similar to: MLE Estimation of Gamma Distribution Parameters for data with 'zeros'

Greetings, all I am having difficulty getting the fitdistr() function to return without an error on my data. Specifically, what I'm trying to do is get a parameter estimation for fracture intensity data in a well / borehole. Lower bound is 0 (no fractures in the selected data interval), and upper bound is ~ 10 - 50, depending on what scale you are conducting the analysis on. I read in the

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes

Hi, I am trying to estiamte parameters for gamma distribution using mle for below data using fitdist & fitdistr functions which are from "fitdistrplus" & "MASS"packages . I am getting errors for both functions. Can someone please let me know how to overcome this issue?? data y1<- c(256656, 76376, 6467673, 46446, 3400, 3100, 5760, 4562, 8000, 512, 4545, 4562,

INTRODUCTION TO THE PROBLEM I am trying to fit a distribution to a dataset. The distribution that I am currently considering is the (3-parameter) Singh-Maddala (Burr) distribution. The final model will fix the mean of the distribution to a given value and estimate the remaining parameters accordingly; however, the code provided below ignores this. For this distribution the three parameters

Hi All I have a data frame called "nbd" with two columns (x and T). Based on this dataset I want to find the parameters of a distribution with the following log-liklihood function and with r and alpha as its parameters: log(gamma(nbd$x+r))-log(gamma(r))+r*log(alpha)-(r+nbd$x)*log(nbd$T+alpha) the initial value for both parameters is 1. I would be thankful if you could help me

Hi, Actually I am facing a similar problem. I would like to fit both an ordinary (symmetric) and a non-central t distribution to my (one-dimensional) data (quite some values.. > 1 mio.). For the symmetric one, fitdistr or funInfoFun (using fitdistr) from the qAnalyst package should do the job, and for the non-central one.. am I right to use gamlss(x ~ 1, family=GT()) ? Anyway, I am a little

Hello, I was asked to try the following code on R, gamma.mles function (xx,shape0,rate0) { n<- length(xx) xbar<- mean(xx) logxbar<- mean(log(xx)) theta<-c(shape0,rate0) repeat { theta0<- theta shape<- theta0[1] rate<- theta0[2] S<- n*matrix(c(log(rate)-digamma(shape)+logxbar,shape/rate-xbar),ncol=1) I<- n*matrix(c(trigamma(shape),-1/rate,-1/rate,shape/rate^2),ncol=2)

Dear [R]-list, I'm looking for a classic equivalent of the wle.gamma function (library wle) that estimate robustly the shape and the scale parameters of gamma data. I have a vector of iid gamma rv : >data=rgamma(100,shape=10,scale=3) and a vector of their weights: >weights=c(rep(.5/70,70),rep(.25/20,20),rep(.25/10,10)) and want to estimate the scale and shape of the gamma

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Hey, I have a set of income data which I'd like to fit to a gamma distribution. How can I estimate the two parameters of the gamma distribution for a vector, e.g. c(2039L, 2088L, 5966L, 2353L, 1966L, 2312L, 3305L, 2013L, 3376L, 3363L, 3567L, 4798L, 2032L, 1699L, 3001L, 2329L, 3944L, 2568L, 1699L, 4545L) I sense this will be a very easy one-liner, but my searching didn't come up with

Hey, I make a regression for Gamma distribution with log link, in R and in SAS. In R, the dispersion is estimated by \phi=Deviance/(#_of_observations), In SAS, there are two options: \phi=Deviance/(#_of_observations-#_of_params) or \phi=Pearson/(#_of_observations-#_of_params). I understand that SAS formulae are correct, however, the coefficients, obtained by R and by the first version of SAS,

Der r helpers, I have a following set of data. Though some other software, I know it follows 3 parameter Gamma distrinution. I need to write a R code for estimating the parameters of 3 Parameter Gamma distribution. c(0,0,18561.9,0,0,0,34400,0,0,0,0,2190,0,0,0,0,60000,0,0,0, ? 19583,0,0,0,109872.87,0,0,0,0,0,0,1244,0,0,25150,0,500,0,0,0, ?

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Hi, I tried to calculate the MLE estimation of a parameter of a gamma distribution of a data set, which is called "amount".. I did it with the command: fitdistr(amount,"gamma") and got the error message: Error in optim(x = c(363.36, 551.15, 603.91, 170.99, 225.21, 218.01, 1816.82, : non-finite finite-difference value [2] Warning message: NaNs wurden erzeugt

Dear R users, Please help me on using "mix" function under package "mixdist". My data distribution shows there are two components for the mixture distribution: left part is an exponential and right part is a normal. So I plan to use "gamma" mixture distribution to estimate the parameters. Here is what I am using for the "mix" function. Test<-mix(x,

I have a data with 236 observations. After plotting the histogram, I found that it looks like non-central t distribution. I would like to get MLE for mu and df. I found an example to find MLE for gamma distribution from "fitting distributions with R": library(stats4) ## loading package stats4 ll<-function(lambda,alfa) {n<-200 x<-x.gam

Dear R Users, I am trying to obtain a best-fit analytic distribution for a dataset with 11535459 entries. The data range in value from 1 to 300000000. I use: fitdistr(data, "gamma") to obtain mle's for the parameters. I get the following error: Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : non-finite finite-difference value [1] And the following warnings:

Hello everyone, With th bar histogram (number of occurrences) hist<-c(24,7,4,1,2,1,1) of seven equally spaces classes ]1-4], ]5-8], ]9-12], ]13-16], ]17-20], ]21-24], ]25-28], I obtained shape=0.8276 and rate=0.1448. I would like to know how to build the continuous pdf of a this gamma distribution knowing these two estimated parameters such that I will be able to predict the pdf of any

INTRODUCTION TO THE PROBLEM I am trying to fit a distribution to a dataset. The distribution that I am currently considering is the (3-parameter) Singh-Maddala (Burr) distribution. The final model will fix the mean of the distribution to a given value and estimate the remaining parameters accordingly; however, the code provided below ignores this. For this distribution the three parameters

Dear Mr. Matthieu Stigler i so excited for your package 'tsDyn'. firstly introduce myself, i student at Gadjah Mada University,Indonesia. i'am new user of R and applying it for solving Bi-Variate ( interest rate and inflation ) with threshold vector error correction model. now, i writing my final examination about threshold vector error correction model and i use refference from paper