similar to: outout clarification of fitdist {fitdistrplus} output

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 Does anyone know if the following is supposed to work? ------------------------------------------------------ site.pp case $psi_rank { Devel: { import "Devel/psi_gfa/*" import "Devel/nodes" import "Devel/os/yum_update" } Prod: { import "Prod/psi_gfa/*" import "Prod/nodes" import

Hi Can anyone tell me what''s wrong with this one. Sorry I''m new and I just want to print some message to the the puppet log on the client side. --------------------------------------------------- $testvar = "Hello world!" class mytest { exec { "/bin/echo $testvar" } } I keep getting: err: Could not parse; using old configuration: Syntax error at

I'm running samba-3.0.9-1.3E.3 and the manpage for smb.conf does not mention anything about 'map to guest = Bad Uid'. However I did add the line 'map to guest = Bad Uid' an run 'testparm /etc/samba/smb.conf'. No complaines but 'testparm' removes the entire line 'map to guest = Bad Uid' from the config file. Hmmmm???

Hi; I would like to fit a contiuous right skewed dist. to a set of data. So, I want to use MLE or Robust estimation of the parameters, then use A-D or K-S test or Robust alternative for the goodness-of -fit. Also I would rank the fitted distributions according to the p-value of the test. Does anyone know how I do this in R. any backages of Robust estimation and for goodness-of-fit? Thank you

Hi, Interpretation problem ! so what i did is by using the: >fit1 <- fitdist(vectNorm,"beta") Warning messages: 1: In dbeta(x, shape1, shape2, log) : NaNs produced 2: In dbeta(x, shape1, shape2, log) : NaNs produced 3: In dbeta(x, shape1, shape2, log) : NaNs produced 4: In dbeta(x, shape1, shape2, log) : NaNs produced 5: In dbeta(x, shape1, shape2, log) : NaNs produced 6: In

================== platform ===================== Linux version: RHEL-3 resp. Scientific Linux 305 Samba vesrion: 3.0.9-1.3E.3 Samba operating mode: security = SERVER ================== problem ===================== 'net use n: \\smbs1\public' on Windows XP, always asks for a password. 'smbmount //smbs1/public /mnt/public -o password=' does not. This all used to work fine

https://bugzilla.mindrot.org/show_bug.cgi?id=2695 Bug ID: 2695 Summary: inconsistent outout of "ssh.add -l" between ed25519 and rsa keys Product: Portable OpenSSH Version: 7.3p1 Hardware: Other OS: Linux Status: NEW Severity: minor Priority: P5 Component:

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, 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

Dear All I have run the following GLM binominal model on a dataset composed by the following variables: TRAN_DURING_CAMP_FLG enviados bono_recibido 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1 benchmark 0 1

Hi I have two vectors, prob and size, and I want to add the random deviates of these two, i.e. sum( sapply( 1:length(prob), function(n){ rbinom(1, size(n), prob(n) } ) ) My problem is that I have to do this for a large number of value combinations. Is there a faster way of doing this? Rainer

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<-

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

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

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

Frede Aakmann T?gersen wrote: > Perhaps > > http://stinet.dtic.mil/cgi-bin/GetTRDoc?AD=ADA266969&Location=U2&doc=GetTRDoc.pdf > > is something that you can use? Thanks a lot - that might help. Rainer > > > > Best regards > > Frede Aakmann T?gersen > Scientist > > > UNIVERSITY OF AARHUS > Faculty of Agricultural Sciences > Dept.

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