similar to: help: exponential fit?

Hi, I was trying to run the example of Indomethacin kinetics from the book: ## From Pinheiro/Bates, Mixed-Effects-Models in S and S-Plus, ## Springer, Second Printing 2001, Section 6.2 library(nlme) plot(Indometh) fm1Indom.nls <- nls(conc~SSbiexp(time,A1,lrc1,A2,lrc2), data=Indometh) summary(fm1Indom.nls) plot(fm1Indom.nls,Subject~resid(.),abline=0) ## .... the last plot command gives me the

Hello all! I have a problem to use a biexponential regression model: I use this code: n3<-nls(proc~SSbiexp(cls,a,b,c,d),data=bline) and this is the error message: Error in nls(y ~ cbind(exp(-exp(lrc1) * x), exp(-exp(lrc2) * x)), data = xy, : singular gradient My data is like this: structure(list(proc = c(387.177462830167, 508.090511433077, 321.836817656365, 151.226805860727,

DeaR I am trying to use a dynamically create formula with nlsList and nlme, but I cannot get the environment of the string-generated formal to work similarly to the manually entered one. Any idea? Dieter #----- library(nlme) # Pinheiro/Bates p 280 fm1Indom.lis = nlsList(conc~SSbiexp(time,A1,lrc1,A2,lrc2), data=Indometh) nlme(fm1Indom.lis,random=pdDiag(A1+lrc1+A2~1)) # works... # Simulating

At the top of page 283 of Pinheiro and Bates, a covariance structure for the indomethicin example is specified as random = pdDiag(A1 + lrc1 + A2 + lrc2 ~ 1) The argument to pdDiag() looks like a two-sided formula, and I'm struggling to reconcile this with the syntax described in Ch4 of the book and online. Further down page 283 the formula is translated into list(A1 ~ 1, lrc1 ~ 1, A2 ~ 1,

Dear R-listers, I would like to know if there is a way to programmatically generate parameter start values for the model y~(a*exp(b*x)+c*exp(d*x)) in R. I've scoured the help files and archives for nls() and similar searches, and have read Fox 2002 - the best advice has been to make estimates from a priori knowledge of the data. However, in the Matlab CurveFit tool, reliable start values are

Following the Indomethicin example in Pinheiro & Bates, chapter 6, page 277 etc, coming to the following comand: fm2Indom.nlme <- update( fm1Indom.nlme, random = pdDiag(A1 + lrc1 + A2 ~ 1) ) debugging nlme gives the following output: Browse[1]> n debug: modelResid <- ~eval(model, data.frame(data, getParsNlme(plist, fmap, rmapRel, bmap, groups, beta, bvec, b, level,

Dear R users, I don't know how to compute an exponential model like this: proc=a*exp(b*cls), or proc=a*exp(b*cls)+c*exp(d*cls). Please help me to solve this problem! Thank you! My data is: row.names proc cls 1 0.5 452.616206 0.5 2 1 255.864021 1.0 3 1.5 150.885316 1.5 4 2 86.289600 2.0 5 2.5 56.321559 2.5 6 3 39.504444 3.0 7 3.5 25.570308 3.5 8 4 5.382726 4.0 -- --- Catalin-Constantin

Hi there are some predefined self-start functions, like SSmicmen, SSbiexp, SSasymp, SSasympOff, SSasympOrig, SSgompertz, SSflp, SSlogis, SSweibull, Quadratic, Qubic, SSexp (nlrwr) Btw, do you know graphic examples for this functions? The SSexpDecay (exponential decay) for y = (y0 - plateau)*exp(-k*x) + plateau from

Hi folks, I was able to track down the problems I had with xemacs / ESS / cygwin / MS W2000 to the file "essd-r.el" in the ESS 5.1.18 package. ESS seems to think, it is running in a complete Unix environment and sends the wrong command line option ("--no-readline" instead of "--ess") to the MS W2000 executable "Rterm.exe". My temporary patch looks

Hi, I want to do a biexponential Fit, i.e. y ~ A1*exp(k1*x) + A2*exp(k2*x) Is this possible? I tried nls() but it stopped with several (different) errors. I'm using y and x as simple vectors and the formula for nls() exactly as mentioned above. Thanks a lot! Jonas

Hi everyone, I'm trying to perform a bi exponential Fit with the package NLS. the plinear algorithm seems to be a good choice see: p<-3000 q<-1000 a<--0.03 b<--0.02 t<-seq(0:144);t y<-p*exp(a*t) + q*exp(b*t)+rnorm(t,sd=0.3*(p* exp(a*t) + q*exp(b*t))) fittA <- nls(y~cbind(exp(a*t), exp(b*t)), algorithm="plinear",start=list(a=-.1, b=-0.2), data=list(y=y, t=t),

Hi there, as part of a 2 x 2 contingency table analysis I would like to estimate conditional probabilities (success rates) in a Bernoulli experiment. In particular I want to test a null hypothesis p <= p0 versus the alternative hypothesis p > p0. As far as I understand the subject, there are UMPU tests for these types of hypotheses. Now I know about R's "binom.test" but the

Hello, please excuse me if this is not the appropriate mailing list, but I'll give it a try! I recently switched from using Emacs 20.7.1 to XEmacs 21.4, both on cygwin / MS W2K. On Emacs the ESS 5.1.17 was working well, but now, when calling R from within XEmacs, it first asks for the working directory, then says "R process is not running". The ESS buffer holds the

R users, I intention is to calculate some summary statistics across factor levels. I know that in Hmisc package there is a summary function which produces neat summary statistics when using "cross" option. I would like to produce similar output with N and Missing columns but produce a data.frame. Is there any built-in function for that? #example data install.packages("Hmisc")

Full_Name: Kevin Wright Version: 1.4 OS: Windows 95 Submission from: (NULL) (170.54.59.160) Note: This was the 1.4 build for Windows that Brian Ripley made available. The first example in the help for reshape doesn't work for me. When I cut and paste, this is what happens: > data(Indometh,package="nls") > summary(Indometh) Subject time conc

No, since I'd like to test null: p <= p0 alternative: p > p0. and my understanding is that binom.test tests null: p = p0 (can only be a "simple" null hypothesis according to help(binom.test)) alternative: p > p0 (or p < p0 or p != p0). Thanks, Mirko. > -----Urspr?ngliche Nachricht----- > Von: Douglas Bates [mailto:bates at stat.wisc.edu] >

Given a data frame of a categorical variable and two continuous variables, I would like to display one continuous variable against the other for each value of the categorical variable, all superimposed on the same plot. For example: data(Indometh); str(Indometh) Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame': 66 obs. of 3

Hi All, I am currently preparing some form of slideshow introducing R and its capabilities for some colleagues. The thing will be about 30 mins, and I'd like to have some "pretty pictures" and some "amazing facts" (I'm trying to sell, obviously :)). Can I ask if it's possible to easily retrieve a gross figure of the number of functions in R considering the

*Respected Sir, I have a simple question regarding plots of time series in R. I have to plot "conc" against "time" **for each individual and display in the same panel for the in-built dataset "Indometh" in R. * ***I have six time series, say subject1.ts, subject2.ts, ............., subject6.ts. The observations are taken at an interval of 0.25 hr. All of the series

Hi all, I have a matrix of 474 rows (samples) with 565 columns (variables). each of the 474 samples belong to one of 120 groups, with the groupings as a column in the above matrix. For example, the group column would be: 1 1 1 2 2 2 . . . 120 120 I want to randomly select one from each group. Not all the groups have the same number of samples, some have 4, some 3 etc. Is there a function to