R.Language users but am try to? estimate parameter of beta distribution particular dataset but give this error, which is not clear to me: (Initial value in "vmmin" is not finite) beta.fit <- fitdistr(data,densfun=dbeta,shape1=value , shape2=value) kindly assist. expecting your reply:

Dear R-Developers, I just started learning how to use Rcpp. Earlier while using it, I encountered an error as shown below: file74d8254b96d4.cpp: In function ‘Rcpp::NumericVector foo(Rcpp::NumericVector, Rcpp::NumericVector, Rcpp::NumericVector, Rcpp::Function, Rcpp::Function)’: file74d8254b96d4.cpp:10: error: invalid operands of types ‘SEXPREC*’ and ‘R_len_t’ to binary ‘operator/’ make: ***

Hi R users: I'm trying to make a function where two of the parameters are functions, but I don't know how to put each set of parameters for each function. What am I missing? I try this code: f2<-function(n=2,nsim=100,fun1=rnorm,par1=list(),fun2=rnorm,par2=list()){ force(fun1) force(fun2) force(n) p1<-unlist(par1) p2<-unlist(par2) force(p1) force(p2)

Dear R gurus, I have a function "goftests" that receives the following arguments: * a vector "x" of data values; * a distribution name "dist"; * the dots list ("...") containing a list a parameters to pass to CDF function; and calls several goodness-of-fit tests on the given data values against the given distribution. That is: ##### BEGIN CODE SNIP #####

I have the following problem: I have a vector x of data (0<x<=1 ) with a U-shaped histogram and try to fit a beta distribution using fitdistr. In fact, hist(rbeta(100,0.1,0.1)) looks a lot like my data. The equivalent to the example in the manual sometimes work: > a <- rbeta(100,0.1,0.1) > fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))1) > shape1

Despite the need to focus on pbeta, I'm still willing to put in some effort. But I find it really helps to have 2-3 others involved, since the questions back and forth keep matters moving forward. Volunteers? Thanks to Martin for detailed comments. JN On 2020-03-26 10:34 a.m., Martin Maechler wrote: >>>>>> J C Nash >>>>>> on Thu, 26 Mar 2020

Hallo, I need your help. I fitted my distribution of data with beta-prime, I need now to plot the Cumulative distribution. For other distribution like Gamma is easy: x <- seq (0, 100, 0.5) plot(x,pgamma(x, shape, scale), type= "l", col="red") but what about beta-prime? In R it exists only pbeta which is intended only for the beta distribution (not beta-prime) This is

I have a problem in where i generate m independent draws from a binomial distribution, say draw1 = rbinom( m , size.a, prob.a ) then I need to use each draw to generate a beta distribution. So, like using a beta prior, binomial likelihood, and obtain beta posterior, m many times. I have not found out a way to vectorize draws from a beta distribution, so I have an explicit for loop

I am new to R and learned to program 10 years ago in C++. I am currently working a project that looks at the distribution of randomly generated beta values. I take 20 random beta values find their sum, repeat 100000 times. Here is my code that it took me 4 hours to get s=numeric(length=100000) for(i in 1:100000){ pop=(rbeta(n=20,shape1=2,shape2=1)) s[i]=sum(pop) } So now I have them all in

I have been trying to use the rbeta() function to generate random beta variates with a particular mean and standard deviaiton for some simulation problems I am working on, however I am unsure of the relationship between the parameters shape1 and shape2 and the mean and standard deviation of the beta distribution. I am guessing that there is a formula for this, but I am unsure what it is. Can

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

HI All: Does anyone know the code behind the qbeta function in R? I am using it to calculate exact confidence intervals and I am getting 'NaN' at places I shouldnt be. Heres the simple code I am using: k<-3 > x<-NULL > p<-rbeta(k,3,3)# so that the mean nausea rate is alpha/(alpha+beta) > min<-10 > max<-60 > n<-as.integer(runif(3,min,max)) > for(i in

make check of R-alpha_2006-04-08_r37675 fails on Debian GNU/Linux 3.1 running on an Intel P4 computer. > version _ platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status

Dear friends. Is this as expected ? Is alpha and beta too large simply ? > dbeta(.1,534,646) [1] NaN Warning message: NaNs produced in: dbeta(x, shape1, shape2, log) Best wishes Troels -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Dear all, we received a bug report for betareg, that in some cases the optim call in betareg.fit would hang the R session and the command cannot be interrupted by Ctrl-C? We narrowed down the problem to the dbeta function which is used for the log likelihood evaluation in betareg.fit. Particularly, the following command hangs the R session to a 100% CPU usage in all systems we tried it (OS X

Hello, I am new to R and have two easy questions. How can you plot multiple density functions in one graph? I have five beta densities that I would like to plot in one graph. I understand how to plot one beta density as a line: plot (x,(dbeta(x,shape1=,shape2=,), type ="l") Does the Pareto distribution need to be added to R with an additional package? thanks, John [[alternative

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){