similar to: How to generate a matrix of Beta or Binomial distribution

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)

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

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 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

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

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

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:

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

hi, i'm new to R and i'm trying to port a quattro pro spreadsheet into R. spreadsheets have optional lower and upper limit parameters on the beta distribution function. i would like to know how to incorporate this with R's pbeta function. thanks in advance, mara. ____________________________________________________________________________________ Park yourself in front of a

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, 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

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

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",

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'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 R users: I try this code, where "fun" is a parameter of a random generating function name, and I pretend to use "..." parameter to pass the parameters of different random generating functions. What am I doing wrong? f1<-function(nsim=20,n=10,fun=rnorm,...){ vp<-replicate(nsim,t.test(fun(n,...),fun(n,...))$p.value) return(vp) } This works! f1()

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

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, 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 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){