search for: jasjeet

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

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 did not allow me t...

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

...across parameters and it can handle datasets in which the number of categories varies by observation. DESCRIPTION: Package: multinomRob Version: 1.0 Date: 2004/02/18 Title: Robust Estimation of Overdispersed Multinomial Regression Models Author: Walter R. Mebane, Jr. <wrm1 at cornell.edu>, Jasjeet Singh Sekhon <jasjeet_sekhon at harvard.edu> Maintainer: Jasjeet Singh Sekhon <jasjeet_sekhon at harvard.edu> Description: overdispersed multinomial regression using robust (LQD and tanh) estimation Depends: R (>= 1.7.0), rgenoud (>= 1.22), MASS (>= 7.1-8), mvtnorm (>= 0.6-3...

...across parameters and it can handle datasets in which the number of categories varies by observation. DESCRIPTION: Package: multinomRob Version: 1.0 Date: 2004/02/18 Title: Robust Estimation of Overdispersed Multinomial Regression Models Author: Walter R. Mebane, Jr. <wrm1 at cornell.edu>, Jasjeet Singh Sekhon <jasjeet_sekhon at harvard.edu> Maintainer: Jasjeet Singh Sekhon <jasjeet_sekhon at harvard.edu> Description: overdispersed multinomial regression using robust (LQD and tanh) estimation Depends: R (>= 1.7.0), rgenoud (>= 1.22), MASS (>= 7.1-8), mvtnorm (>= 0.6-3...

I see something new and unexpected here. > update.packages() trying URL `http://cran.r-project.org/src/contrib/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 163467 bytes opened URL .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... ......... downloaded

...g the snow and rgenoud packages. The function I want to run in task-parallel fashion across multiple machines is one that pre- and post-processes data and runs an external model code. My problem is that external file I/O is happening only in the master node and not in the slaves. I have followed Jasjeet Sekhon's suggestion to test the cluster setup, and that is fine: > library(snow) > > #pick two machines > cl <- makeCluster(c("moab","escalante")) > > clusterCall(cl, sin, 2) > The output should be: > > clusterCall(cl, sin, 2) > [[1]] &g...

...an experiment or quasi-experiment is balanced on baseline covariates. The functions provide valid standard errors and allow one to estimate various estimands. For documentation and further details see: http://jsekhon.fas.harvard.edu/matching Cheers, Jas. ====================================== Jasjeet S. Sekhon Associate Professor Harvard University Center for Basic Research in the Social Sciences jasjeet_sekhon at harvard.edu http://jsekhon.fas.harvard.edu/ Office: 617.496.2426 Fax: 617.507.5524 _______________________________________________ R-packages mailing list R-packages at stat.math....

...an experiment or quasi-experiment is balanced on baseline covariates. The functions provide valid standard errors and allow one to estimate various estimands. For documentation and further details see: http://jsekhon.fas.harvard.edu/matching Cheers, Jas. ====================================== Jasjeet S. Sekhon Associate Professor Harvard University Center for Basic Research in the Social Sciences jasjeet_sekhon at harvard.edu http://jsekhon.fas.harvard.edu/ Office: 617.496.2426 Fax: 617.507.5524 _______________________________________________ R-packages mailing list R-packages at stat.math....

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

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 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

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

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

I would like to limit the significant figures of the calibrated parameters determined by genoud() in the Rgenoud package. Below is some example output, where column 1 is model run number, columns 2-7 are the parameter values, and columns 8-12 are model fit statistics. I would like genoud to internally limit parameters to 4 decimal places as shown in this output. It is clear that the function is

Hi all, i am looking to built a simple example of a very basic propensity score adjustment, just using the estimated propensity scores as inverse probability weights (respectively 1-estimated weights for the non-treated). As far as i understood, MLE predictions of a logit model can directly be used as to estimates of the propensity score. I already considered the twang package and the

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

Dear R-help: Has anyone tried using R on the the AMD Opteron in either 64- or 32-bit mode? If so, any good/bad experiences, comments, etc? We are considering getting this hardware, and would like to know if R can run smoothly on such a beast. Any comment much appreciated. Best, Andy Andy Liaw, PhD Biometrics Research PO Box 2000, RY33-300 Merck Research Labs Rahway, NJ