similar to: Goodness of fit test

Dear R users, I'm working in a brief R-tutorial to a group of students. To make that I'm using Sweave but I've got two problems: First, I want show how R operates with the matrix type but, I write in the .rnw document the code <<echo=T,results=tex>>= matriz <- matrix(vector,nrow=3,ncol=6) matriz @ and after compilating the LaTex document I obtain in the pdf the next

Hi, is there any method for goodness of fit testing of an (as general as possible) univariate distribution with parameters estimated, for normal, exponential, gamma distributions, say (e.g. the corrected p-values for the Kolmogorov-Smirnov or Chi-squared with corresponding ML estimation method)? It seems that neither ks.test nor chisq.test handle estimated parameters. I am aware of function

Thanks Petr. Comments below: At 03:40 PM 18/05/2007, Petr Klasterecky wrote: >Sean Connolly napsal(a): >>Hi all, >>I am wondering if anyone has written (or knows of) a function that >>will conduct a goodness-of-fit test for a gamma distribution. I am >>especially interested in test statistics have some asymptotic >>parametric distribution that is independent

Hi there, I'm a newbie, plesae bear with me. I have a dataset with about 10000 ~ 30000 data points. Would like fit to both Gamma and Normal distribution to see which one fits better. How do I do this in R? Or I could do a normality test of the data, if it's normal, I then will do a normal fit, otherwise, a gamma fit. But again, I don't know how to do this either. Please help! David

Hi all, I am wondering if anyone has written (or knows of) a function that will conduct a goodness-of-fit test for a gamma distribution. I am especially interested in test statistics have some asymptotic parametric distribution that is independent of sample size or values of fitted parameters (e.g., a chi-squared distribution with some fixed df), because I want to fit gamma distributions to

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

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

Hi all, I am attempting to fit, and test the goodness of fit of, a mixed exponential distribution to my dataset which consists of 15minute rainfall intensity data. FYI, the dataset spanning approx.2 years and 7 rainfall stations consists of some three hundred thousand 15min data records, of which some 30 thousand are non-zero rainfall amounts. Could anyone please tell me how i could do

I get John's value (48.8789) in 2.7.0 and R-devel (both on Ubuntu). Really seems to be a numeric issue: > HoltWinters(x, beta = 0, gamma = 0)$alpha alpha 48.87989 > HoltWinters(x * 1.0000000001, beta = 0, gamma = 0)$alpha alpha 0.6881547 > HoltWinters(x * 1.00000000001, beta = 0, gamma = 0)$alpha alpha 48.87989 Providing starting values seems to help, but not

Dear All, I have just estimated this model: ----------------------------------------------------------- Logistic Regression Model lrm(formula = Y ~ X16, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 82 LR chi2 5.58 R2 0.088 C 0.607 0

UseRs, Version 0.9-4 of actuar should be making its way to CRAN mirrors. The main highlights of this new version are speed enhancements for a few functions, support for phase-type distributions and functions for ruin theory. The relevant section of the NEWS file follows Version 0.9-4 ============= Maintenance and new features release. NEW FEATURES -- LOSS DISTRIBUTIONS o Functions

UseRs, Version 0.9-4 of actuar should be making its way to CRAN mirrors. The main highlights of this new version are speed enhancements for a few functions, support for phase-type distributions and functions for ruin theory. The relevant section of the NEWS file follows Version 0.9-4 ============= Maintenance and new features release. NEW FEATURES -- LOSS DISTRIBUTIONS o Functions

An update on this: I just patched HoltWinters() to use optimize() in the univariate case, and it now computes the correct value. David John Bodley wrote: > Hi, > > Thanks for the quick response. I upgraded by version of R on Windows to the > latest (2.7.0) and re-ran the analysis and get the same result of 48.87989. > > The original time series was a non-regular zoo()

Hello, I am new to R exploratory data analysis and plotting. Is anyone aware of a way to overlay a set of conditional histograms with conditional PDFs? Below, I generate a lattice plot of precipitation histograms based on different months and stations, given a subset of the dataset: histogram(~ data | month * station, data = sta.stack[sta.stack[,"type"]=="precip" &

Full_Name: John Bodley Version: 2.5.1 (2007-06-27) OS: Windows XP Submission from: (NULL) (12.144.182.66) I was fitting a number of time series in R using the stats::HoltWinters method to define a single exponential smoothing model, i.e., beta = gamma = 0. I came across an example where the fitted value of alpha was not defined in the [0, 1] interval which seems to violate the lower and upper

Greetings, We wanted to announce v. 1.4.0 of the R package 'KScorrect', which carries out the Lilliefors correction to the Kolmogorov-Smirnoff (K-S) test for use in (one-sample) goodness-of-fit tests. Aside from several minor changes, the biggest change is that the Monte Carlo algorithm now supports parallel implementation, using the platform-independent 'doParallel' and

Greetings, We wanted to announce v. 1.4.0 of the R package 'KScorrect', which carries out the Lilliefors correction to the Kolmogorov-Smirnoff (K-S) test for use in (one-sample) goodness-of-fit tests. Aside from several minor changes, the biggest change is that the Monte Carlo algorithm now supports parallel implementation, using the platform-independent 'doParallel' and

Hi, I need to analyze the influences of several factors on a variable that is a measure of fecundity, consisting of 73 observations ranging from 0 to 5. The variable is continuous and highly positive skewed, none of the typical transformations was able to normalize the data. Thus, I was thinking in analyzing these data using a generalized linear model where I can specify a distribution other than

Hello. I am not certain even how to search the archives for this particular question, so if there is an obvious answer, please smack me with a large halibut and send me to the URLs. I have been experimenting with fitting curves by using both maximum likelihood and maximum spacing estimation techniques. Originally, I have been writing distribution-specific functions in 'R' which work