similar to: Estimate two parameter for exponential dist

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,

Hi all, I am trying to fit a distribution to some data about survival times. I am interested only in a specific interval, e.g., while the data lies in the interval (0,...., 600), I want the best for the interval (0,..., 24). I have tried both fitdistr (MASS package) and fitdist (from the fitdistrplus package), but I could not get them working, e.g. fitdistr(left, "weibull", upper=24)

Hi there, I have fitted a sample (with size 20) to a normal and/or logistic distribution using fitdistr() in MASS or fitdist() in fitdistrplus package. It's easy to get the parameter estimates. Now, I hope to report the confidence interval for those parameter estimates. However, I don't find a function that could give the confidence interval in R. I hope to write a function, however,

Dear Charles, Please, when you have questions about fitdistrplus, contact directly the authors of the package and not R-help. When fitting non ? standard ? distributions with fitdistrplus, you should define by yourself the density and the cumulative distribution functions, or load a package which define them. See FAQ for a general example :

Dear Sirs, The below listed code fits a gamma and a pareto distribution to a data set danishuni. However the distributions are not appropriate to fit both tails of the data set hence a mixed distribution is required which has ben defined as "mixgampar" as shown below. library(fitdistrplus) x<- danishuni$Loss fgam<- fitdist(x,"gamma",lower=0) fpar<-

I would have thought this to be relatively elementary, but I can't find it mentioned in any of my stats texts. Please consider the following: library(fitdistrplus) fp = fitdist(y,"exp"); rate = fp$estimate; sd = fp$sd fOneWeek = exp(-rate*7); #fraction that happens within a week - y is measured in days fr = exp(-rate*dt); #fraction remaining - dt = elapsed time from

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 all, I'm currently working on the fitdistrplus package (that basically fit distributions). There is something I do not understand about the generic function summary. In the current version on CRAN, there is no NAMESPACE saying S3method(summary, fitdist) . However if we use summary on an object send by fitdist function it works fine... According to R-lang, we have " The most

Hi, I am trying to fit a Johnson SB distribution using fitdist function in fitdistrplus Library. I have defined the Johnson SB distribution from ( http://www.ntrand.com/johnson-sb-distribution/) . But it gives me the follwing errors. Any help would be appreciated #xi = xi #lambda =l #delta =d #gamma = g djohn = function(x,xi,l,d,g) (d/(l*sqrt(2*pi)*((x-xi)/l)*(1-((x-xi)/l))))*exp[-0.5*(g +

Dear useRs, We are pleased to announce you a new version of "fitdistrplus" on CRAN : https://cran.r-project.org/web/packages/fitdistrplus/index.html "fitdistrplus" is a package dedicated to help the fit of a parametric distribution to non-censored or censored data. The main new features in this release are few new topics in the FAQ vignette

Dear useRs, We are pleased to announce you a new version of "fitdistrplus" on CRAN : https://cran.r-project.org/web/packages/fitdistrplus/index.html "fitdistrplus" is a package dedicated to help the fit of a parametric distribution to non-censored or censored data. The main new features in this release are few new topics in the FAQ vignette

Capture the results of the apply command into an object and then work with that. Here is one way to do it: > res <- apply(C, 2, fitdist, "gamma") > out <- c( res$A$estimate["shape"], res$B$estimate["shape"], res$A$estimate["rate"], res$B$estimate["rate"]) > names(out) <- c("A shape","B shape","A

Dear R-helpers, I am trying to fit my data to a 4-parameter lognormal distribution (aka Johnson Sb dist) with fitdist function from the library(fitdistrplus). So far, I have learnt that with "mle" method it's not always possible to estimate the gamma and delta parameters even if the bounding estimates are "known"/"guessed". Therefore, I tried to fit it with the

Hi, Let say I have data by two columns A and B, and I have fit each column using the gamma distribution by 'fitdist' . I just want the result show only the shape and rate only. Eg: library(fitdistrplus) A <-c(1,2,3,4,5) B<-c(6,7,8,9,10) C <-cbind(A,B) apply(C, 2, fitdist, "gamma") Output show like this: $A Fitting of the distribution ' gamma ' by maximum

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

Hello, I have detailed (with pictures and whatnot) my question on my weblog at http://www.cs-ed.org/blogs/mjadud/archives/2005/05/a_question_abou.html The short version of the question is this: When I ask 'fitdistr' to try and fit my distribution as a "weibull" distribution, it comes up with some rather wacky parameters. If I take the same distribution, and do something

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

Given a numeric vector of observations, does R have any generic way to estimate the parameters of commonly used distributions (exponential, gamma, etc.) without numerically optimizing the likelihood function? Thanks, David _______________________________________ David R. Bickel http://davidbickel.com Research Scientist Pioneer Hi-Bred International Bioinformatics & Exploratory Research 7250

Hi all, I am attempting to fit, and test the goodness of fit of, a mixed exponential distribution to my dataset which consists of 15minute rainfall intensity data. FYI, the dataset spanning approx.2 years and 7 rainfall stations consists of some three hundred thousand 15min data records, of which some 30 thousand are non-zero rainfall amounts. Could anyone please tell me how i could do

Hello everyone, I was trying to do some distribution fitting with a numerical field called Tolls. The sample size = 999 rows. Basically I assigned the Toll data to a new variable K by doing: k<-dtest$Toll After that, tried to fit a gamma distribution by doing: fitG<-fitdist(k, "gamma") Then the following messages showed (oh and I checked for empty rows before doing this):