similar to: faster way?

Hi, I cannot find a 'vectorized' solution to this 'for loop' kind of problem. Do you see a vectorized, fast-running solution? Objective: Take the value of X at each timepoint and calculate the corresponding value of Y. Leading 0's and all 1's for X are assigned to Y; otherwise Y is incremented by the number of 0's adjacent to the last 1. The frequency and

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,

The data sets I am working with all have a weight variable--e.g., each row doesn't mean 1 observation. With that in mind, nearly all of the graphs and summary statistics are incorrect for my data, because they don't take into account the weight. **** For example "median" is incorrect, as the quantiles aren't calculated with weights: sum( weights[X < median(X)] )

> 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:

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 sometimes indicates that the list of starting values is too far from optimum but this is unlikely since I picked values close to where

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'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

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

Hello, I am using R 2.2.1 with sn 0.4.0 and am trying to save a dns2.plot as a jpg file. Here is the latest version of the code I have tried: x <- seq(0,200,length=200) y <- seq(0, 35, length=200) jpeg(filename = "C:/fig1.jpg", width = 5, height = 4,pointsize = 12, quality = 100, bg = "white", res = NA)

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 ..-

>>>>> 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

Thanks to Ross Ihaka and Bob Wheeler for responding to my earlier question. I looked into the Johnson system functions in SuppDists package. For now, I want to stick with the density(...) estimator, and so still need the variate lookup function. As per Ross' suggestion, I just did a numerical integration on the density object and used approxfun/splinefun to "lookup" the variate

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 ##

(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

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

Hi Michael, On Thu, Dec 7, 2017 at 3:16 AM, Michael Zolotukhin <mzolotukhin at apple.com> wrote: > > Nice to IWYU developers here:) I wonder how hard it would be to run IWYU on > LLVM/Clang (or, if it’s supposed to work, I wonder what I did wrong). There are known problems with running IWYU over LLVM/Clang -- Zachary Turner made an attempt a while back to get it up and running.