search for: stepsiz

Hi,all: I met a problem of nls. My data: x y 60 0.8 80 6.5 100 20.5 120 45.9 I want to fit exp curve of data. My code: > nls(y ~ exp(a + b*x)+d,start=list(a=0,b=0,d=1)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates I can't find out the reason for the error. Any suggesions are welcome. Many thanks. [[alternative HTML

Hi there, I have two series of data. plotting the density function of both gives me an idea about the difference of the data. But I would like to quantify the difference I see. a <- rnorm(100) b <- rnorm(100) da <- density(a) db <- density(b) The problem is that da$x and db$x are different and so I have difficulties to compare them... Is there any way to force the density

...the matrix in an efficient way. From the resulting vector I could then easily take the minimum value index and get the parameters from the parameter matrix. Could someone kindly give me a hint how this could be implemented? Thanks a lot, Daniel Code Snippet: fullEnumeration <- function(par, stepSize = c(.03, .03, .03)) { res <- Inf best.x <- -1 best.y <- -1 best.z <- -1 stepx <- stepSize[1] stepy <- stepSize[2] stepz <- stepSize[3] x <- seq(.01, 2, by=stepx) y <- seq(.01, 2, by=stepy) z <- seq(.01, 15, by=stepz) for (i in 1:len...

Hi, I am doing a project on the simulation of glucose metabolism based on a pharmacokinetic modeling in which we have 4 differential equations. I did this in R by using the odesolve package. It works very well, but I have two questions: Here is the odemodel function _________________________________________________ Ogtt.Odemodel <- function(t, y, p) { absx <- c(-60, -45, -30,

I've finally gotten around to playing with the advanced menu, though the complex example is definitely still beyond me. I'm trying to figure out a few basics from it, such as the tab key for line editing (don't know what I've done wrong here since it seems like I copied that part right) and the timeout. It looks to me like the only ontimeout implemented so far is an option to not

...50, 1:4] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" n.cols <- ncol(data) n.rows <- nrow(data) X <- as.matrix(data) stepsize <- 0.05 c1 <- (2 * pi) ** (n.cols / 2) c2 <- n.rows * (smoothing ** (n.cols + 2)) c3 <- n.rows * (smoothing ** n.cols) Kexp <- function(sqs){ return (exp((-1 * sqs) / (2 * smoothing ** 2))) } FindGradient <- function(x){ XmY <- t(x - t(X)) sqsum <-...

...tle suggestion to make. The current behavior is that we have two viewpoints of the "cube" (it's more like a body with eight side planes now). If it's rotated one step we skip one of the planes. Rotating four times is enough to go round exactly once. Is it possible to decrease this stepsize, so we can rotate the body in eight steps? I.E. When having two monitors rotating one step will shift the contents of one monitor to the other. :) Keep up the good work!

...> x <- rcopula(mycop, 1000) > myfit <- fitCopula(x, mycop, c(0.6, 10), optim.control=list(trace=1), method="Nelder-Mead") Nelder-Mead direct search function minimizer function value for initial parameters = -1747.582044 Scaled convergence tolerance is 2.6041e-05 Stepsize computed as 1.000000 Error in chol(x, pivot = FALSE) : the leading minor of order 2 is not positive definite Kevin D. Oden e: kevin.oden@wachovia.com <mailto:kevin.oden@wachovia.com> [[alternative HTML version deleted]]

WARNING: This is not compatible with the previous lz4 patchset. If you''re using experimental compression that isn''t in mainline kernels, be prepared to backup and restore or decompress before upgrading, and have backups in case it eats data (which appears not to be a problem any more, but has been during development). These patches add lz4 and lz4hc compression

Hi I?m trying to fit a nonlinear model to a derivative of the logistic function y = a/(1+exp((b-x)/c)) (this is the parametrization for the SSlogis function with nls) The derivative calculated with D function is: > logis<- expression(a/(1+exp((b-x)/c))) > D(logis, "x") a * (exp((b - x)/c) * (1/c))/(1 + exp((b - x)/c))^2 So I enter this expression in the nls function:

...entium III processor, 512 MB of RAM). Any suggestions would be greatly appreciated. ############################################################################ #ArchI_burnin_NOdensdep-mod.R# habitats <- 1 carrycap <- 7500 stages <- 5 rland <- NULL numreps <- 2 numsteps <- 50 stepsize <- 100 runlength <- numsteps*stepsize rland <- new.landscape.empty() rland <- new.intparam.land(rland, h = habitats, s = stages, totgen = runlength) rland <- new.switchparam.land(rland, mp = 1) rland <- new.floatparam.land(rland) #life history matrices at zero population densi...

