similar to: updated package boolean 1.5

Version 1.03 of the R package boolean has been uploaded to CRAN and is now available. boolean implements partial-observability logit and probit models for testing Boolean hypotheses. It permits researchers to model the probability of the occurrence of a given outcome as a complex function of the probabilities that other outcomes will occur (or other conditions will be fulfilled). For

Version 1.03 of the R package boolean has been uploaded to CRAN and is now available. boolean implements partial-observability logit and probit models for testing Boolean hypotheses. It permits researchers to model the probability of the occurrence of a given outcome as a complex function of the probabilities that other outcomes will occur (or other conditions will be fulfilled). For

Dear Friends, I have been trying to learn how to use the derivative free optimization algorithms implemented in the package RGENOUD by Mebane and Sekhon. However, it does not seem to work for reasons best described as my total ignorance. If anybody has experience using this package, it would be really helpful if you can point out where I'm making a mistake. Thanks in advance Anup Sample

I recently reformatted my TiBook and reinstalled everything from the system up. After installing Apple's version of XWindows and the XWindows version of R 1.7.0, I ran the two and got a strange warning: ----- R : Copyright 2003, The R Development Core Team Version 1.7.0 Patched (2003-05-14) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under

FYI ---------- Forwarded message ---------- From: Bear Braumoeller <braumoeller.1 at polisci.osu.edu> Date: 2009/8/4 Subject: Re: Package boolean on CRAN To: "cgb at datanalytics.com" <cgb at datanalytics.com> Cc: Ronggui Huang <ronggui.huang at gmail.com>, Ben Goodrich <goodrich at fas.harvard.edu> Gentlemen, Thanks for your enquiries. ?Boolean is under

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

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

We would like to announce the availability on CRAN of a new package multinomRob. It does robust estimation of overdispersed multinomial regression models. The package is also able to estimate overdispersed grouped multinomial logistic and multivariate-t logistic models. The code is relatively general; for example, it allows for equality constraints across parameters and it can handle datasets in

We would like to announce the availability on CRAN of a new package multinomRob. It does robust estimation of overdispersed multinomial regression models. The package is also able to estimate overdispersed grouped multinomial logistic and multivariate-t logistic models. The code is relatively general; for example, it allows for equality constraints across parameters and it can handle datasets in

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

"Matching" version 0.48 is now available on CRAN. Matching provides functions for estimating causal effects by multivariate and propensity score matching. The package includes a variety of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on

"Matching" version 0.48 is now available on CRAN. Matching provides functions for estimating causal effects by multivariate and propensity score matching. The package includes a variety of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on

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

Dear R group, since I do only have a moderately fast MacBook and I would like to get my results faster than within 72h per run ( :-((( ), I tried Amazon AWS, which offers pretty fast computers via remote access. I don't want to post any code because its several hundreds lines of code, I am not looking for the "optimal" answer, but maybe some suggestions from you if faced similar

Hello, I am trying to optimise a nonlinear model to derive 'best-fit' parameter esimates using the genoud function. I have been using the genetic algorithm - gafit - in order to do this, but I am getting parameter estimates that do not always reach the global minimum. I am very keen to apply genoud to optimising this model to see if my results will improve, and also out of personal

I am trying to use rgenoud and snow with an external model and file I/O. Unlike the typical application of rgenoud and snow, I need to run an external executable and do pre- and post-processing of input and output files for each parameter set generated by genoud(). I'm hoping that someone can suggest improvements to my approach and a work-around for file I/O problems I've encountered when

Hi; I have a R script that includes a call to genoud(); genoud process lasts about 4 seconds, what would be OK if I hadn't have to call it about 2000 times. This yields about 2 hours of processing. And I would like to use this script operationally; so that it should be run twice a day. It seems to me that the parallel processing option included in genoud() divides the task inside the function

Hi, I am using the sim1000G R package to simulate data for case/control study. I can not figure out how to manipulate this code to be able to generate 10% or 50% causal SNPs in R. This is whole code provided as example on GitHub: library(sim1000G) vcf_file = "region-chr4-357-ANK2.vcf.gz" #nvariants = 442, ss=1000 vcf = readVCF( vcf_file, maxNumberOfVariants = 442 ,min_maf =

Hello - and sorry for a possibly stupid question, I'm just starting to learn rgenoud. I am defining a function with 5 parameters (p1, p2, p3, p4a, and p4b) and then want to optimize it using genoud. But I am doing something wrong. Before genoud is even able to run it says: "Error in p2 + 1.2 : 'p2' is missing". I assume I did not specify it right. My code is below. The task

Hi Berina, I'm not an expert on genetics. I haven't looked at the package. And I've only glanced at your question. So, this is probably not the best response. But as no one else has responded, here's some comments: (1) Have you checked if there's a function in the package to do what you want? The remainder of these questions assume that you have, and the answer is no. (2)