similar to: piecewise nls?

I have a list that looks like this ... > have <- list(a=7,b=3,c=1) > have $a [1] 7 $b [1] 3 $c [1] 1 and I want to have a simple way to change it to the following without re-typing the values ... > desire <- list(a=c(7,7),b=c(3,3),c=c(1,1)) > desire $a [1] 7 7 $b [1] 3 3 $c [1] 1 1 In other words, I need to create the list in desire from the list in have. In my

I used R and the quantreg package in a manuscript that is currently in the proofs stage. I cited both R and quantreg as suggested by citation() and noted the version of R and quantreg that I used in the main text as "All tests were computed with the R v2.9.0 statistical programming language (R Development Core 2008). Quantile regressions were conducted with the quantreg v4.27 package

I am attempting to create a plot with intervals "stretched" in the x-direction using plotCI() in the plotrix package. The same data provides an appropriate set of intervals when "stretched" in the y-direction but I only get a lower interval when "stretched" in the x-direction. The data are as follows mns <-

I am trying to construct a two-way table where, instead of printing the two-way frequencies in the table, I would like to print the values of a third variable that correspond to the frequencies. For example, the following is easily constructed in R > fact1 <- factor(sample(LETTERS[1:3],10,replace=TRUE)) > fact2 <- factor(sample(LETTERS[25:26],10,replace=TRUE)) > fact3

I am trying to define a general R function that has a function as the output that depends on the user's input arguments (this may make more sense by looking at the toy example below). My real use for this type of code is to allow a user to choose from many parameterizations of the same general model. My "issue" is that when I compile a package with this type of code in it I get a

Sorry for posting this twice, but I still have not solved this problem and am hoping for some assistance. I am attempting to write a function that is flexible enough to respond to the user providing a formula (with a data= argument) or not (similar to plot(x,y) versus plot(y~x,data=data)). I have found a method to work with this in a simple case but am having trouble determining how to

I am trying to use optim() to minimize a sum-of-squared deviations function based upon four parameters. The basic function is defined as ... SPsse <- function(par,B,CPE,SSE.only=TRUE) { n <- length(B) # get number of years of data B0 <- par["B0"] # isolate B0 parameter K <- par["K"]

I have found Sweave() to be great for producing PDF documents. I have been experimenting with RweaveHTML (from the R2HTML) package and have had moderate success. My main issue has been that I simply want the R output to be shown verbatim in the HTML document but RweaveHTML tends to convert most output to a table, for example. So, is there a way to force the RweaveHTML driver to simply provide

I am interested in testing two similar nls models to determine if the lines are statistically different when fitted with two different data sets; one corn, another soybean. I know I can do this in linear models by testing for interactions. See Introductory Statistics with R by Dallgaard p212-218 for an example. I have two different data sets I am comparing to lai. ci.re should have very

All - I have data: TL age 388 4 418 4 438 4 428 5 539 10 432 4 444 7 421 4 438 4 419 4 463 6 423 4 ... [truncated] and I'm trying to fit a simple Von Bertalanffy growth curve with program: #Creates a Von Bertalanffy growth model VonB=nls(TL~Linf*(1-exp(-k*(age-t0))), data=box5.4, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) #Scatterplot of the data plot(TL~age, data=box5.4,

Hello, Fitting a piecewise smooth curve to a set of points (and a piecewise linear function in particular) seems to be a recurring question on this list. Nevertheless, I was not able to find an answer to a question that bothers me. Suppose I have the following data set, and would want to fit it with a piecewise smooth curve, In this model data, one curve is valid for up to 3 and another one for

R Community, I've put together a website that I thought this mailing list might be interested in: http://www.r-cookbook.com It's a (free) community-driven content management system for R "recipes", or working examples. Some of the features of the site are code highlighting, recipe ratings, recipe comments, personal "recipe boxes" to save your favorite

Hi, I posted a question (bellow) a few weeks ago and had a reply (thanks Christian) that partly solves the problem, but I still would like to be able to restrict some of the independent variables in a nls model to be always >0, (is there a way to do it)?? Thanks, Angel >From: "Christian Ritz" <ritz at dina.kvl.dk> >To: "Angel -" <angel_lul at

Hi R experts, I'm trying to use nls() for a piecewise linear regression with the first slope constrained to 0. There are 10 data points and when it does converge the second slope is almost always over estimated for some reason. I have many sets of these 10-point datasets that I need to do. The following segment of code is an example, and sorry for the overly precise numbers, they are just

Hi there, I'm a PhD student investigating growth patterns in fish. I've been using the minpack.lm package to fit extended von Bertalanffy growth models that include explanatory covariates (temperature and density). I found the nls.lm comand a powerful tool to fit models with a lot of parameters. However, in order to select the best model over the possible candidates (without covariates,

I apologize if this comes across as confusing. I will try to explain my situation as best I can. I have R bootstrapping my growth data for fish. It's resampling my database of age and length data and then produces several new datasets for me. In this case, it's resampling my data to create three new datasets of age and length data. Here is my code with my original data called

Is there a function similar to pkgDepends() that returns the packages that a particular package imports or imports from? I believe I can get this information from the matrix returned from installed.packages() but something like pkgDepends() would be more convenient. I did not find anything useful in my search of the R-help archives (my search string may have been poorly chosen), my use of

Following an example on p 111 in 'Nonlinear Regression with R' by Ritz & Streibig, I have been fitting nls models using square brackets with the grouping variable inside. In their book is this example, in which 'state' is a factor indicating whether a treatment has been used or not: > Puromycin.m1 <- nls(rate ~ Vm[state] * + conc/(K[state] + conc), data = Puromycin,

Hello, I'm a beginner with R and it's the first time I'm using the R-help list... I hope I'm in the right place, if not: Sorry!! I need to do non linear regressions on a data set which columns are: "river.name" "Portata" "PTG.P" "PO4.P" "NT.N" "NH4.N" "NO3.N" "BOD5" "SiO2"

I'm fitting nonlinear functions to some growth data but I'm getting radically different results in R to another program (Prism). Furthermore the values from the other program give a better fit and seem more realistic. I think there is a problem with the results from the r nls function. The differences only occur with weighted data so I think I'm making a mistake in the weighting.