similar to: three par. fitting with fitdistr

Hi all, I'm using the function "fitdistr" (library MASS) to fit a distribution to given data. What I have to do further, is getting the log-Likelihood-Value from this estimation. Is there any simple possibility to realize it? Regards, Carsten

Hello, maybe that my Question is a "beginner"-Question, but up to now, my research didn't bring any useful result. I'm trying to fit a distribution (e.g. lognormal) to a given set of data (ML-Estimation). I KNOW about my data that there is a truncation for all data below a well known threshold. Is there an R-solution for an ML-estimation for this kind of data-problem? As

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:

Hi R lovers! I'd like to know how to use the parameter 'start' in the function fitdistr() obviously I have to provide the initial value of the parameter to optimize except in the case of a certain set of given distribution Indeed according to the help file for fitdistr " For the following named distributions, reasonable starting values will be computed if `start'

Hello, I tried to fit a lognormal distribution by using optim. But sadly the output seems to be incorrect. Who can tell me where the "bug" is? test = rlnorm(100,5,3) logL = function(parm, x,...) -sum(log(dlnorm(x,parm,...))) start = list(meanlog=5, sdlog=3) optim(start,logL,x=test)$par Carsten. [[alternative HTML version deleted]]

--WWm7B+u2U4 Content-Type: text/plain; charset=us-ascii Content-Description: message body text Content-Transfer-Encoding: 7bit G'day all, I believe that this is related to PR#1717 (filed under not-reproducible) which was reported for a version of R that is a quite a bit older than the ones used in for this report. But I noticed this behaviour under R 2.1.1 and R 2.2.0 on my linux box and

Hello, I want to estimate the parameters of a binomial distributed rv using MLE. Other distributions will follow. The equation system to solve is not very complex, but I've never done such work in R and don't have any idea how to start... The system is: (1) n*P = X (2) [sum {from j=0 to J-1} Y{j} /(n-j)] = -n * ln (1-X / n) where * only X is given (empirical mean)

Hi R-users, I would like to do looping for this process below to estimate alpha beta from gamma distribution: Here are my data: day_data1 <- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1943 48.3 18.5 0.0 0.0 18.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 2.8 1944 0.0 0.0

Hello, I am using fitdistr function for parameter estimation. When I use fd<-fitdistr(V2,"gamma") I get following error: Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, : initial value in 'vmmin' is not finite fd<-fitdistr(V2,"weibull") Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, :

Hi, two questions - I think simple to solve for you ... (1) I've written a function containing some loops. Each loop will generate a few outputs. Finally I have to combine them to get something like a "spreadsheet" that my colleagues can import in EXCEL. Up to now I'm doing it as follows: With each loop-step I assign new values for each "column" of my desired output

Hello, we have a set of biological cell-size data, which are only available as frequencies of discrete size classes, because of the high effort of manual microscopic measurements. The lengths are approximately gamma distributed, however the shape of the distribution is relatively variable between different samples (maybe it's a mixture in reality). Is there any ML fitting (or

Hello, I've the following setting: (1) Data from a source without truncation (x) (2) Data from an other source with left-truncation at threshold u (xu) I have to fit a model on these these two sources, thereby I assume that both are "drawn" from the same distribution (eg lognormal). In a MLE I would sum the densities and maximize. The R-Function could be:

Hello, I'm trying to use fitdistr() from the MASS package to fit a gamma distribution to a set of data. The data set is too large (1167 values) to reproduce in an email, but the summary statistics are: Min. 1st Qu. Median Mean 3rd Qu. Max. 116.7 266.7 666.7 1348.0 1642.0 16720.0 The call I'm trying to make is: fitdistr(x,"gamma") and the error is: Error in optim(x =

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,

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

I have split my original dataframe to generate a list of dataframes each of which has 3 columns of factors and a 4th column of numeric data. I would like to use lapply to apply the fitdistr() function to only the 4th column (x$isi) of the dataframes in the list. Is there a way to do this or am I misusing lapply? As a second solution I tried splitting only the numeric data column to yield a

I have split my original dataframe to generate a list of dataframes each of which has 3 columns of factors and a 4th column of numeric data. I would like to use lapply to apply the fitdistr() function to only the 4th column (x$isi) of the dataframes in the list. Is there a way to do this or am I misusing lapply? As a second solution I tried splitting only the numeric data column to yield a list

Hi, I'm trying fitdistr but I'm getting some errors > fitdistr(rnorm(100),"Normal") Error in fitdistr(rnorm(100), "Normal") : 'start' must be a named list > fitdistr(rnorm(100),"Normal",start=list(mean=0,sd=1)) Error in fitdistr(rnorm(100), "Normal", start = list(mean = 0, sd = 1)) : supplying pars for the Normal is not

Dear R users and Dear authors of the distr package and sequels I am trying to use the (very nice) package distrMod as I want to implement maximum likelihood (ML) fit of some univariate data for which I have derived a theoretical continuous density (pdf). As it is a parametric density, I guess that I should implement myself a new distribution of class AbscontDistributions (as stated in the pdf

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