similar to: Curve Fitting

Hi, I desperately try to do s.th. like m=1.23455 sig=0.84321 plot(1,1) text(0.8,1,sprintf("<Sigma>=%1.2f?%1.2f",m,sig)) where actually the greek letter should appear for Sigma. I have tried all sorts of combinations with expression and paste etc. but could not work it out. Maybe someone has an idea and can help me. Thanks a lot+ Thomas

Hi, I am having troubles in putting greek letters and formatted text in a plot m=1.43432 sig=0.124333 text(10.5,0.07,sprintf("<Sigma>=%1.2f±%1.2f",m,sig)) I would like to have the greek letter Sigma followed by the formatted numeric values of m and sig. Does someone know a solution? thanks a lot Thomas --------------------------------- Thomas Bschorr Department of Physics

Hi all, Like a lot of people I noticed that I get different results when I use nls in R compared to the exponential fit in excel. A bit annoying because often the R^2 is higher in excel but when I'm reading the different topics on this forum I kind of understand that using R is better than excel? (I don't really understand how the difference occurs, but I understand that there is a

Good morning gentlemen! How using a weighted model in nls2? Values with the nls are logical since values with nls2 are not. I believe that this discrepancy is due to I did not include the weights argument in nls2. Here's an example: MOISTURE <- c(28.41640, 28.47340, 29.05821, 28.52201, 30.92055, 31.07901, 31.35840, 31.69617, 32.07168, 31.87296, 31.35525, 32.66118, 33.23385,

Dear All, I was trying to call a self-defined function that performs nonlinear regression and gets the corresponding prediction upper limit using nls2 package. However, weird thing happened. When I called the function in the main program, an error message "fitted(nlsmodel): object 'nlsmodel' not found" came up. But when I directly ran the codes inside the function, no error came

Good morning gentlemen! When I use the function nls2, and store it in an object, that object is automatically printed, without the summary or to draw the object. For example. model <- nls2 (...) Number of iterations to convergence: ... Achieved convergence tolerance: ... Nonlinear regression model model: ... ~ ... Date: NULL The B k ... ... ... residual sum-of-squares: ... Number

Dear Madam or sir, Does anyone know if there is a pre-compiled version of package nls2 for windows, please? Thank you. Souleymane

I am using nls to fit a non linear function to some data. The non linear function is: y= 1- exp(-(k0+k1*p1+ .... + kn*pn)) I have chosen algorithm "port", with lower boundary is 0 for all of the ki parameters, and I have tried many start values for the parameters ki (including generating them at random). If I fit the non linear function to the same data using an external

first, apologies for so many posts yesterday and today. I am wrestling with nls() and nls2(). I have tried to whittle it down to a simple example that still has my problem, yet can be cut-and-pasted into R. here it is: library(nls2) options(digits=12); y= c(0.4334,0.3200,0.5848,0.6214,0.3890,0.5233,0.4753,0.2104,0.3240,0.2827,0.3847,0.5571,0.5432,0.1326,0.3481) x=

  Hi, I fitted tree growth data with Chapman-Richards growth function using nls. summary(fit.nls) Formula: Parameters: Estimate Std. Error t value Pr Signif. codes: 0 ''***'' 0.001 ''**'' 0.01 ''*'' 0.05 ''.'' 0.1 '' '' 1 Residual standard error: 1.879 on 713 degrees of freedom Algorithm

Dear R users and R friends, I have a little problem... I don't know anymore which package to use if I want to perform a power regression analysis. To be clear, I want to fit a regression model like this: fit <- ....(y ~ a * x ^ b + c) where a, b and c are coefficients of the model. The R Site does not have the answer I want... Thanks in advance and with kind regards, David

I'm using nls to fit periodic gene-expression data to sine waves. I need to set the upper and lower boundaries, because I do not want any negative phase and amplitude solutions. This means that I have to use the "port" algorithm. The problem is, that depending on what start value I choose for phase, the fit works for some cases, but not for others. In the example below, the fit works

Hi, All: What tools exist for diagnosing singular gradient problems with 'nls'? Consider the following toy example: DF1 <- data.frame(y=1:9, one=rep(1,9)) nlsToyProblem <- nls(y~(a+2*b)*one, DF1, start=list(a=1, b=1), control=nls.control(warnOnly=TRUE)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial

hello, I have got some trouble with R functions nlm(), nls() or optim() : I would like to fit 3 parameters which must stay in a precise interval. For exemple with nlm() : fn<-function(p) sum((dN-estdata(p[1],p[2],p[3]))^2) out<-nlm(fn, p=c(4, 17, 5), hessian=TRUE,print.level=2) with estdata() a function which returns value to fit with dN (observed data vactor) My problem is that only

Hi there, I am trying to compare nonlinear least squares regression with AIC and anova. The simplest model is one nonlinear curve, and in the more complex model I have a categorical variable (producing parameter estimates for four curves). Both models run fine, but when I try to produce an AIC value for the second model I get the error: > AIC(pow.nls1) [1] 114408.3 > AIC(pow.nls2) Error in

Hola a todos estoy aplicando la función polinómica de Hossfeld [1], y algunos otros modelos no lineales para tratar de ajustarlos a un grupo de datos forestales,   [1] Y= b*t*exp(c)/(t*exp(c)+a) Al colocar la función en R con parámetros estimados, me devuelve los siguiente: ## model1 <- nls(ho ~ (b*edad*exp(c)/(edad*exp(c)+a)), data=nigra,     start=list(a=0.005,b=0.08,c=-0.00006),

Dear R-users, I am trying to create a model using the NLS function, such that: Y = f(X) + q + e Where f is a nonlinear (Weibull: a*(1-exp(-b*X^c)) function of X and q is a covariate (continous variable) and e is an error term. I know that you can create multiple nonlinear regressions where x is polynomial for example, but is it possible to do this kind of thing when x is a function with unknown

Hello, I'm using the following for loop to find regression curves using a list of functions (formList), a list of starting values (startList), uppervalues (upperList) and lower values (lowerList). A sample of the list of function I use in the loop is the following: FormList <- list(PTG.P ~ fz1(Portata, a, b), PTG.P ~ fz2(Portata, a, b), PTG.P ~ fz3(Portata,a, b, d, e), PTG.P ~

Subject: nls, step factor 0.000488281 reduced below 'minFactor' of 0.000976563 Hi there, I'm trying to conduct nls regression using roughly the below code: nls1 <- nls(y ~ a*(1-exp(-b*x^c)), start=list(a=a1,b=b1,c=c1)) I checked my start values by plotting the relationship etc. but I kept getting an error message saying maximum iterations exceeded. I have tried changing these

Hola, quiero ajustar una curva sinoidal de la forma "f(x)=k/( 1+(c/log(x))^n)" mediante la función 'nls' pero me da error el siguiente código: >datos<-read.table(file="datos.csv", header=TRUE,sep=";",dec=",") >library(nls) >fit <- nls(y ~ k/(1+(c/log(x))^n), datos, start = list (k=100 , c =5*10^(-6), n=1)) Error en