similar to: gamma regression & zeros

Dear R Users, I am trying to obtain a best-fit analytic distribution for a dataset with 11535459 entries. The data range in value from 1 to 300000000. I use: fitdistr(data, "gamma") to obtain mle's for the parameters. I get the following error: Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : non-finite finite-difference value [1] And the following warnings:

Hello everyone, With th bar histogram (number of occurrences) hist<-c(24,7,4,1,2,1,1) of seven equally spaces classes ]1-4], ]5-8], ]9-12], ]13-16], ]17-20], ]21-24], ]25-28], I obtained shape=0.8276 and rate=0.1448. I would like to know how to build the continuous pdf of a this gamma distribution knowing these two estimated parameters such that I will be able to predict the pdf of any

Greetings, all I am having difficulty getting the fitdistr() function to return without an error on my data. Specifically, what I'm trying to do is get a parameter estimation for fracture intensity data in a well / borehole. Lower bound is 0 (no fractures in the selected data interval), and upper bound is ~ 10 - 50, depending on what scale you are conducting the analysis on. I read in the

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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 would like to fit a gamma pdf to my data using the method of RSS (Residual Sum-of-Squares). Here are the data: x <- c(86, 90, 94, 98, 102, 106, 110, 114, 118, 122, 126, 130, 134, 138, 142, 146, 150, 154, 158, 162, 166, 170, 174) y <- c(2, 5, 10, 17, 26, 60, 94, 128, 137, 128, 77, 68, 65, 60, 51, 26, 17, 9, 5, 2, 3, 7, 3) I have typed the following code, using nls method:

Dear R users, ?rgamma gives me : rgamma(n, shape, rate = 1, scale = 1/rate) rate: an alternative way to specify the scale. The Gamma distribution with parameters ‘shape’ = a and ‘scale’ = s has density f(x)= 1/(s^a Gamma(a)) x^(a-1) e^-(x/s) Should I understand that scale=1/rate ? Is it written somewhere ? Then

Hello all, I was wondering if someone can enlighten me as to the difference between the logLik in R vis-a-vis Stata for a GLM model with the gamma family. Stata calculates the loglikelihood of the model as (in R notation) some equivalent function of -1/scale * sum(Y/mu+log(mu)+(scale-1)*log(Y)+log(scale)+scale*lgamma(1/scale)) where scale (or dispersion) = 1, Y = the response variable, and mu

Good afternoon/morning readers. This is the first time I am trying to run some Bayesian computation in R, and am experiencing a few problems. I am working on a Poisson model for cancer rates which has a conjugate Gamma prior. 1) The first question is precisely how I work out the parameters. #Suppose I assign values to theta with *seq()* *theta<-seq(0,1,len=500)* #Then I try out the

Hello, we have a set of biological cell-size data, which are only available as frequencies of discrete size classes, because of the high effort of manual microscopic measurements. The lengths are approximately gamma distributed, however the shape of the distribution is relatively variable between different samples (maybe it's a mixture in reality). Is there any ML fitting (or

For a real data column X value ranged between (56.4521,32317.9) with missing values, I need to overlay 2 plots: histogram & fitted gamma density. I use following to generate histogram. xbk_seq(50,33000,by=100) hist(x,breaks=xbk) But I don't know how to get "fitted gamma density"? In SAS proc capability, I got Shape=2.59, Scale=3481). But when I do plot(dgamma(x,

Dear all, How can I compute the log likelihood of a gamma distributions of a vector. I tried the following. But it doesn't seem to work: samples<-c(6.1, 2.2, 14.9, 9.9, 24.6, 13.2) llgm <- dgamma(samples, scale=1, shape=2, log = TRUE) It gives [1] -4.291711 -1.411543 -12.198639 -7.607465 -21.397254 -10.619783 I expect it only returns "one" value instead of vector.

Hello, I'm trying to compare the fit of two distributions, normal and gamma, to a histogram of my response variable. rate<-mean(na.omit(rwb$post.f.crwn.length))/var(na.omit(rwb$post.f.crwn.length)) shape<-rate*mean(na.omit(rwb$post.f.crwn.length)) hist((rwb$post.f.crwn.length), main="rwb$post.f.crwn.length")

Hello, I'm having some difficulty understanding the useage of the "dropterm()" function in the MASS library. What exactly does it do? I'm very new to R, so any pointers would be very helpful. I've read many definitions of what dropterm() does, but none seem to stick in my mind or click with me. I've coded everything fine for an interaction that runs as follows: two sets

Dear R experts, How should lambda and gamma (with std.errors) be calculated for a weibull model with age as an independent predictor? I have assumed that this can be done with survreg with e. g. (summary(survreg(Surv(time, status) ~ age, dist = 'weibull')) ) and predict.survreg with e.g. (predict(model, se.fit = T, newdata = data.frame(age = seq(50, 80, 5)) but unfortunately I'm

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):

for learning purposes and also to help someone, i used roger peng's document to get the mle's of the gamma where the gamma is defined as f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) * exp(-scale*y_i) ( i'm defining the scale as lambda rather than 1/lambda. various books define it differently ). i found the likelihood to be n*shape*log(scale) +

Hi, All, I am getting some funny results trying to use R's built in distribution functions. In R: > dgamma(4.775972,1.37697964405418, 0.106516604930466) [1] 0.05585295 > dgamma(4.775972,1.37697964405418, 0.106516604930466,TRUE) ### THIS IS JUST WRONG! [1] 0.01710129 > log(dgamma(4.775972,1.37697964405418, 0.106516604930466)) [1] -2.885033 > In C:

Hi All, Here 's what I got using dgamma function : > nu<-.2 > nu*log(nu)-log(gamma(nu))+(nu-1)*log(1)-nu*(1) [1] -2.045951 > dgamma(1,nu,nu,1) [1] 0.0801333 > dgamma(1,nu,nu,0) [1] NaN Warning message: NaNs produced in: dgamma(x, shape, scale, log) Could anyone tell me what is wrong here ? I am using R-2.4.1 on windows XP. Thanks a lot.

I define priors in jags within r using a gamma distribution. I would like to control the shape but I have problems. Any help will be usefull. From help of dgamma ___________________ The Gamma distribution with parameters shape = a and scale = s has density f(x)= 1/(s^a Gamma(a)) x^(a-1) e^-(x/s) and rate=1/scale From jags user manual ____________________ dgamma(r, mu) has a density of