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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, f(xhat), tol=0) # good: 1.8 e-12 #[1] "Mean relative difference: 1.816536e-12" abs( opt$objective - f(xhat) ) < 1e-4 ## Must be T...

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 removing the bounds. However, when bounds are added the snippet returns 'FALSE'. An older R version (3.4.4), compiled using the same gcc-8.2, did not have the problem. Between the t...

...$ 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 ..- attr(*, "names")= chr [1:2] "function" "gradient" $ message : chr "relative convergence (4)" > xhat <- rep(1, 10) > all.equal(opt$par, xhat, tol=0) [1] "Mean relative difference: 3.266165e-07" > all.equal(opt$objective, f(xhat), tol=0) [1] "Mean relative difference: 6.722005e-13" > abs( opt$objective - f(xhat) ) < 1e-4 [1] TRUE > sessionInfo() R ver...

...or BLAS/LAPACK .. > > I also use gcc 8.2 (on Fedora 28 Linux) and R's own BLAS/LAPACK > and don't see such problems: > > The code > > 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$par, xhat, tol=0) # good: 5.53 e-7 > all.equal(opt$objective, f(xhat), tol=0) # good: 1.8 e-12 > abs( opt$objective - f(xhat) ) < 1e-4 ## Must be TRUE > > gives > >> f <- function(x) sum( log(diff(x)^2+.01) + (x[1]-1)^2 ) &g...

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]]

...e 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 removing the bounds. However, when bounds are added the snippet returns 'FALSE'. > An older R version (3.4.4), compiled using the same gcc-8.2, did not have...

...utine 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 the parameters usually end up. I have also tried switching these around a bit. Here is the code: xhat = c(statemw-(1-alpha)*rval) survivalf <- function(x) {(1-plnorm(statemw,mean=mu,sd=logalpha))} wagefn <- function(lam, eta, alpha, xhat, mu, logalpha) { n=nrow(cpsdata2) wagevec = matrix(nrow=n,ncol=1) for (i in 1:n) { if (cpsdata2[i,2] > 0){ wagevec[i,] <- c(eta*l...

...e R-forge (developmental) version of the optimx package at > https://r-forge.r-project.org/projects/optimizer/ > > I used the code > > ## KKristensen19nlminb.R > 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 > opt > library(optimx) > optx <- opm(rep(0,10), f, lower=-1, upper=3, method="ALL") > summary(optx, order=value) > optxc <- opm(rep(0,10), f, gr="grcentral", lower=-1, upper=3,...

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 -a Linux localhost.localdomain 4.17.7-200.fc28.x86_64 #1 SMP Tue Jul 17 16:28:31 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux My version of R is described by: Sour...

...es: > 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 ok... [I gave a version of the above which reveals a bit more ...] > My system is described by: adcomp.git> uname -a > Linux localhost.localdomain 4.17.7-200.fc28.x86_64...

...r 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 ## KKristensen19nlminb.R 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 opt library(optimx) optx <- opm(rep(0,10), f, lower=-1, upper=3, method="ALL") summary(optx, order=value) optxc <- opm(rep(0,10), f, gr="grcentral", lower=-1, upper=3, method="ALL") summar...

...: 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 the > parameters usually end up. I have also tried switching these around a bit. > > Here is the code: > > xhat = c(statemw-(1-alpha)*rval) > survivalf <- function(x) {(1-plnorm(statemw,mean=mu,sd=logalpha))} > > wagefn <- function(lam, eta, alpha, xhat, mu, logalpha) { > n=nrow(cpsdata2) > wagevec = matrix(nrow=n,ncol=1) > for (i in 1:n) { > > if (cpsdata2[i,2] >...

...<- matrix(ncol=no.obs,nrow=no.obs) Ffast <- matrix(ncol=no.obs,nrow=no.obs) P <- Pminus <- array(dim=c(no.state,no.state,n)) Pfast <- Pfastmin <- matrix(NA,ncol=no.state,nrow=no.state) K <- array(dim=c(no.state,no.obs,n)) Kfast <- matrix(nrow=no.state,ncol=no.obs) X <- Xhatminus <- array(dim=c(no.state,1,n)) Xfast <- Xfastmin <- matrix(ncol=1,nrow=no.state) v <- array(dim=c(no.obs,1,n)) vfast <- matrix(nrow=no.obs,ncol=1) Y <- matrix(ncol=1,nrow=no.obs) log.lik <- numeric(n) Rprof() kalman <- function(a)...

...sure if the values of Ypred within the validat slot are the predictions of each observation of Y when leave-one-out cross validation is applied. My code is as follows: > mypls <- mvr(Xtrain, Ytrain, method="SIMPLS", validation="CV", ncomp=1, niter=nrow(Ytrain)) > Xhat <- mypls$training$Xscores %*% t(mypls$training$Xload) > R2 <- 1-(sum((Xhat-Xtrain)^2)/sum(Xtrain^2)) > Q2 <- 1-(sum((Ytrain-mypls$validat$Ypred[,,1])^2)/sum(Ytrain^2) Is this right? Thank you Ana ------------------------------------------- Ana Conesa, PhD Bioinformatics Depa...

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