similar to: nls for piecewise linear regression not converging to least square

Hi, I have a small dataset that I'm fitting a segmented regression using nls on. I get a step below minimum factor error, which I presume is because residual sum of square is still "not small enough" when steps in the parameter space is already below specified/default value. However, when I look at the trace, the convergence seems to have been reached. I initially thought I might

I want to fit discrete data that was measured on a wavegenerator. In this minimal example i generate some artificial data: testsin <- 2+ 5 * sin(1:100) #generate sin data testsin <- testsin+ rnorm(length(testsin), sd = 0.01) #add noise mydata <- list(X=1:100, Y=testsin) # generate mydata object nlmod <- nls(X ~ A+B*sin(C* Y), data=mydata, start=list(A=2, B=4, C=1), trace=TRUE) #

Hello there, I am trying to use R function NLS to analyze my data and one of the examples in the documentation is - ## the nls() internal cheap guess for starting values can be sufficient: x <- -(1:100)/10 y <- 100 + 10 * exp(x / 2) + rnorm(x)/10 nlmod <- nls(y ~ Const + A * exp(B * x), trace=TRUE) plot(x,y, main = "nls(*), data, true function and fit, n=100") curve(100 +

Greets, I'm trying to learn to use nls and was running the example code for an exponential model: > x <- -(1:100)/10 > y <- 100 + 10 * exp(x / 2) + rnorm(x)/10 > nlmod <- nls(y ~ Const + A * exp(B * x)) Error in B * x : non-numeric argument to binary operator In addition: Warning message: In nls(y ~ Const + A * exp(B * x)) : No starting values specified for some

Hello Is there any function to calculate White's standard errors in R in an NLS regression. The sandwich and car package do it but they need an lm object to calculate the error's. Does anyone have idea how to do it for an NLS object ? Regards The woods are lovely, dark and deep But I have promises to keep And miles before I go to sleep And miles before I go to sleep ----- [[alternative

All: Are there any functions out there for selecting all the points in a region of a plot. I envision something like the identify() function except one could circle a cloud of points (and perhaps a vector would be returned of the same length as the points plotted indicating logical membership in the circled cloud). Perhaps someone has done something with the locator() function that would

Seriously, if a method gives a wrong result, it's wrong. line() does NOT implement the algorithm of Tukey, even not after the patch. We're not discussing Excel here, are we? The method of Tukey is rather clear, and it is NOT using the default quantile definition from the quantile function. Actually, it doesn't even use quantiles to define the groups. It just says that the groups

Le 31/05/2017 ? 17:30, Serguei Sokol a ?crit : > > More thorough reading revealed that I have overlooked this phrase in the > line's doc: "left and right /thirds/ of the data" (emphasis is mine). Oops. I have read the first ref returned by google and it happened to be tibco's doc, not the R's one. The layout is very similar hence my mistake. The latter does not

Estimada Patricia: El mail con spam ha salido de mi casilla, pero yo no lo he enviado intencionalmente. Algo o alguien habrá robado mi lista de contactos y envió este mail  en forma masiva a todos ellos. Pido disculpas por las molestias ocasionadas y te pido por favor que tengas en cuenta este punto para futuras eventualidades, porque a cualquiera le puede pasar.   Saludos! Cesar Procopio.

Hi folks I wonder if anyone can help me. I want to run some simulations to see how big a sample size might be necessary to distinguish a curved bivariate relationship (e.g. something that might be best described by a quadratic model) from a relationship that is two straight lines with a sudden change in slope (e.g. something best described by a breakpoint regression). I am using

Dear helpers, I am using constrOptim to minimize a function subject to inequality constraint. It works well when the number of parameters to optimize is low (e.g. 4) but when they are more (e.g. 10) it does not produce the expected results. The function is minus the determinant of a binomial logit model with one explanatory variable. Calling ?xeta? the vector of explanatory variables the

Hi everyone, I had a question about linux qos using the tc command. I''m sorry if I''m mailing to the wrong list. I''d appreciate any redirection to the right one. What I want to do is to converge several flows. Is this possible with Linux ?I only found examples that fan out, like the tree structure. And was wondering if it''s possible to converge it.

Howdee, My question appears at #6 below: 1. I want to model the growth of each of a large number of individuals using a 4-parameter logistic growth curve. 2. nlme does not converge with the random structure that I want to use. 3. nlsList does not converge for some individuals. 4. I decided to go around nlsList using: t(sapply(split(data, list(data$id)), function(subd){coef(nls(mass ~

The coxph function in R is not working for me when I use a continuous predictor in the model. Specifically, it fails to converge, even when bumping up the number of max iterations or setting reasonable initial values. The estimated Hazard ratio from the model is incorrect (verified by an AFT model). I've isolated it to the "x1" variable in the example below, which is log-normally

I'm trying to build a SEM using the sem package. I'll attach my model specification below. When I turn debug=TRUE, it seems as if I'm getting to convergence because I get this message: Successive iterates within tolerance. Current iterate is probably solution. However, at the end of the process I get this message: Warning message: In sem.default(ram = ram, S = S, N = N,

Yeh, I eventually figured that out. Thanks for responding though. Jack ________________________________________ From: llvmdev-bounces at cs.uiuc.edu [llvmdev-bounces at cs.uiuc.edu] on behalf of llvmdev-request at cs.uiuc.edu [llvmdev-request at cs.uiuc.edu] Sent: Sunday, November 20, 2011 10:00 AM To: llvmdev at cs.uiuc.edu Subject: LLVMdev Digest, Vol 89, Issue 47 Send LLVMdev mailing list