similar to: apply and functions with many arguments

Hello all, Can someone tell me why the following mixture of two log-normal distributions does not get truncated? What puzzles me is that the function works almost always, but for certain combinations (like the one below), it does not. # R code example library(distr) mix<-UnivarMixingDistribution(Lnorm(3.2,0.5),Lnorm(5.4,0.6),mixCoeff=c(0.3,0.7))

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

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

Hello, The following piece of code works fine in R.2.10 (ubuntu): switch(distr,         normal    = {if (is.infinite(param["desv"]))                      n <- c(n,"La desv. estándar no puede ser Inf.")                    if (param["desv"]<0)                      n <- c(n,"La desv. estándar no puede ser <0.")                    },        

Hello everyone, I've been trying to adapt a switch emulator to emulate nouveau's compute. We've been told some things like indirect dispatch use the MME in Nouveau, however, looking at NVIDIA's open gpu documentation there's no MME in compute engine since Kepler. https://github.com/NVIDIA/open-gpu-doc/blob/master/classes/compute/clb1c0.h MME for compute should still exist

Dear all, I need to plot an cumulative distribution plot of a variable and then to fit a distribution to that, probably a weibull or lognormal. I have plotted the ecdf as > plot(ecdf(x)) but I haven't managed to fit the distribution. I have as well attached the data. I would appreciate if you could help me on that. Thank you. Kind regards Maria -------------- next part --------------

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes

The main patches are from Christoph. Unfortunately they're a little beyond my understanding of all the vertex-related details, but they generally seemed fine. I'm just going to push these unless someone steps up to review them. Christoph Bumiller (2): nvc0: add support for indirect drawing nvc0: fix translate path for PRIM_RESTART_WITH_DRAW_ARRAYS Ilia Mirkin (1): nouveau: check if

>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: >

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, Well, i need some help, practical and theoretical. I am wondering why the fitdistplus (mle function) is returning an error for this code: [code] x1 <- c(100,200,140,98,97,56,42,10,2,2,1,4,3,2,12,3,1,1,1,1,0,0); plotdist(x1); descdist(x1, boot =1000); y<- sum(x1); d= as.vector(length(x1)); for(i in 1:length(x1)){ d[i] = x1[i]/y; } fitdist(d, "beta") [/code] Error:

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

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

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 +

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, 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 userRs, "playing around" with combinations of replicate() and random number generating functions inside a self-defined "wrapper" function I encounterd a puzzling behaviour. The following are intentionally simple (and rather nonsense-) examples to isolate the relevant aspects. Please, note the seemingly "inconsistent" behaviour for the second call of

The data.frame is constructed by one of the following functions: funweek <- function(df) if (length(df$elapsed_time) > 5) { rv = fitdist(df$elapsed_time,"exp") rv$year = df$sale_year[1] rv$sample = df$sale_week[1] rv$granularity = "week" rv } funmonth <- function(df) if (length(df$elapsed_time) > 5) { rv =

Hi, we have some questions concerning the definition of new arithmetic methods. In our package "distr" (on CRAN) we define some new arithmetic methods for "+", "-", "*", "/". After loading "distr" the corresponding arithmetic methods work. Now, if we define a new class and also a new method for one of the arithmetic methods