similar to: smbclient 2.0.0b2

Dear R useRs, i try to compute the posterior mean for the parameters omega and beta for the following posterior density. I have simulated data where i know that the true values of omega=12 and beta=0.01. With the function postMeanOmega and postMeanBeta i wanted to compute the mean values of omega and beta by numerical integration, but instead of omega=12 and beta=0.01 i get omega=11.49574 and

hello, how i cant to charge in form dynamic a checkbutton, try to do it with a vector be charged automaticamente but not works, for example library(tcltk) tt<-tktoplevel() f<-tkframe(tt) tkpack(f) i<-2 if (i==1) {b1<-tkcheckbutton (f,text="b1",variable="b1",relief="raised");tkpack(b1);print(tclvalue ("b1"))}else if (i==2) {b1<-tkcheckbutton

I sent this in with an old version, but it's in latest version as well. The fix is simple. In the summary.table function, the parameter is calculated incorrectly for a test of independence among all cells when the table is more than 2-way table. Example: Consider X: > X a b c 1 A1 B2 C1 2 A3 BA3 C2 3 A2 B1 C4 4 A1 B2 C3 5 A3 BA3 C2 6 A1 BA3 C1 7 A2 BA3 C2 8 A1

Hi I searched in the list and only found questions without answers e.g. http://finzi.psych.upenn.edu/R/Rhelp02a/archive/19955.html : Is there a way to get the same results with lme as with aov with Error()? Can anybody reproduce the following results with lme: id<-c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5)

7 Jan 09 Hello, I am using R version 2.7.0 in a Windows XP context. I am also using the glmm.admb package (created by Dave Fournier, Hans Skaug, and Anders Nielson) to run mixed-effects negative binomial models. To the best of my knowledge and ability, I have searched and studied the R-help, R-sig-mixed models, and ADMB NBMM for R (through Otter Research Ltd) list servs; R help

I have a complex function that I want to maximize (I have multiplied this function by -1 so that it becomes a minimization problem in the code below). This function has two equality constraints. I get the programs to run but the answer isn't correct because, when it does converge, at least one of the constraints is violated. Any suggestions? Code below Violated constraint (an easy check):

Commit-ID: bae97a14a3dab910cd57c1d36003b18a869f788f Gitweb: http://git.kernel.org/?p=libs/klibc/klibc.git;a=commit;h=bae97a14a3dab910cd57c1d36003b18a869f788f Author: Martijn Dekker <martijn at inlv.org> AuthorDate: Wed, 7 Mar 2018 17:32:29 +0000 Committer: Ben Hutchings <ben at decadent.org.uk> CommitDate: Fri, 25 Jan 2019 02:57:21 +0000 [klibc] builtin: Greater resolution

For an application, I have formulas defined in terms of a left Kronecker product of matrices, A,B, meaning A \otimes_L B = {A * B[i,j]} -- matrix on the left multiplies each element on the right. The standard kronecker() function is the right Kronecker product, A \otimes_R B = {A[i,j] * B} -- matrix on the right multiplies each element on the left. The example below shows the result of

Commit-ID: e86e3a7edc8934dc6a9ecd4bb360d19672f65ccf Gitweb: http://git.kernel.org/?p=libs/klibc/klibc.git;a=commit;h=e86e3a7edc8934dc6a9ecd4bb360d19672f65ccf Author: Martijn Dekker <martijn at inlv.org> AuthorDate: Wed, 7 Mar 2018 17:32:29 +0000 Committer: Ben Hutchings <ben at decadent.org.uk> CommitDate: Sat, 28 Mar 2020 21:42:54 +0000 [klibc] dash: builtin: Greater

Thank you for reading. I am trying to get sphericity values, and I understood I need to use mlm, but how do I implement a nested within subject design in mlm? I already read the R newsletter, fox chapter appendix, EZanova, and whatever I could find online. My original ANOVA anova(aov(resp ~ sucrose*citral, random =~1 | subject, data = p12bl, subset = exps==1)) Or anova(aov(resp ~

Hi, I don't understand why this code doesn't work (f(b2)): ///////////////// setClass("B0", representation(b0 = "numeric")) setClass("B1", representation("B0", b1 = "character")) setClass("B2", representation("B1", b2 = "logical")) f <- function(x) class(x) setMethod("f", "B0",

=BE=B4=C6=F4=D5=DF=A3=BA =A1=B0=B5=DA=CB=C4=BD=EC=B1=B1=BE=A9=B3=AF=D1=F4=B9=FA=BC=CA=C9=CC=CE=F1= =BD=DA=A1=B1=BD=AB=D3=DA2003=C4=EA10=D4=C2=D4=DA=B1=B1=BE=A9=BE=D9=B0=EC=A1= =A3=BD=EC=CA=B1=A3=AC=B9=FA=BC=D2=C1=EC=B5=BC=C8=CB=A1=A2=CD=E2=B9=FA=D5=FE= =D2=AA=BA=CD=C0=B4=D7=D4=C3=C0=A1=A2=B5=C2=A1=A2=B7=A8=A1=A2=B0=C4=B4=F3=C0=

Hello everyone! I am having some trouble supplying the gradient function to nlm in R for windows version 2.2.1. What follows are the R-code I use: fredcs39<-function(a1,b1,b2,x){return(a1+exp(b1+b2*x))} loglikcs39<-function(theta,len){ value<-sum(mcs39[1:len]*fredcs39(theta[1],theta[2],theta[3],c(8:(7+len))) - pcs39[1:len] * log(fredcs39(theta[1],theta[2],theta[3],c(8:(7+len)))))

Dear Wiza[R]ds, I am very grateful to Duncan Murdoch for his assistance with this problem. His help was invaluable. However, the problem has become a little more complicated for me. Now, in each plot, I need to plot the observed and fitted values of a supine and upright posture experiment. Here is what I have and how far I got. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma #

Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...

Dear all, A question on modelling proportional data in R. I have a test experiment that was designed in a particular way, and which I can analyse "by hand" to an extent. I am really struggling to get R to give me sensible results in modelling it "properly", so must be doing something wrong here. As background, I conduct a series of experiments and count the

On Jan 22, 2008, at 2:10 AM, humeafo wrote: > I am a newbie to LLVM, so I have to say sorry if I asked the > question in the wrong place. > In some cases when I generate LLVM IR from machine assembly(with > limited type information) I have to convert the pointers to I32, > after the standard mem2reg pass there still are things like: > > inttoptr i32 %1 to i8* > >

Previous kernel was 4.13.0-10.1-liquorix-amd64 The updated kernel that I'm having trouble was not liquorix -- was straight from Debian. Here's my apt log: Log started: 2018-01-02 12:04:48 (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ...

Hello, I am new to R and I am trying to estimate a discrete model with three choices. I am stuck at a point and cannot find a solution. I have probability functions for occurrence of these choices, and then I build the likelihood functions associated to these choices and finally I build the general log-likelihood function. There are four parameters in the model, three of them are associated to

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2