similar to: Beta fit returns NaNs

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

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

Hi, Well, i need some help, practical and theoretical. I am wondering why the fitdistplus (mle function) is returning an error for this code: [code] x1 <- c(100,200,140,98,97,56,42,10,2,2,1,4,3,2,12,3,1,1,1,1,0,0); plotdist(x1); descdist(x1, boot =1000); y<- sum(x1); d= as.vector(length(x1)); for(i in 1:length(x1)){ d[i] = x1[i]/y; } fitdist(d, "beta") [/code] Error:

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 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){

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

Hi , I'm having problem with the following plot. Basically I have to do 51(length of d, see below) plots. I want 6 plots (say) in a page and rests are in different pages. win.graph() starts a plot in new page. But I'm not being able to combine par(mfrow = c(3,2)) and win.graph() within a loop. > d [1] 14.108 13.883 11.022 13.426 2.341 9.010 12.868 15.603 30.810 3.931 11.825

Please guide me through to resolve the error message that I get this is what i have done. >x1<- rnorm(100,2,1) >x1fitbeta<-fitdistr(x1,"beta") Error in fitdistr(x1, "beta") : 'start' must be a named list Yes, I do understand that sometime for the distribution to converge to the given set of data, it requires initial parameters of the distribution, to

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 R users, first I take this opportunity to greet all the R community for your continuous efforts. I wrote a function to calculate the pdf and cdf of a custom distribution (mixed gamma model). The function is the following: pmixedgamma3 <- function(y, shape1, rate1, shape2, rate2, prev) { density.function<-function(x) { shape1=shape1 rate1=rate1 shape2=shape2

Hi , I am new to R. I am trying to run a simple R script as shown below: aov.R ------ data1<-c(49,47,46,47,48,47,41,46,43,47,46,45,48,46,47,45,49,44,44,45,42,45,45,40 ,49,46,47,45,49,45,41,43,44,46,45,40,45,43,44,45,48,46,40,45,40,45,47,40) matrix(data1, ncol= 4, dimnames = list(paste("subj", 1:12), c("Shape1.Color1", "Shape2.Color1", "Shape1.Color2",

Buenos dias! Os escribo para ver si me podeis ayudar con un asunto en el que me he quedado un poco encallado. Lo que tengo que hacer es sacar los percentiles (0.001, 0.005, 0.95 y 0.999) de varias distribuciones beta, concretamente 418. Cada distribucion esta definida por los parametros "shape1" y "shape2". Por lo tanto tengo una base de datos de 418 filas y en cada una de

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

Hi all, I compiled 64-bit R on an Apple Mac G5 running OS X, but it failed make check. Simon Urbanek suggested I post results to R-devel. > On Nov 6, 2007, at 10:23 PM, Steven McKinney wrote: > > > Hi Simon, > > > > Would you be able to give more guidance on how to compile 64-bit > > libiconv for Tiger, > > You can get the sources from Apple and compile

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

Dear All, I need to fit a custom probability density (based on the symmetric beta distribution B(shape, shape), where the two parameters shape1 and shape2 are identical) to my data. The trouble is that I experience some problems also when dealing with the plain vanilla symmetric beta distribution. Please consider the code at the end of the email. In the code, dbeta1 is the density of the beta

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,

Hi, I am trying fit certain data into Beta distribution. I get the error saying "Error in fitdist(discrete_random_variable_c, "beta", start = NULL, fix.arg = NULL) : the function mle failed to estimate the parameters, with the error code 100" Below is the sorted data that I am trying to fit. Where am I going wrong. Thanks a lot for any help. Vinod

Hi, I noticed this problem on my home desktop running FC4 and again on my laptop running FC5. Both have previously compiled and passed make check-all on 2.3.1 svn revisions from 10 days ago or so. On both these machines, make check-all is consistently failing (4 out of 4 attempts on the FC 4 desktop and 3 out of 3 on the FC 5 laptop) in the p-r-random-tests tests. This is with both default

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: ***