similar to: distributions and glm

Sorry if it is a silly question, I haven't found documentation on this and I don't know if it is possible. library(MASS) ## for fitdistr library(msm) ## for dtnorm #prepare truncated normal distribution dtnorm0 <- function(x, mean, sd , log = FALSE) { dtnorm(x, mean, sd, 105, 135, log) } set.seed(1) #Generate normal distribution with the TRUE population mean (day 106 of the

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:

Hi All, I have a sequence of positive integers, which is right skewed. The mean value is 6 and variance is 11. I suspect it can be fitted by poisson distribution. But I'm not familiar with the function to fit distribution. Could you please help me with it? Also, once I fit the poisson distribution, how can I check the good-ness of this fitting? Thank you! [[alternative HTML

Please guide me through to resolve the error message that I get this is what i have done. >x1<- rnorm(100,2,1) >x1fitbeta<-fitdistr(x1,"beta") Error in fitdistr(x1, "beta") : 'start' must be a named list Yes, I do understand that sometime for the distribution to converge to the given set of data, it requires initial parameters of the distribution, to

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"] <-

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 have found a problem with using the glm function with a gamma family. I have a vector of data, assumed to be generated by a gamma distribution. The parameters of this gamma distribution are estimated in two ways (i) using the glm() function, (ii) "by hand", using the optim() function. I find that the -2*likelihood at the maximum found by (i) is substantially larger than that

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

If my aim is to select a good subset of parameters for my final logit model built using glm(). What is the best way to cross-validate the results so that they are reliable? Let's say that I have a large dataset of 1000's of observations. I split this data into two groups, one that I use for training and another for validation. First I use the training set to build a model, and the the

Hello, I am currently writing a program whose goal is to fit usual distributions (estimating parameters and confidence intervals for a given distribution). After some research in R, R-help and google I have found most of what I was looking for (especially thanks to MASS - fitdistr() ), however there are still a few distributions I could not find R code for: Multinormal, Truncated normal,

Dear All, A simple question: packages like fitdistr should be ideal to analyze samples of data taken from a univariate distribution, but what if rather than the raw data of the observations you are given directly and only a histogram? I was thinking about generating artificially a set of data corresponding to the counts binned in the histogram, but this sounds too cumbersome. Another question is

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:

Hi, Suppose I have a vector of data. Is there a method in R to help us automatically suggest which distributions fits to that data (e.g. normal, gamma, multinomial etc) ? - Gundala Viswanath Jakarta - Indonesia

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

Hello, How can I do a test of two weibull distributions? I have two weibull distribution sets from two wind datasets in order to check whether they are same. I thought 2 sample t-test would be applicable but I couldn't find any ways to do that on the Internet. Does anyone know what type of test is applicable to my purpose? and what R function can you recommend? Plus, if it turned out that

Dear Sir, I am a new user of R and I am doing a tast, which is: find the maximum likelihood estimate of the parameter of Gaussian distribution for generated 100 numbers by using >x=rnorm(100, mean=3, sd=1). I tried to use following Maximum Likelihood function >fn<-function(x) (-50*log((sd(x))^2))-50*log(sqrt(2*pi))-(1/2*((mean(x))^2))*(sum((x-(mean(x))^2)), but it did not work. I am

Hi all, I'm using the function "fitdistr" (library MASS) to fit a distribution to given data. What I have to do further, is getting the log-Likelihood-Value from this estimation. Is there any simple possibility to realize it? Regards, Carsten

Dear all I've got the dataset data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 I know from other testing that it should be possible to fit the data with the exponentialdistribution. I tried to get parameterestimates for the exponentialdistribution with R, but as the values of the parameter

Hi just wondering if there is a package that can get the maximum likelihood or method of moments estimator for distributions with censored data? The distributions I'm interested in are: Exponential, pareto, beta, gamma and lognormal. -- View this message in context: http://www.nabble.com/MLE-for-censored-distributions-in-R-tp15022863p15022863.html Sent from the R help mailing list archive at

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: