similar to: test the goodness of it for negative binomial type 2

Dear All, I run the goodness of fit test using goodfit() in vcd package. The result is as follow: Goodness-of-fit test for poisson distribution X^2 df P(> X^2) Pearson 1.053348 2 0.5905661 Warning message: In summary.goodfit(gf) : Chi-squared approximation may be incorrect I want to save the the test statistics(X^2), df, and p-value. How can I save the result.

Hi All, I have a dataset consisting of abundance counts of a fish and I want to test if my data are poisson in distribution or normal. My first question is whether it is more appropriate to model my data according to a poisson distribution (if my test says it conforms) or use transformed data to normalise the data distribution? I have been using the vcd package gf<-goodfit(Y,type=

Hello, Sample code: library("vcd") dummy <- rnbinom(200, size=1.5, prob=0.8) gf <- goodfit(dummy, type="nbinomial", method="MinChisq") plot(gf) I would like to: 1. add a lgened stating the bars show the actual counts and the red dots - the fit. 2. show the goodness-of-fit values calculated somewhere on an empty white space ob the plot. But... the legend

Hello, I am a student and for project I need R. I have one problem regarding function goodfit(). Does goodfit() require frequency count of numbers or numbers themselves? For example suppose I have data say 150 readings.Do I need to use goodfit() directly on data or should I make suitable no of bins and then apply goodfit()? Aswad [[alternative HTML version deleted]]

Dear R-Help: As you can see, Prof. Friendly refers me to your site for an executable version of vcd. I don't mean to be obtuse, but 15 minutes spent exploring your site failed to locate a downloadable version of the vcd package to which he referred. I know plainly what this application can do. What I need to know is how to obtain the application itself. My thanks in advance for any

I have troubles understanding how goodfit() function in the vcd package computes the Pearson coefficient. Can anybody provide more information on the computation? In particular, for HorseKicks data in vcd package, goodfit() yields > oo <- goodfit(HorseKicks,type="poisson",method="MinChisq") > summary(oo) Goodness-of-fit test for poisson distribution

Try changing your method to "ML" and try again. I tried the run the first example from the documentation and it failed with the same error. Changing the estimation method to ML worked. @List: Can anyone else verify the error I got? I literally ran the following two lines interactively from the example for goodfit: dummy <- rnbinom(200, size = 1.5, prob = 0.8) gf <- goodfit(dummy,

Hey, Does anyone know if there is a way to get back from goodfit what it estimated the parameters to be? I used the code fit<-goodfit(round(data$PLX_NRX),type="nbinomial" and got a pretty good fit. I could not however duplicate this good fit with any parameter estimates that I had. Any ideas??? Thanks, Elizabeth Lawson ---------------------------------

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

Dear r list,   I am using glm.nb in the MASS package to fit negative binomial models to data on manta ray abundance, and AICctab in the bbmle package to compare model IC.  However, I need to test for the goodness of fit of the full model, and have not been able to find a Pearson's Chi Squared statistic in any of the output.  Am I missing it somewhere?  Is there a way to run the test using

I'm trying to enter a frequency table manually so that I can run a goodness of fit test (I only have the frequencies, I don't have the raw data). So for example, let's say I want to re-create the HorseKicks table: library(vcd) data(HorseKicks) str(HorseKicks) 'table' int [1:5(1d)] 109 65 22 3 1 - attr(*, "dimnames")=List of 1 ..$ nDeaths: chr [1:5]

Dear all, I am analyzing counts of seabirds made from line transects at sea. I have been fitting Poisson and negative binomial distributions to the data using the goodfit function from the vcd library. I would also like to evaluate how well a quasi-poisson distribution fits the data. However, none of the potentially suitable functions I have identified (goodfit(vcd), fitdistr(MASS),

Hi, this is probably a very newbie statistical question. I have some experimental data. My hypothesis is that the data are binomially distributed with a known N and p. I am trying to obtain a sort of p-value to tell me whether or not my hypothesis is correct. What can I do with R? Thank you, Pier Luca

I have a dataset which I want to model using a Poisson distribution, with a given parameter. I would like to know what is the proper way to do a ''goodness of fit'' test using R. I know the steps I''d take if I were to do it ''manually'': grouping the numbers into classes, calculating the expected frequencies using ''ppois'', then

Dear R-Users, How can I use chisq.test() as a goodness of fit test? Reading man-page I?ve some doubts that kind of test is available with this statement. Am I wrong? X2=sum((O-E)^2)/E) O=empirical frequencies E=expected freq. calculated with the model (such as normal distribution) See: http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm for X2 used as a goodness of fit test. Any

Dear R-list members, I have been a user of SPSS for a few years and quite new to R. I read the documentation and tried samples but I have some problems to obtain results for a logistic regression under R. The following SPSS script LOGISTIC REGRESSION vir /METHOD = FSTEP(LR) d007 d008 d009 d010 d011 d012 d013 d014 d015 d016 d017 d018 d069 d072 d073 /SAVE = PRED COOK SRESID

Good morning, in my work I need to study data distributions and so I need to fit the experimental distribution by theoretical curves such as normal, Poison, binomial and so on. I'd like to know, given a vector of data, for example x<-rnorm (1000, 10) if they follow a normal distribution. I'd like to do a fit (to estimate the parameters of the theoretical distribution) and then a

I haven't been an R lister for a bit, but I hope to enlist someone's help here. I think this is a simple question, so I hope the answer is not much trouble. Can you please respond directly to this email address in addition to the list (if responding to the list is warranted)? I have a histogram and I want to see if the data fit a Poisson distribution. How do I do this? It is

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Hi, I would like to see if a matching variable is an effect-modifier in a conditional logistic regression. Naturally, the matching variable can't enter directly in the model but as an interaction with terms that are in. However, I have problems in formulating the correct model the term that's already in the model is a factor. I am using treatment contrasts and the problem is that if I