similar to: optimization and gradient

Dear all, I am currently creating a package and writing the documention files for some functions (the main functions). When using R CMD check myPackage, I get a warning message indicating that some of my functions are not documented. How can I get rid of this problem? Of course, I don't want to document every function in the package... Thanks for your help. -- David Ardia H-building, room

Dear all, I am currently building a package with C code (in scr folder). Everything was ok, but now, I get the following error message when compiling the package using R CMD INSTALL: make[2]: *** No rule to make target 'R/Copy', needed in [...] What does that mean? I can still compile other packages containing C code, but not that one. No 00UNLOCK directory has been created . I am on

Dear all, How can I pass '-Inf' and 'Inf' values from R to C code using the function '.C(...)'. When running my code, I get an error since C does not recognize -Inf and Inf values. Of course, I could use instead a very low (or high) number, but I was wondering whether a more elegant solution exists. Thanks for your help. -- David Ardia H-building, room 11-26

I'm a very recent user of R. I have been adapting my Splus programmes and I found only one (important) problem. There exists no function "nlmin" in R and its substitute, "nlm", does not work well with my kind of problems, sometimes no achieving convergence, other tines "converging" to impossible values. My models are highly nonlinear and are to be estimated by

Dear all, The new version of AdMit (version 1.01-01) is now available from CRAN. SUMMARY The package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings

Dear all, The new version of AdMit (version 1.01-01) is now available from CRAN. SUMMARY The package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings

This is a follow-up question for PR#15052 <http://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15052> There is another thing I would like to discuss wrt how nlminb() should proceed with NAs. The question is: What would be a successful way to deal with an evaluation point of the objective function where the gradient and the hessian are not well defined? If the gradient and the hessian both

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

'AdMit' 1.00-02 is a contributed R package which provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the

Dear all, I very often use the R function "apply", for speedup purposes. I am now also using MATLAB, and would like to use the same kind of function. I have already asked MATLAB people, and the answer is : "vectorize"... but of course, this is not always possible. So, instead of using a FOR loop all the time, I tried using the bsxfun. So you R people, who might also use MATLAB,

Hello splus-users, I am trying to fit a regression model for an ordered response factor. So I am using the function polr in library(MASS). My data is a matrix of 1665 rows and 63 columns (one of the column is the dependent variable). The code I use is polr(as.ordered(q23p)~.,data=newdatap) but I am getting the following warning message singularity encountered in: nlminb.1(temp, p, liv, lv,

I've noticed unstable behavior of nlminb on some Linux systems. The problem can be reproduced by compiling R-3.5.2 using gcc-8.2 and running the following snippet: f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 ) opt <- nlminb(rep(0, 10), f, lower=-1, upper=3) xhat <- rep(1, 10) abs( opt$objective - f(xhat) ) < 1e-4 ## Must be TRUE The example works perfectly when

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

Hi Everybody, My problem is that nlminb doesn't converge, in minimising a logLikelihood function, with 31*6 parameters(2 weibull parameters+29 regressors repeated 6 times). I use nlminb like this : res1<-nlminb(vect, V, lower=c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, n-15)), upper=c(rep(Inf, 12), rep(0.99, 3), rep(Inf, n-15)), control = list(maxit=1000) ) and that's the result :

Hello, R 3.5.2 on ubuntu 18.04. sessionInfo() at the end. Works with me, same results, cannot reproduce the error. f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 ) opt <- nlminb(rep(0, 10), f, lower=-1, upper=3) str(opt) xhat <- rep(1, 10) all.equal(opt$par, xhat, tol=0) # good: 5.53 e-7 #[1] "Mean relative difference: 5.534757e-07" all.equal(opt$objective,

Dear all I want to compute Monte Carlo p-values in lmer-models based on sampled data sets. To speed up calculations, I've tried to use internal functions from the Matrix package (as suggested ealier on the list by Doug Bates). So I did: fm2 <- lmer(resistance ~ ET + position + (1|Grp), Semiconductor,method='ML') simdata<-simulate(fm2,nsim=1) ynew <- simdata[,1] mer

Dear R-users I am trying to find a value for the risk free rate minimizing the difference between a BS call value with impl. volatilities minus the market price of a call (assuming this is just the average bid ask price) Here is my data: http://r.789695.n4.nabble.com/file/n3747509/S%26P_500_calls%2C_jan-jun_2010.csv S%26P_500_calls%2C_jan-jun_2010.csv S0 <- 1136.03 q <- 0.02145608 S0

Hello again Is there any way (or an alternative non-linear minimiser) that arguments to the function called in nlm can be passed in version 0.62.4? Like (I believe) nlmin in a well known other program or optimise in R. Do we use global variables? Shurely not! \John -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read