similar to: Parameter info from nls object

Hello! I work with : R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) On Windows XP Professional (Version 2002) SP2. At this moment I use the function "nls" combined with a selfStar model (SSmicmen, related to Michaelis-Menten equation, and provided by the "stats" package). When I realise the following operation (cf. p 59 of the

Hi, I would like to be able to access the calculated results from the nls package. Using the example in R, fm3DNase1 we can reurn certain parts of the calculations: > coef(fm3DNase1) Asym xmid scal 2.345179 1.483089 1.041454 > resid(fm3DNase1) [1] -0.0136806237 -0.0126806237 0.0089488569 0.0119488569 -0.0025803222 [6] 0.0064196778 0.0026723396 -0.0003276604

I'm wondering whether AIC scores extracted from nls() objects using AIC() are based on the correct number of estimated parameters. Using the example under nls() documentation: > data( DNase ) > DNase1 <- DNase[ DNase$Run == 1, ] > ## using a selfStart model > fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) Using AIC() function: >

Hola amigos, Supongamos que se quiere ejecutar un modelo no lineal con nls. Pensemos en el ejemplo de la ayuda: DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) summary(fm1DNase1) Aquí se está modelando la densidad óptica de un ensayo relacionada de forma no lineal (logística) con (el logaritmo) de la concentración de una proteína.

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

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) Now for a weighted fit: fm2DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal),

Thanks Adaikalavan, however the problem remains. Considering AIC() as applied to the linear model in AIC() help documentation: > data(swiss) > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1) [1] 326.0716 Clearly this includes the estimation of the residual standard error as an estimated parameter, as this gives the correct score: > -2*logLik(lm1) + 2*(length(coef(lm1))+1)

Hi, I am trying to run the following nonlinear regression model. > nreg <- nls(y ~ exp(-b*x), data = mydf, start = list(b = 0), alg = "default", trace = TRUE) OUTPUT: 24619327 : 0 24593178 : 0.0001166910 24555219 : 0.0005019005 24521810 : 0.001341571 24500774 : 0.002705402 24490713 : 0.004401078 24486658 : 0.00607728 24485115 : 0.007484372

Hi all, I try to write a simple function in a script. The script is as follows yo<-function(Xdata) { n<-length(Xdata[,1]) Lgm<-nls(formula=LgmFormula, data=Xdata, start=list(a=1500,b=0.1),weights=Xdata$Qe) return(Lgm) } After the execution of the script, when I call the function yo on data called NC60.DATA I get an error. #yo(NC60.DATA) Erreur dans eval(expr, envir, enclos)

Dear WizaRds, A run of nls produces the following concise summary: > summary(cs.wt) Formula: 0 ~ wt.MM(conc, time, A1, a1, A2, a2) Parameters: Estimate Std. Error t value Pr(>|t|) A1 4.814e+02 2.240e+01 21.495 0.0296 * a1 7.401e-01 7.435e-02 9.956 0.0637 . A2 1.613e+02 1.738e+01 9.280 0.0683 . a2 1.770e-02 7.324e-03 2.417 0.2497

Hi all, I try to represent a multiple curve graphic where the x-axis is the temperature and the different y-axes are the different X (X22,X43,X44...) some X corresponds to the same molecule (22 and 44 are for CO2 for instance) so I use the same colour for them. I wanna mix the linetype with the colour to be able to visually see the difference between X43 and X45 The best I have done up to now

I am having a couple of problems someone may be able to cast some light on. Question 1: I am making a logistic model but when i do this: glm.model = glm(as.factor(form$finished) ~ ., family=binomial, data=form[1:150000,]) I get this: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for 'barrier') which is

Dear sirs It seems like there is a bug in `profile.nls` with `algorithm = "plinear"` when a matrix is supplied on the right hand side. Here is the bug and a potential fix ##### # example where profile.nls does not work with `plinear` but does with # `default` require(graphics) set.seed(1) DNase1 <- subset(DNase, Run == 1) x <- rnorm(nrow(DNase1)) f1 <- nls(density ~ b1/(1 +

Dear all, I am studying a bit the various support functions that exist for extracting information from fitted model objects. From the help files it is not completely clear to me whether the number returned by nobs() should be the same as the "nobs" attribute of the object returned by logLik(). If so, then there is a slight inconsistency in the methods for 'nls' objects with

Dear list, I have encountered a problem with predict.nls (Windows XP, R.2.5.0), but I am not sure if it is a bug... On the nls man page, an example is: DNase1 <- subset(DNase, Run == 1) fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), data = DNase1, start = list(xmid = 0, scal = 1)) alg = "plinear", trace =

Hello, How do I specify the formula and random effects without a startup object ? I thought it would be a mixture of nls and lme. after trying very hard, I ask for help on using nlme. Can someone hint me to some examples? I constructed a try using the example from nls: #variables are density, conc and Run #all works fine with nls DNase1 <- subset(DNase, Run == 1 ) fm2DNase1 <- nls(

Hi everybody, I'm trying to fit a Garch(1,1) process to the DAX returns. My data consists of about 2300 10day-logreturns in chronologically descending order (see attachment). But if I use the garch function I get a very high alpha_1 and a quite low beta, which doesn't make that much sense. I think I am missing something, but have no idea what it might be. I'd appreciate it a lot

What am I failing to understand here? The script below works fine if the dataset being used is DNase1 <- DNase[ DNase$Run == 1, ] per the example given in help(nlrob). Obviously, I am trying to understand how to use nls and nlrob to fit curves to data using R. #package=DAAG attach(codling) plot(pobs~dose) #next command returns 'step factor reduced below min factor

Hello I followed the example in page 59, chapter 11 of the 'Introduction to R' manual. I entered my own x,y data. I used the least squares. My function has 5 parameters: p[1], p[2], p[3], p[4], p[5]. I plotted the x-y data. Then I used lines(spline(xfit,yfit)) to overlay best curves on the data while changing the parameters. My question is how do I calculate the residual sum of squares.

Dear List, I have been trying to use library sm to do density estimation on a 3D dataset. I am using the current MacOS X binary of sm from CRAN. If I do this on a 2D dataset, sm.density returns a list including the component "estimate" which contains the density estimate over a uniform grid. When doing this with 3D data, although I get a nice plot (even when I don't ask for one),