similar to: DBLEPR?

On 24.04.2015 22:44, Ben Bolker wrote: > Prof J C Nash (U30A <nashjc <at> uottawa.ca> writes: > >> >> I was preparing a fix for a minor glitch in my optimx package and R CMD >> check gave an error that the title was not in title case. > > [snip] to make Gmane happy ... > >> I have found >> >> A Replacement and Extension of the

> On 25 Apr 2015, at 13:11 , Prof J C Nash (U30A) <nashjc at uottawa.ca> wrote: > > Hendrik pointed out it was the parentheses that gave the complaint. > Single quotes and no parentheses seem to satisfy R CMD check. Perhaps > that needs to be in the WRE. Well, it is in ?toTitleCase: ...However, unknown technical terms will be capitalized unless they are single

I was preparing a fix for a minor glitch in my optimx package and R CMD check gave an error that the title was not in title case. It is A Replacement and Extension of the optim() Function R CMD check suggests the incorrect form A Replacement and Extension of the Optim() Function 'Writing R Extensions' suggests single quotes, i.e., A Replacement and Extension of the 'optim()'

I am making my first attempts at using some Fortran code with R, and so far it's going OK. To print from my Fortran programs, it seems I need subroutines dblepr, realpr and intpr. From the excellent "Writing R Extensions" document: "Three subroutines are provided to ease the output of information from FORTRAN code. subroutine dblepr(label, nchar, data, ndata)

Hi I've built a dll using Fortran code and can call it by either R or Fortran. Calling by the former gives me the wrong answer and the later gives the correct answer. From what I've read, it looks like I should use the subroutines DBLEPR, INTPR and REALPR to get values to show up in R which I hope will allow me to find the error. I've downloaded the R-2.12.0.tar.gz but have

I have not included the previous postings because they came out very strangely on my mail reader. However, the question concerned the choice of minimizer for the zeroinfl() function, which apparently allows any of the current 6 methods of optim() for this purpose. The original poster wanted to use Newton-Raphson. Newton-Raphson (or just Newton for simplicity) is commonly thought to be the

This is mainly a reminder to others developing R packages to be careful not to supply control list items that are not used by the called package. Optimx is a wrapper package that aims to provide a common syntax to a number of existing optimization packages. Recently in extending optimx package I inadvertently introduced a new control for optimx which is NOT in any of the wrapped optimization

Recently I reported a small bug in optim's SANN method failing to report that it had exceeded the maximum function evaluation limit in the convergence code. This is a small enough matter that I was reluctant to create a full-blown bug report. Indeed in the optimx package Ravi Varadhan and I have been developing on r-forge (under the OptimizeR project) it was a minimal work around to fix

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

There are now several R geographic user groups, and a few have mailing lists on the R mailing list system. Thanks to Martin M, there's also a pointer to a page I'm maintaining to list/describe the groups. The page is at http://macnash.telfer.uottawa.ca/RUG.html Contact me if you have a listing. I'm prepared to wikify it if there is sufficient interest. John Nash

x1<-c(5.548,4.896,1.964,3.586,3.824,3.111,3.607,3.557,2.989,18.053,3.773,1.253,2.094,2.726,1.758,5.011,2.455,0.913,0.890,2.468,4.168,4.810,34.319,1.531,1.481,2.239,4.204,3.463,1.727) y<-c(2.590,3.770,1.270,1.445,3.290,0.930,1.600,1.250,3.450,1.096,1.745,1.060,0.890,2.755,1.515,4.770,2.220,0.590,0.530,1.910,4.010,1.745,1.965,2.555,0.770,0.720,1.730,2.860,0.760)

Hi, I would like to write a function that finds parameters of a log-normal distribution with a 1-alpha CI of (x_lcl, x_ucl): However, I don't know how to optimize for the two unknown parameters. Here is my unsuccessful attempt to find a lognormal distribution with a 90%CI of 1,20: prior <- function(x_lcl, x_ucl, alpha, mean, var) { a <- (plnorm(x_lcl, mean, var) - (alpha/2))^2 b

I've been creating some R tools that manipulate objective functions for optimization. In so doing, I create a character string with R code, and then want to have it in my workspace. Currently -- and this works fine -- I write the code out, then use source() to bring it in again. Example: cstr<-"jack<-function(x){\n cat(\"Silly x:\")\n print(x) \n }\n" write(cstr,

I'm planning to try to use Mike Powell's new BOBYQA optimization routine to see if it is a good candidate to eventually replace Nelder-Mead as the optim() default. (Don't panic -- this won't happen quickly.) Mike writes f77 code. After some chasing about, I've realized that because optimization codes need to call the objective function written in R, they need to use a language

I spent more time than I should have debugging a script because I wanted x<-seq(0,100)*0.1 but typed x<-seq(O:100)*0.1 seq(0:100) yields 1 to 101, Clearly my own brain to fingers fumble, but possibly one others may want to avoid it. JN

Has anyone ever successfully used Fortran character strings with R? ?Foreign says R C Fortran integer int * integer .... character char ** [compiler dependent] Character strings are passed as C arrays of character strings to Fortran: the first string may be usable if its length is passed

This message is about a curious difference in timing between two ways of computing the same function. One uses expm, so is expected to be a bit slower, but "a bit" turned out to be a factor of >1000. The code is below. We would be grateful if anyone can point out any egregious bad practice in our code, or enlighten us on why one approach is so much slower than the other. The problem

Hey, R Users my windows is 64 bit windows 7.?I am trying to install the package ucminf into my 64 bit version R but cannot.??the package I downloaded is from http://cran.r-project.org/web/packages/ucminf/index.html?and I installed it with the "install from local zip files", due to I did not connect my computer to internet. did anyone meet this problem and is there a version of

Hi, That looks like the dependency on nlopt for nloptr (used by several of the solvers) was somehow missing. There is SUPPOSED to be a check of what is installed and a warning issued, but possibly one got missed. Even some base packages do sometimes need the external libraries manually installed, and I find I'm doing that after various OS updates or resets when the long term support runs out.

Having looked at documentation and codes, I'm still looking to find out who did the installation of the Port libraries. As I work on optimization code improvements, there are often choices of settings (tolerances etc.), and I'd prefer to learn if there are particular reasons for choices before diving in and making any adjustments. It is also worth giving credit where it is due. Given