similar to: Elementary question about computing confidence intervals.

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 :

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 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<-

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'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

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)

>From the documentation I have found, it seems that one of the functions from package plyr, or a combination of functions like split and lapply would allow me to have a really short R script to analyze all my data (I have reduced it to a couple hundred thousand records with about half a dozen records. I get the same result from ddply and split/lapply: >

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

Hello, I like to fit data against a negative binominal distribution x2<-c(rep(10,14),rep(9,8),rep(8,13),rep(7,11),rep(6,6),rep(5,18),rep(4,7),re p(3,21),rep(2,33),rep(1,55),rep(0,225)) f2<-fitdist(x2,"nbinom",method="mle") plot(f2) summary(f2) gofstat(f2) I receive the following result: Fitting of the distribution ' nbinom ' by maximum

I'm trying to estimate two parameter for a markov chain (exponential). I used maximum likelihood to do it. (fitdistr{MASS},fitdist{fitdistrplus}), but I get just one parameter. Do you have any suggestion to help me? THANKS!! -- View this message in context: http://r.789695.n4.nabble.com/Estimate-two-parameter-for-exponential-dist-tp4648917.html Sent from the R help mailing list archive at

Dear R Community, I am pleased to announce the beta-release of the prob package. The source code is now on CRAN, and binaries should be generated there before long. In the meantime, you can get it with install.packages("prob", repos = "http://r-forge.r-project.org") The prob package gives a framework for doing elementary probability on finite sample spaces in R. The

Dear R Community, I am pleased to announce the beta-release of the prob package. The source code is now on CRAN, and binaries should be generated there before long. In the meantime, you can get it with install.packages("prob", repos = "http://r-forge.r-project.org") The prob package gives a framework for doing elementary probability on finite sample spaces in R. The

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

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