search for: nbinomial

Dear R-helpers, let us assume, that I have the following dataset: a <- rnbinom(200, 1, 0.5) b <- (1:200) c <- (30:229) d <- rep(c("q", "r", "s", "t"), rep(50,4)) data_frame <- data.frame(a,b,c,d) In a first step I run a glm.nb (full code is given at the end of this mail) and want to predict my response variable a. In a second step, I would

...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, type = "nbinomial", method = "MinChisq") and got back Warning messages: 1: In pnbinom(q, size, prob, lower.tail, log.p) : NaNs produced 2: In pnbinom(q, size, prob, lower.tail, log.p) : NaNs produced Again, I hope this helps. Sincerely, Jason Q. McClintic Aswad Gurjar wrote: > Hello, > &...

I have seen several queries about parameterizing the negative binomial scale parameter. This is called the heterogeneous negative binomial. I have written a function called "nbinomial" which is in the msme package on CRAN. Type ?nbinomial to see the help file. The default model is a negative binomial for which the dispersion parameter is directly related to mu, which is how Stata, SAS, SPSS, Limdep, and so forth parameterize the negative binomial. The direct parameter...

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.

...5L, 2L, 0L, 36L, 0L, 5L, 27L, 1L, 13L, 24L, 15L, 25L, 23L, 5L, 37L, 26L, 29L, 14L, 15L) La función Ord_plot me dice que la mejor distribución para los datos es binomial negativa. Ord_plot(peces) La función distplot parece indicar que la mejor distribución es poisson distplot(peces, type = "nbinomial") distplot(peces, type = "poisson") Pero no entiendo porque la discrepancia. Muchas gracias, Manuel -- *Manuel Spínola, Ph.D.* Instituto Internacional en Conservación y Manejo de Vida Silvestre Universidad Nacional Apartado 1350-3000 Heredia COSTA RICA mspinola@una.ac.cr mspino...

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 --------------------------------- [[alternative HTML version deleted]]

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 command does not work. Any help? Thank...

I'm trying to use gsDesign for a noninferiority trial with binary endpoint. Did anyone know how to specify the trial with different sample sizes for two treatment groups? Thanks in advance! [[alternative HTML version deleted]]

...power() of Hmisc, switching the roles of type-I and type-II errors, as cpower() assumes no treatment difference as the null hypothesis. Now, I would like to introduce event rate difference under the null-hypothesis, and calculate sample size accordingly. Any ideas? (similar functionality exists in nBinomial function of gsDesign for binomial tests) Thanks, Ehud

...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, type = "nbinomial", method = "MinChisq") and got back Warning messages: 1: In pnbinom(q, size, prob, lower.tail, log.p) : NaNs produced 2: In pnbinom(q, size, prob, lower.tail, log.p) : NaNs produced Again, I hope this helps. Sincerely, Jason Q. McClintic Aswad Gurjar wrote: > Hello, > &...

...itting a negative binomial model on this data set. The reader will find the dataset in a dumped format below for trials. A friend of mine tried the same with STATA and got the following result without any warning from STATA : . glm nbcas pop area v_100khab gares ports axe_routier lacs, family(nbinomial) link(log) eform Iteration 0: log likelihood = -616.84031 Iteration 1: log likelihood = -599.77767 Iteration 2: log likelihood = -597.22486 Iteration 3: log likelihood = -597.14784 Iteration 4: log likelihood = -597.14778 Iteration 5: log likelihood = -597.14778 Generalized li...