similar to: scale estimation for Gamma distribution

Hi All, could someone please shed some light on the proper goodness-of-fit analysis for the GLM output based on Gamma distributions with the log-link? My objective is to test the goodness-of-fit for the final model (and not the comparison of nested models). In particular, should the 'Residual Deviance' be compared with the Chi-Square distribution, or should the 'Scaled Residual

I would like the option to specify alternative scale parameters when using the gamma family, log link glm. In particular I would like the option to specify any of the following: 1. maximum likelihood estimate 2. moment estimator/Pearson's 3. total deviance estimator Is this easy? Possible? In addition, I would like to know what estimation process (maximum likelihood?) R is using to

Hi All, could someone shed some light on what the difference between the estimated dispersion parameter that is supplied with the GLM function and the one that the 'gamma.dispersion( )' function in the MASS library gives? And is there consensus for which estimated value to use? It seems that the dispersion parameter that comes with the summary command for a GLM with a Gamma dist. is

Der r helpers, I have a following set of data. Though some other software, I know it follows 3 parameter Gamma distrinution. I need to write a R code for estimating the parameters of 3 Parameter Gamma distribution. c(0,0,18561.9,0,0,0,34400,0,0,0,0,2190,0,0,0,0,60000,0,0,0, ? 19583,0,0,0,109872.87,0,0,0,0,0,0,1244,0,0,25150,0,500,0,0,0, ?

Hey, I have a set of income data which I'd like to fit to a gamma distribution. How can I estimate the two parameters of the gamma distribution for a vector, e.g. c(2039L, 2088L, 5966L, 2353L, 1966L, 2312L, 3305L, 2013L, 3376L, 3363L, 3567L, 4798L, 2032L, 1699L, 3001L, 2329L, 3944L, 2568L, 1699L, 4545L) I sense this will be a very easy one-liner, but my searching didn't come up with

Please how can one go about this one? I don't know how to go about it. [[alternative HTML version deleted]]

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,

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

Hi, There is a lot of speex quality estimations. One of this comparative estimation is even available on the official site <http://speex.org/comparison/>. I'd like to present yet another one. And I thought that the best place for this presentation would be Speex-dev mailing list. I want to get feedbacks and criticisms please. If Speex authors consider to make some parts of this work

i have a time Series of IBM closing px from 1/1/2000 to today I want to graph the time serie by dividing the graph by year and month all the monthly graphs with the same year will go to one page. so from 1/1/2000 to 11/19/2010. i will have 11 pages, and each page will have 12 graphs (jan to dec) except for 2010. I am able to do it in R, but when i use sweave, I can only print the last page.

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

Dear all, Is there any function for calculating confidence limits for coefficients in an lm() object? I know of the confint() function in the MASS library working very well on my binomial GLMs and I have tried it (using glm () , family=gaussian) but it gives NAs according to below. Does the confint() function not accept gaussian GLMs? Could there be convergence problems in the GLM? Note the

Hello, I''m struggling with this stuff and can''t find an answer so please advise. What I''m trying to achieve @collection has 7 elements I want to: - cycle through all those elements - assign element["smth"] through element["smth3"] to @data.set1_smth through @data.set1_smth3 - the last iteration would be assigning element["smth"] to

Dear R users, Please help me on using "mix" function under package "mixdist". My data distribution shows there are two components for the mixture distribution: left part is an exponential and right part is a normal. So I plan to use "gamma" mixture distribution to estimate the parameters. Here is what I am using for the "mix" function. Test<-mix(x,

How correctly remove function from module? For example: int f1(int x) { ... a = f2(smth); ... } int f2 (int y) { ... b = f1(smth); ... } I need delete from module both f1 and f2. They haven't uses in other part of module, but I can't delete them with eraseFromParent, because they are use each other. Yours sincerely, Kadysev Mikhail -------------- next part

Михаил wrote: > How correctly remove function from module? > For example: > > int f1(int x) { > ... > a = f2(smth); > ... > } > int f2 (int y) { > ... > b = f1(smth); > ... > } > > I need delete from module both f1 and f2. They haven't uses in other > part of module, but I can't delete them with eraseFromParent, because > they are use each

Hello, I try to find a function for M-estimation in multivariate linear regression model (function that can estimate betas in my model: y=x * beta + e, where y is a matrix). I´ve searched R-site for a long time, but I am hopeless. I would like to ask, if there is any function for M-estimation in multivariate linear regression model in R. I know I can estimate betas in my model by rlm() function

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

Михаил wrote: > Thanks, but I replaceAllUsesWith() - works well, but I still get bug in > eraseFromParent(): > > While deleting: i32 (%class.B*, i32)* %_ZN1B1xEi > An asserting value handle still pointed to this value! > UNREACHABLE executed at /Users/neonomaly/LLVM/LLVM/lib/VMCore/Value.cpp:561! The replaceAllUsesWith + eraseFromParent pattern remains correct, but there's

Dear all:Request your indulgence. The econophysics gurus do this stuff all the time: all their PDFs are smooth, with neat log x axis. 1. The kernel density estimate (KDE) function returns the empirical probability density at 2^n points (min: 512). The big question is how do I scale back the x-values (say, density$x) to x-values in terms of the original dataset? 2. To give you a concrete idea, i