similar to: nonlinear least squares, multiresponse

I tried to define a function like: fnx <- function(x, by.vars=Month) print(by(x, by.vars, summary)) But this doesn't work (does not find x$Month; unlike other functions, such as subset(), the INDICES argument to "by" does not look for variables in dataset x. Is fully documented, but I forget every time). So I tried using "with": fnxx <- function(x, by.vars=Month)

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

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

Hello! I have dataset where one variable is 'multiresponse', like this: [1] "1 2" "1 2 3" "4" "1 4" "4 3" etc. 'responses' separated by space. observations in different 'rows' of data.frame. I can do strsplit(data$var,' ') and make a list, where multiple responses are elements of character vectors, like this: $

I am looking for a way to do multiresponse parameter estimation for a problem that is quite similar in form to the alpha-pinene example in Bates and Watts (1988), p. 147, where the model is a set of ODEs. I am specifically interested in obtaining the parameter covariance matrix, as well. Which package(s) in R would I use to do such a thing? Mark Schaffer Dept. of Chemical Engineering

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]]

Hi, I am fitting a weighted least square regression and trying to compute SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know what I am coding wrong. Can you help please? xnam <-colnames(X) # colnames Design Matrix fmla1 <- as.formula(paste("Y ~",paste(xnam, collapse=

> 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:

Full_Name: Albrecht Gebhardt Version: 0.90.0 OS: Linux + Tru64 Submission from: (NULL) (143.205.61.72) This is where example(krige) from library(sgeostat) stops with R 0.90.0: krige> image(grid$x, grid$y, grid$krige$zhat, add = T) Error in length(x) == nrow(z) : comparison (1) is possible only for vector types in previous versions of R the above image() call worked, because image() did the

Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i

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

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

Hi guys I have data that contains the variances vt of the yields of 1, 2, 3, 4, 5,10, 20 year bonds. Assuming the Hull-White model for the yield of a t-year zero-coupon bond, I have to estimate the ? of the Hull-White model using nonlinear least squares and give a 95% con?dence interval for each parameter. Please can you guys tell how to find out ? using R. Any suggestion regarding what functions

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on

Hi, I am new to R, and am trying to solve the following optimization problem: This is a nonlinear least squares problem. I have a set of 3D voxels. All I need is to find a least squares fit to this data. The data model actually represent a cube-like structure, consisting of seven straight lines. The lines have some intersections (and at this intersection both of the participating lines end).

Hello, I am trying to optimise a nonlinear model to derive 'best-fit' parameter esimates using the genoud function. I have been using the genetic algorithm - gafit - in order to do this, but I am getting parameter estimates that do not always reach the global minimum. I am very keen to apply genoud to optimising this model to see if my results will improve, and also out of personal

Hello, I'd like to fit a user defined function to a data set, but I have problems to find my problem. The user defined function is a combination of two rectangular functions, and the listing below gives an example for what I want to do. The problem is, that I get the error message for fit1 and fit2 "Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

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