similar to: syntax of nlme

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 =

Hi! When checking validity of a model for a large number of experimental data I thought it to be interesting to check the information provided by the summary method programmatically. Still I could not find out which method to use to get to those data. Example (not my real world data, but to show the point): [BEGIN] > DNase1 <- subset(DNase, Run == 1) > fm1DNase1 <- nls(density ~

Dear all I try to refine my nlme models and with partial success. The model is refined and fitted (using Pinheiro/Bates book as a tutorial) but when I try to plot plot(augPred(fit4)) I obtain Error in predict.nlme(object, value[1:(nrow(value)/nL), , drop = FALSE], : Levels (0,3.5],(3.5,5],(5,7],(7,Inf] not allowed for vykon.fac > Is it due to the fact that I have unbalanced

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 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

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

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: >

I?m new to R so maybe this issue has been asked before and I still could not read the complete set of past messages sent to the list. I found a weird behabiour that I will explain with a simple example. Lets consider the following block of commands: > x <- diag(c(1,4,10)) > x [,1] [,2] [,3] [1,] 1 0 0 [2,] 0 4 0 [3,] 0 0 10 > invx <- x^-1 > invx

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

Full_Name: Tadashi Kadowaki Version: 2.4.0 OS: Redhat Linux 9 Submission from: (NULL) (58.12.166.67) Doesn't nls function support subset? It seems not to work. And, there are no information in the online help. Has it sunk into oblivion?

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)

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.

Hello, I am wondering if someone can help me. I have the following function that I derived using nls() SSlogis. I would like to find its derivative. I thought I had done this using deriv(), but for some reason this isn't working out for me. Here is the function: asym <- 84.951 xmid <- 66.90742 scal <- -6.3 x.seq <- seq(1, 153,, 153) nls.fn <- asym/((1+exp((xmid-x.seq)/scal)))

I ran into a problem trying to make a plot from a file that's read using source. Basically, I have the following code in a file "plot.R" : library(nlme) data(Loblolly) fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc), data = Loblolly, fixed = Asym + R0 + lrc ~ 1, random = Asym ~ 1, start = c(Asym = 103, R0 = -8.5, lrc = -3.3))

Full_Name: Hendrik Weisser Version: 2.6.1 OS: Linux Submission from: (NULL) (139.19.102.218) The following computation never finishes and locks R up: > values <- list(x=10:30, y=c(23.85, 28.805, 28.195, 26.23, 25.005, 20.475, 17.33, 14.97, 11.765, 8.857, 5.3725, 5.16, 4.2105, 2.929, 2.174, 1.25, 1.0255, 0.612, 0.556, 0.4025, 0.173)) > y.max <- max(values$y) > model <- nls(y ~

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

Hi R help, I am trying to determine how nls() generates a function based on the self-starting SSlogis and what the formula for the function would be. I've scoured the help site, and other literature to try and figure this out but I still am unsure if I am correct in what I am coming up with. ************************************************************************** dat <-

deriv seems to have problems with a minus-sign before a bracket. Below are four examples of the same function, the top one is wrong, all others are correct (hopefully). Rest of expression not shown, it is the same for all versions. _ platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32 status major 1 minor 3.0 year 2001 month 06 day 22 language R

This is only partly a question about R, as I am not quite sure about the underlying statistical theory either. I have fitted a non-linear mixed-effects model with nlme. In the fixed part of the model I have a factor with three levels as explanatory variable. I would like to use Tukey HSD or a similar test to test for differences between these three levels. I have two grouping factors:

Hi folks, I am tearing my hair out on this one. I am using an example from Pinheiro and Bates. ### this works data(Orange) mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange ) ### This works mod <- nlme(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange, fixed = Asymp + xmid + scal ~ 1, start =