similar to: Re: gregmisc version 0.7.3 now available

Dear Greg, Thanks for the new release. The decomposition of the SSQ is just what I need! Regards, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK Webpage: http://myprofile.cos.com/martinhoyle >>> gregory_r_warnes at groton.pfizer.com 10/30/02 07:16PM >>> Version 0.7.3 of the gregmisc package

Hi, the function makeARIMA(), designed to construct some state space representation of an ARIMA model, uses a C function called getQ0, which can be found at the end of arima.c in R source files (library stats). getQ0 takes two arguments, phi and theta, and returns the covariance matrix of the state prediction error at time zero. The reference for getQ0 (cited by help(arima)) is:

Dear R list, Please, could someone help me with SSQ decomposition and contrasts. Below my data, graphic, ANOVAs and my doubt: # Data a = paste('a', gl(3, 8), sep='') b = paste('b', gl(2, 4, 24), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4, 24.8, 24.6, 26.7, 25.2, 25.7, 26.3, 25.1, 26.4, 19.6,

# Dear R list, # # A have a doubt about SSQ decomposition and contrasts with ANOVAs. # So, I would like a tip from more advanced R users. # Below my data, the basic script and my doubts: # Data r = paste('r', gl(3, 8), sep='') e = paste('e', rep(gl(2, 4), 3), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4,

#Hello, #I have a question about obtaining results from a loop I have written. #Below is a sample of individual genotypes from a genetic question I am working on called "P.genotype.sample ". P.genotype.sample<-matrix(10,10,10) P.genotype.sample[,1]<-c(2,2,1,5,1,1,5,6,1,3) P.genotype.sample[,2]<-c(6,3,3,6,8,1,6,7,2,3) P.genotype.sample[,3]<-c(2,2,2,3,3,2,2,2,3,3)

#Hello, #I have would like to paste a single column of a matrix # in pair wise fashion with other columns based upon # even and odd column numbers. # I can do it in a very clunky fashion and I know there # must be a better way. below is a sample matrix and my extremely # clunky code that gets the job done for a small matrix, but i plan to # do this on a much grander scale. any help would be very

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Thanks Bert for all the help. I got the legend figured out Friday but left early becoz of long weekend so didn't get a chance to reply.. I modified the plot margins a little bit and Here's what I finally had... par(mar=c(c(10, 6, 6, 10) + 0.1)); par(xpd=FALSE); with (dataFrame, stripchart(marbles_buried ~ genotype, method="jitter", vertical=TRUE, col = c('blue',

Hi, I am conducting an association analysis of genotype and a phenotype such as cholesterol level as an outcome and the genotype as a regressor using multiple linear regression. There are 3 possibilities for the genotype AA, AG, GG. There are 5 people with the AA genotype, 100 with the AG genotype and 900 with the GG genotype. I coded GG genotype as 1, AG as 2 and AA as 3 and the p-value for the

Hi All, I have a problem trying to get a set of columns recognised as a list and can't work out how to do it despite trawling through the mailing list archives, and docs. A short example... to.convert <- NULL n <- 6 for(x in 1:n){ to.convert <- paste(to.convert, paste((2 * x) -1, (2 * x), sep=":"), sep=",") } to.convert <- gsub("^,", "",

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I

On 02/03/2015 8:46 AM, sarah manderni wrote: > Thanks! I went through the online posts which supports the power of > .Call over .C. But my probably naive question is why does this work > for my code with R but not R-devel? Because of the change mentioned in the NEWS file. > And another question is related to using .Call. Based on the manual > page, I do not need to change the

Hello, My R skills are somewhere between novice and intermediary, and I am hoping that some of you very helpful forum members, whom I've seen work your magic on other peoples' problems/questions, can help me here. I have a matrix with the following format: (i) individual plants comprising many different genotype groups (i.e., a plant is genotype 1 or genotype 2 or genotype 3, etc). The

Thanks! I went through the online posts which supports the power of .Call over .C. But my probably naive question is why does this work for my code with R but not R-devel? And another question is related to using .Call. Based on the manual page, I do not need to change the function parameters when using .Call. So I can run like this: .Call("sppedUp", D, S, pD, pS, nrow(D), as.integer(N),

-----Original Message----- From: Ghosh, Sandeep Sent: Thursday, June 30, 2005 5:43 PM To: 'Berton Gunter' Subject: plot legend outside the grid Thanks for the pointers... I managed to get everything to look and feel the way I want except for the legend to plot outside the grid... Thanks for the note on the par, but I'm not able to it to plot outside the plot grid.. dataFrame <-

Hi I am using ggplot2 at the moment and I must say it is definitely better then ggplot - good work. My problem is that I am using facet_grid() in the following way: > p <- ggplot(ssq, aes(x=year, y=-log(ssq))) > p + geom_point() + facet_grid(me*gi~cs*rz) and it works nicely, except that I would like to have, in naddition to the values of me, gi, cs and rz the name of the variable.

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

I was not sharing it on maling list because i thought that someone can use all ideas i proposed in their GSOC proposal. Surely i will contribute to xapian project. sorry if that was against the rules The algorithm is not developed by me but after having much research on various clustering techniques. I found that there is a new algorithm called CLUBS(Clustering Using Binary Splitting) which gives

I want to specify a two-factor model in lme, which should be easy? Here's what I have: factor 1 - treatment FIXED (two levels) factor 2 - genotype RANDOM (160 genotypes in total) I need a model that tells me whether the treatment, genotype and interaction terms are significant. I have been reading 'Mixed effects models in S' but in all examples the random factor is not in the main