similar to: truncated negative binomial using rnegbin

ices.conf: <Public>1</Public> <!-- The name of you stream, not the name of the song! --> <Name>Poohba's Urban Sounds</Name> <!-- Genre of your stream, be it rock or pop or whatever --> <Genre>RnB</Genre> icecast.xml <directory> <yp-url-timeout>15</yp-url-timeout>

I want to generate 1,000 random samples of sample size=1,000 from ZINB. I know there is a rnegbin() to generate random samples from NB, and I know I can use the following process: do i=1 to 1000 n=0 do i=1 to 1000 if runi(1)>0.1 then x(i) = 0; else x(i)=rnegbin(); n=n+1; if n>1000 then stop; end; output; end; Anybody can help me out with the R code? Thanks very much ahead of time.

tabulate has .C("R_tabulate", as.integer(bin), as.integer(length(bin)), as.integer(nbins), ans = integer(nbins), PACKAGE="base")$ans The implementation of R_tabulate has if(x[i] != R_NaInt && x[i] > 0 && x[i] <= *nbin) and so copes with (silently drops) NA. Perhaps the .C could have NAOK=TRUE? This is useful in apply'ing tabulate to

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

Jack Moffitt <jack@xiph.org> writes: > Now I see the problem. This will not cause anything to show up. You > have no sources. Eh? -> sources Listing sources [Id: 509] [Sock: 14] [Time of connect: 18/Feb/2002:20:55:14] [IP: 205.147.43.110] [State: 1] [Type: pulling relay] [Proto: x-audiocast] [Clients: 2] [Dumpfile/fd: (null)/-1] [Priority: 0] [Song Title: ] [Song URL:

Hi R-Users! I fitted a negative binomial distribution to my count data using the function glm.nb() and obtained the calculated parameters theta (dispersion) and mu. I would like to simulate values from this negative binomial distribution. Looking at the function rnbinom() I was looking at the relationship between the two possible parametrizations of the negative binomial and found that for this

Dear R-users, I coded two equivalent ways to perform (in a simplified version) some matrix multiplications I would like to use in a more general framework. In the first case I used Kronecker product and vectorization of a certain matrix. This approach takes less time, but, as you may guess, I run out of memory when dimensions are large. In the second approach, I profited of sparseness and

I thought the YP code was going to be fixed to use less resources so it could be used with other stream formats. It seems to be taking a long time. As it is now, Icecast2 is not a good solution for those wanting to be on a popular directory. I am forced to recommend using Shoutcast to my clients so they can get onto a popular directory. I would prefer to recommend Icecast2. Ross Levis.

Dear All, I am trying to fit some data both as a negative binomial and a zero inflated binomial. For the first case, I have no particular problems, see the small snippet below library(MASS) #a basic R library set.seed(123) #to have reproducible results x4 <- rnegbin(500, mu = 5, theta = 4) #Now fit and check that we get the right parameters fd <- fitdistr(x4, "Negative

I can understand wanting to boost the adoption of Xiph codecs, but I think restricting the directory to vorbis streams only will not help this endeavour. I can imagine it will just piss a lot of people, including myself, and give Xiph a bad name. I get very few listeners of my AAC+ stream now since this occured. The restriction is not an insentive to switch to Ogg Vorbis at all. The incentive

I'm working with a large dataset - large enough that when I do a scatter plot the points all blur together, so I want to plot their density by color - a heat map or something like that. I've used smoothScatter for tasks like this, but the problem is that my current dataset really only looks good on a log-log scale. When I do the following command smoothScatter( data,

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

Hi Rob The stream format has nothing to do with your audio files. I probably have 70% MP3's and 30% Ogg Vorbis files, and 1 or 2 WAV files for jingles on my hard drive. I only encode new CD's to Ogg Vorbis. But my station is streaming in AAC+ format, and I have also streamed in Ogg Vorbis format simultaneously. There is no way I would bother streaming in MP3 format. It's 15+

Hello, I have found that in smoothScatter it is not possible to reverse the axes plotted (R version 2.9.0) .   It appears that this arises from the hard coding of xlim and ylim in smoothscatter.R in the lines : x <- x[ xlim[1] <= x[,1] & x[,1] <=xlim[2], ]   (line  number 25) and x <- x[ ylim[1] <= x[,2] & x[,2] <= ylim[2], ]  (line number 31) This results in a x

Hello, I have a few questions, regarding the smoothScatter function. I have a scatter plot with more than 500,000 data points for two samples. So, I am wanting to display the density in colors to convince people that my good correlation coefficient is not due to an "influential point effect" and plus, I also want to make my scatter plot look pretty. Anyway ... I have been able to

Dear R-Developers, please find attached a set of drafted functions for flat file import and export, partially extending existing functions, partially completely written as new code. I thought you might be interested in those functions and the accompanying utilities for padding and trimming. Main features are - supports several formats, i.e. fixed width and CSV (with one exception) - supports

Hi, I'm trying to setup some complicated binning with statistics and could use a little help. I've found the bin2 function from the ash package, but it doesn't do everything I need. My intention is to copy some of their code and then modify as needed. I have a vector of two columns: head(data) r1 r2 [1,] 0.03516559 0.03102128 [2,] 0.02162539 0.14847034

Hello, I'm using the eclat function of the arules package (1.0-6) for the identification of frequent itemsets. I need the tidLists, but if I set in the function tidLists=TRUE R crashes (Windows XP Professional SP3, 32 bit, R version 2.12.1 (2010-12-16), reproducible on two different computers) with two different error messages or non at all. Minimum examples are: library(arules)

Visual inspection of the plot of a density() function vs a normal with the same mean and variance suggests the area under the density curve is bigger than under the normal curve. The two curves are very close over most of the domain. Assuming the normal curve does integrate to 1, this implies the area under density() is > 1. Is there any assurance that the density kernel smoother produces

Hi, Is there a Zipf-like distribution RNG in R? Thanks, Weiguang