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Dear All I am using rgenoud to solve the following maximization problem: myfunc <- function(x) { x1 <- x[1] x2 <- x[2] if (x1^2+x2^2 > 1) return(-9999999) else x1+x2 } genoud(myfunc, nvars=2, Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.000001) How can one increase the precision of the solution $par [1] 0.7072442 0.7069694 ? I

Hi, Anybody knows a function that can fit haplotype data to a Cox model. I've been searching it in the web without succeed. I use "haplo.stats" package, but unfortunatelly it's not possible to analyse survival data, amb I right?. Thanks in advance. Isaac Subirana (isubirana@imim.es) [[alternative HTML version deleted]]

Hello! I need to purchase a new box, which I would like to optimize for good R performance. For the record, I will run Fedora Core 5 as and OS, and I wanted to know if anyone has experience with how the following affects R performance: - Is there a big advantage to having a 64-bit CPU over having a 32-bit? - Does an Opteron offer any advantages over an Athlon, and if yes, does it justify an

Hi all, I've been looking in R for an EM algorithm adjusted for multiple-locus haplotypes frequencies, but failed in 100%. Has anyone heard of anything of this kind in R? Thanks in advance, Marcin

Dear everyone, I am trying to run rgenoud on several chips simultaneusly. I used the instructions provided on Jasjeet Sekhon's Homepage (http://sekhon.berkeley.edu/rgenoud/multiple_cpus.html). However, I have the newer version of R (R 2.12) installed - for a 64-bit machine. So, when I tried to install the special version of "snow" from a zip file provided by Jasjeet on his page, R

I've just uploaded an R version of Daniel Schaid's 'haplo.score' library (from http://www.mayo.edu/statgen/) to CRAN. >From the DESCRIPTION file: [haplo.score is a] suite of routines that can be used to compute score statistics to test associations between haplotypes and a wide variety of traits, including binary, ordinal, quantitative, and Poisson. These methods assume

Sehr geehrte Damen und Herren, hiermit m?chte ich bei Ihnen nachfragen, ob die f?r Splus geschriebene "haplo.score"- Funktion auch unter R verwendet werden kann. siehe http://www.mayo.edu/statgen/software/ Wenn ja, wie sind die Aufrufe Splus CHAPTER -m und Splus make in R zu realisieren? Herzlichen Dank f?r Ihre M?he mit freundlichen Gr??en -- Dr. rer. nat. Andreas Fr?hlich

Hello! I need to compare 2 datasets whether they come from the same distribution. I use function ks.boot{Matching}. And what is the confidence level of the p-value, returned by ks.boot function? The code is: set=read.table("http://stella.sai.msu.ru:8080/~gala/data/testsets.csv", header=T,sep=',') set1=set[!is.na(set$set1),'set1']

I am trying to determine whether two samples are identical or not. I'm aware that somebody can use the Kolmogorov-Smirnoff test to compare empirical distributions, but since my samples have ties I'm not sure if I'm getting the right p-values for the comparison. Can the Kolmogorov-Smirnoff test be adjusted for the case when ties exists and are there any functions that already

Does anyone else find that using the Var.calc option (for heteroscedasticity consistent std. errors) in Match() (from the Matching library) slows down computation of the matching estimator by a lot? I don't really understand why when I use this option it slows down so much, but for me it does significantly. I want to use the heteroscedasticity consistent std. errors in my project, but as long

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the

Hi R users, "genoud" function of "rgenoud" package will optimize my function. If opt = genoud(fn,2,max=TRUE,starting.value=c(1,10),........) opt$value will give the optimized value of the function, "fn". My problem is from the same opt, can I get the value of the function at the initial parameter values? I need the initial value of the function for

I am trying to use the optimizing function genoud() with the snow package on a couple of i686 machines running Redhat Linux WS4 . I don't know anything about PVM or MPI, so I just followed the directions in snow and rgenoud for the simplest method and started a socket cluster. My function fn for genoud involves updating an input file for a separate numerical model with the latest parameter

To anyone who uses the Match() function in the Matching library... How do you go about deciding how many matches you will use? With my data, my standard errors generally get smaller if I use more matches. Speaking of standard errors, when correcting for heteroscedasticity, how many matches do you use (this is the Var.cal option). It seems to me that it might make sense to use the same number

Hi, I am trying the following: library(ismev) library(evd) fit <- gev.fit(x,show=FALSE) ks.test(x,pgev,fit$mle[1],fit$mle[2],fit$mle[3]) but I am getting: Warning message: cannot compute correct p-values with ties in: ks.test(x, pgev, fit$mle[1], fit$mle[2], fit$mle[3]) where x is: [1] 239 38 1 43 22 1 5 9 15 6 1 9 156 25 3 100 6 [18] 5 100

Hi R-masters I need yours help about a problema in one of may ongoing researchers. In my research the subjects (20 in total) answer 60 questions (20 type G, 20 type S and 20 type P). Which questions is classified about 3 factor (2 level each) and the subject score with 2 scale (not integer value is possible but rare): Val range -7 to 7 and other Car range 1 to 7. This a code to fake database

Dear all, I'm using the package "Genetics", and I'm interested in the computation of D' statistics for Linkage Disequilibrium, for which the LD() command has been realised. Unfortunately I don't find any reference on "how" the D' is computed by the LD() function. In the package documentation it is generally referred as "MLE" estimation, but

Hi All, is there any chance of vectorising the two ifelse() statements in the following code: for(i in gp){ new[i,1] = ifelse(srow[i]>0, new[srow[i],zippo[i]], sample(1:100, 1, prob =Y1, rep = T)) new[i,2] = ifelse(drow[i]>0, new[drow[i]>0,zappo[i]], sample(1:100, 1, prob =Y1, rep = T)) } Where I am forced to check if the value of drow and srow are >0 for each line... in

Hello list, I am dealing with a noisy function (gradient,hessian not available) with simple boundary constraints (x_i>0). I've tried constrOptim() using nelder mead to minimize it but it is way too slow and the returned results are not satisfying. simulated annealing is so hard to tune and it always crashes R program in my case. I wonder if there are any packages or functions can do