similar to: Announcing fitdistrplus 1.0-8

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 :

We are pleased to announce your a new version of fitdistrplus ( https://cran.r-project.org/package=fitdistrplus). Among the new features of the package (https://cran.r-project.org/web/packages/fitdistrplus/NEWS), a FAQ vignette is now available ( https://cran.r-project.org/web/packages/fitdistrplus/vignettes/FAQ.html). We will be delighted to update it with new questions sent by users. Do not

We are pleased to announce your a new version of fitdistrplus ( https://cran.r-project.org/package=fitdistrplus). Among the new features of the package (https://cran.r-project.org/web/packages/fitdistrplus/NEWS), a FAQ vignette is now available ( https://cran.r-project.org/web/packages/fitdistrplus/vignettes/FAQ.html). We will be delighted to update it with new questions sent by users. Do not

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<-

Why not define your own functions based on d? e.g. myCumDist <- function(x) { integrate(d, lower=-Inf, upper=x)$value } myQuantile <- function(x) { uniroot(f=function(y) { h(y) - x }, interval=c(-5,5)) } # limits -5,5 should be replaced by your own which might require some fiddling e.g. d <- function(x) { exp(-x^2/2)/(sqrt(2*pi)) } # just an example for you to test with; use your own

Dear All, Apologies for not providing a reproducible example, but if I could, then I would be able to answer myself my question. Essentially, I am trying to fit a very complicated custom probability distribution to some data. Fitdistrplus does in principle everything which I need, but if require me to specify not only the density function d, but also the cumulative p and and inverse cumulative

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

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,

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 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 All, I apologise if this question has been answered before, but my background is a little different from most people using R, and the language we use seems to be different! I am trying to analyse some nuclear physics data, which consists of an ensemble of "energy" readings in a detector that, when binned, form a number of Gaussian shaped peaks superimposed on a varying background

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 all, I have been trying to fit my data (only right censored) with gumbel distribution using fitdistrplus. I am getting very high standard error. I have been wondering why. The followings are the outputs: fit1=fitdistcens(dr0, "gumbel", start=list(a=99, b=0.6), optim.method= "L-BFGS-B", lower = 0.0, upper = Inf) > summary(fit1) FITTING OF THE DISTRIBUTION ' gumbel

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

Hello, I would like to fit a mixture of two Weibull distributions to my data, estimate the model parameters, and compare the fit of the model to that of a single Weibull distribution. I have used the mix() function in the 'mixdist' package to fit the mixed distribution, and have got the parameter estimates, however, I have not been able to get the log-likelihood for the fit of this model

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

In glm() you can use the summary() function to recover the shape parameter (the reciprocal of the dispersion parameter). How do you recover the scale parameter? Also, in the given example, how I estimate and save the geometric mean of the predicted values? For a simple model you can use fitted() or predicted() functions. I will appreciate any help. ? ? ? #Call required R packages require(plyr)?

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

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