similar to: Non-linear Regression best-fit line

Hi All, Another nls related problem (for background, I'm migrating a complicated modelling package from S-plus to R). Below I've reduced this to the minimum necessary to demonstrate my problem (I think); the real situation is more complicated. Two similar selfStart functions, ssA and ssB. The 'initial' function for ssB modifies its arguments a little and then calls getInital

Hello. I am having a problem setting up a self-starting function for use in nonlinear regression (and eventually in the mixed model version). The function is a non-rectangular hyperbola - called "NRhyperbola" - which is used for fitting leaf photosynthetic rate to light intensity. It has one independent variable (Irr) and four parameters (theta, Am, alpha and Rd). I have created this

Hi, I have a program written in R which is good on the version 1.2, but for the fallowing versions of R, an error always is at the same place. That is at the level of the fallowing line: Sur<- getInitial(res2[m:M,2]~SSasymp(res2[m:M,1],Asymp,resp0,lrc),data=res2) Error in eval(expr,envir,enclos):numeric envir arg not of length one I don't know at all this langage for the instant.

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),

Hello, I'm having problems creating and using a selfStart model with nlme. Briefly, I've defined the model, a selfStart object, and then combined them to make a selfStart.default model. If I apply getInitial to the selfStart model, I get results. However, if I try usint it with nls or nlsList, these routines complain about a lack of initial conditions. If someone could point out

Hi everybody, I'm using the method described here to make a linear regression: http://www.apsnet.org/education/advancedplantpath/topics/Rmodules/Doc1/05_Nonlinear_regression.html > ## Input the data that include the variables time, plant ID, and severity > time <- c(seq(0,10),seq(0,10),seq(0,10)) > plant <- c(rep(1,11),rep(2,11),rep(3,11)) > > ## Severity

Hello. Following pages 342-347 of Pinheiro & Bates, I am trying to write a self-starting nonlinear function (a non-rectagular hyperbola) to be used in nonlinear least squares regression (and eventually for a mixed model). When I use the getInitial function for my self-starting function I get the following error message: > getInitial(photo~NRhyperbola(Irr,theta,Am,alpha,Rd),dat) Error

I'm trying to fit a Gompertz sigmoid as follows: x <- c(15, 16, 17, 18, 19) # arbitrary example data here; y <- c(0.1, 1.8, 2.2, 2.6, 2.9) # actual data is similar gm <- nls(y ~ a+b*exp(-exp(-c*(x-d))), start=c(a=?, b=?, c=?, d=?)) I have been unable to properly set the starting value '?'s. All of my guesses yield either a "singular gradient" error if they

Dear R-help, Here's a simple example of nonlinear curve fitting where nls seems to get the answer wrong on a very simple exponential fit (my R version 2.7.2). Look at this code below for a very basic curve fit using nls to fit to (a) a logarithmic and (b) an exponential curve. I did the fits using self-start functions and I compared the results with a more simple fit using a straight lm()

I am trying to fit a logistic model to my 10 year data (1999-2008) by year. Codes like below: Year <- c(1999: 2008) for(y in 1:length(year)) { file.input <- paste("C:\\", year[y], "\\data.csv", sep="") table <- read.csv(file=fileinput, header=TRUE, as.is=TRUE, na.strings=c("")) initial <- getInitial(percent ~ SSlogis(age, Asym,

Dear R-helpers, does anyone know of some package/function that can build a network from the functions that are implemented in a package, i.e. visualize the cross-references from one function to another in the same or some dependent package? An example would be a function like 'nls' on top of the hierarchy and then a network of nodes from the functions that are called within 'nls'

Hello. I am trying to fit a nonlinear mixed model involving 3 parameters. I have successfully made a self-starting function. getInitial() correctly outputs the initial estimates. I can also use the nlsList with this function to get the separate nonlinear fits by group. However, I get an error message when using the nlme function. Here is the relevent code:

Hi, I've been trying to work with the gnls() function in the "nlme" package. My decision to use gnls() was so that I could fit varPower and such to some of the data. However, in working with a small dataset, I've found that the results given by gnls() don't seem to make any sense and they differ substantially from those produced by nls(). I suspect that I am just

Hi Everyone, I seek guidance to avoid wasting a lot of time and doing things badly. Several times I've solved my problems, only to find that my solutions were clumsy and not robust. (see "nested" getInitial calls; variable scoping problems: Solved?? http://finzi.psych.upenn.edu/R/Rhelp02a/archive/139943.html for one truly horrible approach). I'm sure that I'm not the

Hello, I'm trying to do some non-linear regression with 2 cell types and 4 tissue type treatments using selfStart models Following Ritz and Streibig (2009), I wrote the following routines: ##Selfstart expDecayAndConstantInflowModel <- function(Tb0, time, aL, aN, T0){ exp(-time*aL)*(T0*aL+(-1+exp(time * aL))*Tb0 * aN)/aL } expDecayAndConstantInflowModelInit <- function(mCall, LHS,

Hi Everyone, I'd omitted the non-optional 'parameters' argument to selfStart. Making this change to SSbatch gives the same (successful) result from the two calls to nls. SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate coefficients as mean of each batch xy <- sortedXyData(mCall[["Batch"]],

Hi everyone, I thought that for a selfStart function, these two should be exactly equivalent > nls(Aform, DF) > nls(Aform, DF, start=getInitial(Aform, DF)) but in this example that is not the case in R (although it is in S-plus V6.2) ------------------------------ SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate

getInitializer returns the complete initializer of the global. My globals are complex nested structs, from which I want to extract e.g. one double datafield. Example: >From a struct Stuct having double,array(3xint),float fields, I could extract the second int value using the index sequence 0,1,1 with getGetElementPtr. This gives me a constantPointer, but I would need to get a constInt...

Hi Tim, I forgot to say that I try to do all this in the LLVM API, not IR. I read in a bc'ed program and try to edit it using the API. Alex Tim Northover wrote: >> But I can not succeed in using the getGetElementPtr result in >> constructing an initalizer for another global value (which expects a >> ConstantFP* and not a ConstantExpr*). > > The result of a

Hi all, I can get access to the data stored in globals as follows: GlobalVariable* pGvarAct=_set_a_valid_global_; const_ptr_indicesVec=_the_indexes_ llvm::Constant* pConst; pConst=ConstantExpr::getGetElementPtr(pGvarAct, const_ptr_indicesVec); and e.g. use this to repalece an argumrnt of an instruction like: I->setOperand(someArgumentIndex,pConst); However, getGetElementPtr returns a