similar to: About poisson distribution fitting and testing

Hello! I would like to get MLE for parameter lambda of Poisson distribution. I can use fitdistr() for this. After looking a bit into the code of this function I can see that value for lambda and its standard error is estimated via estimate <- mean(x) sds <- sqrt(estimate/n) Is this MLE? With my poor math/stat knowledge I thought that MLE for Poisson parameter is (in mixture of LaTeX

it is easy to fit a distribution using fitdistr poisdata <- rpois(n = 100, lambda = 2) poismle <- fitdistr(poisdata, "Poisson") poismle but i would like to know whether its possible to get an identical result using glm. I use poistab <- data.frame(table(poisdata)) colnames(poistab) <- c("width","freq"); poistab[,"width"] <-

I am using the MASS library function fitdistr(x, dpois, list(lambda=2)) but I get Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : Function cannot be evaluated at initial parameters In addition: There were 50 or more warnings (use warnings() to see the first 50) and all the first 50 warnings say 1: non-integer x = 1.452222 etc Can anyone tell me what I am doing

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),

Hello, I am a new user of R and found the function dtnorm() in the package msm. My problem now is, that it is not possible for me to get the mean and sd out of a sample when I want a left-truncated normal distribution starting at "0". fitdistr(x,dtnorm, start=list(mean=0, sd=1)) returns the error message "Fehler in "[<-"(`*tmp*`, x >= lower & x <= upper,

Dear all, I have wrote some sample code that would allow me easier fit fast many distributions and check which of the fits performs better. My sample code (that you can of course execute it looks like that) distrList<-list(   "exponential",  "geometric", "log-normal",  "normal", "Poisson") fitfunction<-function(Type,x){     return

Hi folks! I run the following code to get a CI for a Poisson with lambda=12.73 library(MASS) set.seed(125) x <- rpois(100,12.73) lambda_hat<-fitdistr(x, dpois, list(lambda=12))$estimate #Confidence Intervals - Normal Approx. alpha<-c(.05,.025,.01) for(n in 1:length(alpha)) { LowerCI<-mean(x)-(qnorm(1-alpha[n]/2, mean = 0, sd = 1)*sqrt(var(x)/length(x)))

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

[code]library(MASS) x=c(rep(0,8096), rep(1,1629), rep(2,233), rep(3,38), rep(4,4) ) x.bar=round(mean(x),4) x.var=round(var(x),4) p.hat=round(x.bar/x.var,4) alpha.hat=round(x.bar*p.hat/(1-p.hat),4) fitdistr(x, "Negative Binomial") fitdistr(x, "Poisson")[/code] 1- fitdistr(x, "Negative Binomial") the parameters got here,

Forum, I'm a grad student in Civil Eng, took some Stats classes that required students learn R, and I have since taken to R and use it for as much as I can. Back in my lab/office, many of my fellow grad students still use proprietary software at the behest of advisers who are familiar with the recommended software (Statistica, @Risk (Excel Add-on), etc). I have spent a lot of time learning

I am new to R. So, please bear with me if this questions is already been asked and answered. I am looking for a R function that fit a dataset to common distributions such as Normla, Log-Normal, Poisson, Weibull, and Beta. Basically I am looking for ways to estimate distribution parameters rather than having to write a program for it. Thanks for your help. T. S. Ramanarayanan Aventis

I need to estimate the parameters for negative binomial distribution (pdf) using maximun likelihood, I also need to estimate the parameter for the Poisson by ML, which can be done by hand, but later I need to conduct a likelihood ratio test between these two distributions and I don't know how to start! I'm not an expert programmer in R. Please help -- View this message in context:

Hello all, I would like to compare two sets of count data which form Poisson distributions. I'd like to generate some sort of p-value of the likely-hood that the distributions are the same. Thanks in advance for your advice. Cheers, Mark Mark Gosink, Ph.D. Head of Computational Biology Scripps Florida 5353 Parkside Drive - RFA Jupiter, FL 33458 tel: 561-799-8921 fax:

Hello, I tried to fit a poisson distribution but looking at the function fitdistr() it does not optimize lambda but simply estimates the mean of the data and returns it as lambda. I'm a bit confused because I was expecting an optimization of this parameter to gain a good fit... If I would use mle() of stats4 package or mle2() of bbmle package, I would have to write the function by myself

I'm looking for goodness of fit tests for gamma distributions with large data sizes and for different data. I have a matrix with around 4.000 data values in it and i have fitted a gamma distribution with "fitdistr". You can see the example: > fitdistr(corpo,"gamma",lower=0.001) Errore in optim(x = c(5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, :

Hi there, In the description of command "pam", it mentions "For datasets larger than (say) 200 observations". Now my dataset is a "54732 by 5" dataframe named "test". When I try to run pam(test,4),it shows " cannot allocate vector of length 1497768547". Is it because the row too big that it can't handle? Thank you!

Hi there, Sorry for the double email. Does R have the packages for the following clustering methods? And if it does, what the commands for them? 1. SOM (Self-organization map) 2. Graph partitioning: 3. Neural network 4. Probability Binning Thank you very much! Linda [[alternative HTML version deleted]]

Dear List Members, I need a bit help about fitting some theoretical distributions (such as geometric, exponential, lognormal or weibull distribution) to the following *dry spell*, *wet spell*, *cycles (Wet-Dry or Dry-Wet)* from my meteorological (daily rainfall) data http://www.angelfire.com/ab5/get5/R.rainfall.txt only for rainy seasen (july - september) of 14 years only:

Hi, I am trying to fi a glm-poisson model to 400.000 records. I have tried biglm and glmulti but i have problems... can it really be the case that 400.000 are too many records??? I am thinking of using random samples of my dataset..... Many thanks, -- View this message in context: http://r.789695.n4.nabble.com/glm-poisson-fitting-400-000-records-tp3925100p3925100.html Sent from the R help

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,