Hi, so here it is, LZ4 compression method inside btrfs. The patchset is based on top of current Chris'' for-linus + Andi''s snappy implementation + the fixes from Li Zefan. Passes xfstests and stresstests. I haven''t measured performance on wide range of hardware or workloads, rather wanted to publish the patches before I get distracted again. I''d like to ask

...####################################################################### >> ## >> #ArchI_burnin_NOdensdep-mod.R# >> >> habitats <- 1 >> carrycap <- 7500 >> stages <- 5 >> rland <- NULL >> numreps <- 2 >> numsteps <- 50 >> stepsize <- 100 >> runlength <- numsteps*stepsize >> >> rland <- new.landscape.empty() >> rland <- new.intparam.land(rland, h = habitats, s = stages, totgen = >> runlength) >> rland <- new.switchparam.land(rland, mp = 1) >> rland <- new.floatparam...

Dear R Gang, I'm interested in using R and the nls package for fitting kinetic models. I'm having some difficulty getting a model specified for nls though. The math for the model that I want to fit is dg(t)/dt = K1 f(t) - k2 g(t) where g(t) and f(t) are measured data at a sequence of times t. K1 and k2 are the parameters of the model. If I solve this, the solution is g(t) = K1

...t; library(stats4) > erizo.mle <- mle(start= ini.pars, minuslogl = loglike, method="Nelder-Mead", control = list(maxit=1500, trace=TRUE)) Nelder-Mead direct search function minimizer function value for initial parameters = 1159.477620 Scaled convergence tolerance is 1.72776e-05 Stepsize computed as 84.671790 BUILD 5 3165.307359 1159.477620 . . . HI-REDUCTION 303 1158.377359 1158.377314 LO-REDUCTION 305 1158.377339 1158.377303 LO-REDUCTION 307 1158.377321 1158.377303 Exiting from Nelder Mead minimizer 309 function evaluations used Error en optim(start,...

...ylim=NULL) { x <- as.matrix(data) # n x p numeric matrix center <- colMeans(x) # centroid n <- nrow(x); p <- ncol(x); cov <- cov(x); d <- mahalanobis(x,center,cov) # distances qqInteractive(qchisq(ppoints(n),df=p),d, # ppoints(n) makes a sequence from 0 to 1. with stepsize 1/n main="QQ Plot Assessing Multivariate Normality", # qchisq() makes a chi squared distribution function for the given probabilities in ppoints(n) and degress of freedom df ylab="Mahalanobis D2", xlim=xlim, ylim=ylim) #abline(a=0,b=1) } y <- c((1:100)+rnorm...

...t = NULL, misccovariates = NULL, miscconstraint = NULL, qconstraint=NULL, econstraint=NULL, covmatch = "previous", initprobs = NULL, data = list(), fromto = FALSE, fromstate, tostate, timelag, death = FALSE, tunit = 1.0, exacttimes = FALSE, fixedpars = NULL, stepsize=1.0... ) } with corresponding \item's in the \arguments list. Among the differences (see the end of the argument lists) In code by not Rd: stepnumerator, stepdenominator, do.what In Rd but not code: stepsize Yet R CMD check says ** help >>> Building/Updating help pages for package...

Linux 3.11 added respective support, so I think we should follow suit. 1: xen: add LZ4 decompression support 2: libxc: add LZ4 decompression support Signed-off-by: Jan Beulich <jbeulich@suse.com>

...being copied along the way. What follows is both the R and C code which I use only for testing and a plot of both measurements with DUP=TRUE and DUP=FALSE: (RED: DUP=FALSE, GREEN: DUP=TRUE) http://www.nabble.com/file/p20368695/CandR.png R code: ---------- # sequence from 512 to 2^23 with 2^17 stepsize a <- seq(512, 2^23, 2^17) # storage for wall time h <- length(a); j <- length(a) for (i in 1:length(a)) { x <- as.double(1:a[i]) y <- as.double(x) # system.time()[3] is (actual) wall time h[i] <- system.time(.C("commTest", x, y, DUP=FALSE))[3]...

...ith that flag, the build runs into trouble with the first example in ?optim. Running it by hand gives me: > optim(c(-1.2,1), fr, control=list(trace=6)) Nelder-Mead direct search function minimizer function value for initial parameters = 24.200000 Scaled convergence tolerance is 3.60608e-07 Stepsize computed as 0.120000 BUILD 3 24.200000 7.095296 REFLECTION 5 15.080000 4.541696 REFLECTION 7 7.095296 4.456256 [...] HI-REDUCTION 191 0.000002 0.000000 LO-REDUCTION 193 0.000001 0.000000 and the process just hangs (until ctrl-z and kill -9). If I build R with...