similar to: Using MLE on a somewhat unusual likelihood function

(inline) > On 7 Nov 2017, at 06:02 , Hugo Andr? <karlhugoandre at gmail.com> wrote: > > So I am trying to use the mle command (from stats4 package) to estimate a > number of parameters using data but it keeps throwing up this error message: > > Error in solve.default(oout$hessian) : > Lapack routine dgesv: system is exactly singular: U[1,1] = 0 > > This error

Hello all... I am just starting to teach myself Bayesian methods, and am interested in learning how to use UMacs. I've read the documentation, but the single example is a bit over my head at the level I am at right now. I was wondering if anyone has any simple examples they'd like to share. I've successfully done a couple of simple gibbs examples, but have had a hard time

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,

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

No error on Windows 10, R.3.5.2 patched, Rblas compiled with OpenBLAS 0.20, Rlapack is base. > 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) List of 6 $ par : num [1:10] 1 1 1 1 1 ... $ objective : num -41.4 $ convergence: int 0 $ iterations : int 66 $ evaluations: Named int [1:2] 96 830 ..-

> On 1 Feb 2019, at 10:00, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > > ........ >>> sessionInfo() >> R version 3.5.2 (2018-12-20) >> Platform: x86_64-pc-linux-gnu (64-bit) >> Running under: Scientific Linux release 6.4 (Carbon) > >> Matrix products: default >> BLAS/LAPACK:

Hi, Is there a faster way to do this? It takes forever, even on a moderately sized dataset. n <- dim(dsn)[1] dsn2 <- dsn[order(-dsn$xhat),] dsn2[1, "cumx"] <- dsn2[1, "xhat"] for (i in 2:n) { dsn2[i, "cumx"] <- dsn2[i - 1, "cumx"] + dsn2[i, "xhat"] } [[alternative HTML version deleted]]

>>>>> Kasper Kristensen via R-devel >>>>> on Mon, 28 Jan 2019 08:56:39 +0000 writes: > 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

Prof Nash, Prof Galanos Is it possible to use a generic code stub in front of packages that use optimx to improve optimx use or curtail it according to the requirements? Best Regards Amit +91 7899381263 ________________________________________________________________________ Please request Skype as available 5th Year FPM (Ph.D.) in Finance and Accounting Area Indian Institute

I get the failure message. To be specific: adcomp.git>R CMD BATCH --quiet test_nlminb.R adcomp.git>cat test_nlminb.Rout > 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 [1] FALSE My system is described by: adcomp.git>uname

Thank you, Brad (and others), >>>>> Brad Bell on Mon, 4 Feb 2019 07:21:18 -0700 writes: > I get the failure message. To be specific: adcomp.git> R CMD BATCH --quiet test_nlminb.R adcomp.git> cat test_nlminb.Rout >> f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 ) >> opt <- nlminb(rep(0, 10), f, lower=-1, upper=3) >>

This is not about the failure on some platforms, which is an important issue. However, what is below may provide a temporary workaround until the source of the problem is uncovered. FWIW, the problem seems fairly straightforward for most optimizers at my disposal in the R-forge (developmental) version of the optimx package at https://r-forge.r-project.org/projects/optimizer/ I used the code ##

Hi, I got the following problem. I am doing a maximum likelihood estimation for a Kalman Filter. For this purpose, I have to invert an error matrix Ffast of dimension "no. parameters X no.parameters". The usualy optim methods often find only local minima, so I decided to make the optimization using the SANN algorithm, which is very slow already. However, this becomes a real problem

Dear list I am using the mvr function of the package pls.pcr to compute PLS resgression using a X matrix of gene expression variables and a Y matrix of medical varaibles. I would like to obtain the R2 (sum of squares captured by the model) and Q2 (proportion of total sum of squares captured in leave-one-out cross validation) of the model. I am not sure if there are specific slots in the

I'm trying to fit a model to solve a biological problem. There are multiple independent variables, and also there are multiple responses. Each response is a function of all the independent variables, plus a set of parameters. All the responses depend on the same variables and parameters - just the form of the function changes to define each seperate response. Any ideas how I can fit

Good morning to you all, Sorry for taking your time from your research and teaching schedules.   If you have a non-stationary univariate time Series data that has the transformation: Say; l.dat<-log (series) d.ldat<-diff (l.dat, differences=1) and you fit say arima model. predit.arima<-predict (fit.series, n.ahead=10, xregnew= (n+1) :( n+10)) How could I re-transform

Estimados, Muchas gracias por sua ayuda, ya pude instalar data.table y esta funcionando. Respecto al paquete biodiversityR, no se porque ahora no me corre. El script esta correcto, pero no se porque me tira error. > fase<-diversitycomp(biobsp, y=biopcat,factor1="Fase",index="richness",method=?s") Error in diversitycomp(biobsp, y = biopcat, factor1 =