similar to: Negative binomial regression for count 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

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

Dear Madam/Sir: I downloaded the latest source file (version 3.0.1) and follow = "Manifest" guide to build the binaries, but got failure. OS: Red Hat Linux release 7.3 (Valhalla) Kernel: 2.4.18-3 on an i686 ***** Procedures I did to build: [max@localhost source]$ ls -l configure -rwxr-xr-x 1 max max 973150 Feb 6 10:30 configure [max@localhost source]$ ./configure :

--- On Mon, 3/12/12, aleksandr shfets <a_shfets at mail.ru> wrote: > From: aleksandr shfets <a_shfets at mail.ru> > Subject: Fwd: Re[2]: [R] B-spline/smooth.basis derivative matrices > To: "Vassily Shvets" <shv736 at yahoo.com> > Received: Monday, March 12, 2012, 5:15 PM > > > > -------- ???????????? ????????? > -------- > ?? ????:

On 21 July 2010 22:14, Reza Yazdani <ry.yazdani at gmail.com> wrote: > Hello, > > I down loaded "llvm-gcc4.2-2.7-x86_64-linux" and I built Spec2006 with it. > It is great and except for one benchmark the rest work fine. I want to build > them with LTO now. I followed the directions in > "http://llvm.org/docs/GoldPlugin.html" and built ar, nm-new, and

Thanks for that email. The one lihe change I made is this : from #define FLAC__MAX_RICE_PARTITION_ORDER (15u) to #define FLAC__MAX_RICE_PARTITION_ORDER (6u) and that seemed to make decoder_new() happy, but it's promptly crashing after making a call to the read callback (below), then to the meta callback. The meta callback did nothing but print a string and return. I removed it, and

--- Reza Naima <reza@reza.net> wrote: > I've cross-compiled flac for the armv4l processor (rio receiver), and > > i'm trying to start up a decode thread : > > #include <FLAC/stream_decoder.h> > .... > FLAC__StreamDecoder *flac = NULL; > flac = FLAC__stream_decoder_new(); > if (flac == NULL) { >

433.milc, 447.deall, and 450.soplex are run as part of the nightly tests on Darwin (=MacOSX) in a way that closely approximates -O4. They are working there. I am inclined to suspect gold given that they work at -O3. Can a Linux person comment? On Jul 23, 2010, at 10:19 AMPDT, Reza Yazdani wrote: > Following is the list of fp benchmarks that fail. They all pass with > -O3, but

I changed just the FLAC__EntropyCodingMethod_PartitionedRice struct, but it hasn't changed a thing. I'm still getting the same behaviour. typedef struct { unsigned order; unsigned parameters[1 << 6]; unsigned raw_bits[1 << 6]; } FLAC__EntropyCodingMethod_PartitionedRice; Any other thoughts? Thanks, Reza Josh Coalson wrote: > --- Reza Naima

I appreciate to Roger Koenker, Achim Zeileis and Vito Muggeo for their informative answers. Listed below is unedited replies I got followed by the question I posted. Kyong 1. Roger Koenker: You might try rqss() in the quantreg package. It gives piecewise linear fits for a nonparametric form of median regression using total variation of the derivative of the fitted function as a penalty

The wiki page settings control a netlogon security setting. I guess the "server signing" parameter applies to all the other traffic. But my guess - and it is only a guess- is that requiring server signing is causing the netlogon process to break since it does not support signing. The e-mail chain you reference does not make it clear if the samba instance is a domain

Hello, I down loaded "llvm-gcc4.2-2.7-x86_64-linux" and I built Spec2006 with it. It is great and except for one benchmark the rest work fine. I want to build them with LTO now. I followed the directions in " http://llvm.org/docs/GoldPlugin.html" and built ar, nm-new, and ld-new in binutils. I also built libLLVMgold.so. I get the following error: llvm-gcc: -use-gold-plugin,

Hi, I tried that out, I got a new error about pthreads in my config.log. Could you take another look. Thanks again, Reza > On Aug 20, 2019, at 12:36 PM, Daniel P. Berrangé <berrange@redhat.com> wrote: > > On Tue, Aug 20, 2019 at 12:29:15PM -0400, reza shahriari wrote: >> Hi, >> >> I have attached my compressed config file. >> >> Thanks,

Hi, I have some S functions plus Fortran routines that I want to use in R. They work in S, but when I try to use "dyn.load" to link fortran codes in R , I get the following error. thanks, reza (I am using R-1.0.1 on windows NT, The name of fortran file (compiled Ratfor) is deldirld.o and it's in the D:\Reza\476\tv.gonsrc.R\ directory) >dyn.load("deldirld.o") Error in

Hi, there are a lot of messages dealing with overdispersion, but I couldn't find anything about how to test for overdispersion. I applied a GAM with binomial distribution on my presence/absence data, and would like to check for overdispersion. Does anyone know the command? Many thanks, Anna -- View this message in context:

Hello, The share of concurring votes (i.e. yes-yes and no-no) in total votes between a pair of voters is a function of their ideological distance (index continuous on [1,2]). I show by other means that the votes typically are highly positively correlated (with an average c=0.6). This is because voters sit together and discuss the issue before taking a vote, but also because they share common

Dear list members, while I appreciate the possibility to deal with overdispersion for count data either by specifying the family argument to be quasipoisson() or negative.binomial(), it estimates just one overdispersion parameter for the entire data set. In my applications I often would like the estimate for overdispersion to depend on the covariates in the same manner as the mean. For example,

Hi all, I have made an overdispersion test for a data set and get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.24267 47444.85 0 after deleting the outliers from the data set I get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.27106 0 1 The

Dear R users, I work with a descrete variable (sclae 0 - 27) which is highly skwed to the right (many zeros and small numbers). I measure this variable on a control and intervention cohort 5 times a year. When I analyze analyze this varoable at each time point separately and use GLM with family quasi-Poisson (descrete outcome and two binary variables, gender and cohort, are predictors), I observe

Hi Reza, -O4 is the highest level of LLVM optimization that I know of. But, I don't know if it has been tried on Spec2006. IIRC, Dan Gohman has run Spec. tests with LLVM, so he can provide more info. - fariborz On Jul 19, 2010, at 6:06 PM, Reza Yazdani wrote: > Hi, > > What are the best options to compile Spec2006 with LLVM compilers to > get the best performance numbers