similar to: Heterogeneous negative binomial

R-listers: I have searched the help files and everything I have related to R graphics. I cannot find how to graph y against several distributions on a single graph. Here is code for creating 4 Poisson distributions with different mean values, although I would prefer having it in a loop: The top of the y axis for the first distribution, with count of 0, is .6, which is the highest point for

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,

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

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 ---------------------------------

Estimados miembros de la lista. Estoy usando 2 funciones del paquete vcd y obtengo resultados que parecen discrepar entre ambas funciones. > dput(peces) c(26L, 63L, 1L, 21L, 20L, 50L, 0L, 104L, 19L, 0L, 8L, 32L, 0L, 24L, 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

Finding and not unnecessarily duplicating existing functionality is important also from a user perspective. Negative binomial regression provides a somewhat extreme example of existing overlap between packages, with the scope that this creates for confusing users, especially as the notation is not consistent between these different implementations. In addition to MASS::glm.nb(), note

Hola, Estoy tratando de calcular el poder estadistico de un GLM en R, programas estadisticos como SPSS lo puede calcular pero no eh encotrado nada sobre R. Alguien tiene alguna idea o sabe de literatura donde pueda encontrar ayuda? Luego tengo otra pregunta mas bien teorica sobre la funcion offset utilizada en negative poisson, por lo que eh entendido esta funcion posibilita trabajar con

Hi Group, I'm looking for a method to calculate sample size for a non-inferiority survival analysis. Initially I've used cpower() 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?

Dear all, I am using the book "Generalized Linera Models and Extension" by Hardin and Hilbe (second edition, 2007) at the moment. The authors suggest that instead of OLS models, "the log link is generally used for response data that take only positive values on the continuous scale". Of course they also suggest residual plots to check whether a "normal" linera model

I am trying to find out the log-likelihood function of the negative multinomial. I would like to program a regression model for this distribution for a book I am writing. Any help I can obtain will be most appreciated. Joseph Hilbe [[alternative HTML version deleted]]

Readers of this mailing list may be interested to know that the book "A Beginner's Guide to Generalized Additive Models with R' is now available from: http://www.highstat.com/BGGAM.htm Upcoming books in 2013: A Beginner's Guide to GLM with R and JAGS. AF Zuur, J Hilbe, EN Ieno A Beginner's Guide to GAMM with R. AF Zuur, AA Saveliev, EN Ieno Kind regards, Alain Zuur

Members of this mailing list may be interested in the following new book: Beginner's Guide to GLM and GLMM with R. - A frequentist and Bayesian perspective for ecologists - Zuur AF, Hilbe JM and Ieno EN This book is only available from: http://www.highstat.com/BGGLM.htm This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both

-----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of r-help-request at r-project.org Sent: Monday, February 11, 2008 12:00 PM To: r-help at r-project.org Subject: R-help Digest, Vol 60, Issue 11 Send R-help mailing list submissions to r-help at r-project.org To subscribe or unsubscribe via the World Wide Web, visit

As a fourth option, I wonder if the first author could fork the package? Presumably, appropriately cited, a fork is permitted by the license under which it was released. Then the original package, by both authors, still exists (and a final version could point to the new one) and the new package, citing the previous version appropriately, is by a single author. The page of CRAN's policies

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

Estimado Matías Ledesma Leí en la lista lo que usted desea realizar, en la misma expresa: La idea del estudio es mostrar un valor medio con un intervalo de confianza por estación de la frecuencia de malformaciones por individuo. Creo que se expreso en forma incorrecta, yo no se cuál es su estudio, yo no soy estadístico, soy veterinario, pero lo que usted expresa en su idea, aunque en

From: matutetote@hotmail.com To: javier.ruben.marcuzzi@gmail.com CC: r-help-es@r-project.org Subject: RE: [R-es] calculo de poder estadistico en glm Date: Wed, 12 Jun 2013 07:24:13 +0000 Estimado Javier, Gracias por tus comentarios. Si, puede ser que me haya expresado en forma incorrecta. Mi idea fue tratar de ser lo mas conciso posible y limitarme al problema estadistico que tengo, por lo

Estimado Matias, Reenvio a la lista. Es tarde aqui y hay que descansar. Quizas alguien en otra parte del mundo pueda darte una mano. En unas horas te envio mi opinion. Saludos, Jorge.- 2013/6/11 Matias Ledesma <> > Hola Jorge, > > Muchas gracias por las referencias, ya consegui el libro asi que me voy > aponer a leerlo aver si encuentro una solucion. > > La data

Buenas tardes, ¿Cómo interpretarías el intercepto que da R en un modelo de ceros inflados? Por un lado en la parte de conteo tengo un intercepto de -4.2 y en la parte de ceros de 102, ambos salen significativos (***). ¿Qué me dirían? Gracias

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.