similar to: Moving window regressions - how can I improve this code?

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

Dear Group: I have been trying to write an R function within which the function groupedData() would be used. The following is a sample program: ####################### sid<-rep(1:6,times=2) time<-c(1:12) trt<-rep(letters[1:3],times=4) tem<-data.frame(sid,time,trt) test1.fun<- function(dat=dat) { groupedData(time ~ trt|sid, data=dat) } test1.fun(dat=tem)

Last week I posted a question concerning the mCall function, which is used to create self-starting functions and is described in the book by Pinheiro, J.C. and Bates, D.M. (Mixed-effects models in S and S-PLUS). On page 345 one finds the following call: xy<-sortedXyData(mCall[["x"]], LHS,data) It is necessary to replace the "x" in the call to mCall by the actual

Hi, I have great problems with my work in R. I look for to model the growth of fish. I have "Longitudinal data", a serie of repeated measures for each individual. Using the corresponding packages "nlme" in R. I treat to fit to the data different growth functions, wich were entered by me. Unfortunately for no it was arrived at the convergence, several error messages appeared. I

Dear All, I need to sum a column from another dataframe based on the row values of one dataframe. I am stuck in a loop trying to accomplish it and at current speed it will take more than 80 hours to complete. Needless to say I am looking for a more elegant/quicker solution. Really need some help here. Here is the issue: I have a dataframe CALL (the dput of head is given below) which has close to

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

Hello, list, I am making a self-starting nonlinear function to model the relation of tree height (H) and diameter (D) in a forest stand. The function I am trying is H=a*exp[b*(D+5.8)^(-c)]. To calculate the initial estimates of the parameters, I linearized the formula by taking logarithms and fixing the parameter c=1. Then I calculated the initial estimates of a and b using lm() on the

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 not certain how nlme works so I followed an example from the web ( http://www.menne-biomed.de/gastempt/gastempt1.html). I was able to successfully reproduce the example. However, when I modified my the example to use my data and with my formula, I get a set of errors having to do with the log() function. I get 10 of them (all exactly the same) and there are 10 levels in my factor variable.

I have encountered a strange behavior of the str function - it seems to modify the object that is displayed. Probably I'm using something unsupported (objects consisting just of an external reference), but still I'm curious as of why this happens. I create (in C code) EXTPTRSXP and associate a class to it via SET_CLASS. Such objects works fine until it's passed to str as the

Hi Everyone, I posted this a couple of weeks ago with no responses. My interface (via gmane) seemed a bit flakey at the time, so I'm venturing to repost with some additional information. I'm trying to write selfStart non-linear models for use with nls. In these models some combinations of parameter values are illegal; the function value is undefined. That's OK when calling the

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

Using formulas constructed from strings only partially works for me in lme: library(nlme) data(Orthodont) fm2<-lme(as.formula("distance~age"),data=Orthodont,random=~1|Subject) summary(fm2) # works augPred(fm2) # fails #Error in inherits(object, "formula") : #Argument "object" is missing, with no default I assume that my use of as.formula is wrong, but

Hi Everyone, I'm trying to write selfStart non-linear models for use with nls. In these models some combinations of parameter values are illegal; the function value is undefined. That's OK when calling the function directly [e.g. SSmodel(x, pars...)]; I return an appropriate non-value such as NA or Inf. However, when called from nls [e.g. nls(y~SSmodel(x, pars...), ...)] those

I'm trying to write a function that has a dataframe as an argument. Within the function, I call groupedData to create a grouped-data object from the dataframe argument. However, the groupedData function doesn't seem to see the data. I'm guessing it's getting the argument name rather than its value, but I'm not positive. My system: R 1.3.1 on WinNT 4.0 (Pentium II), nlme

I am trying to fit a curve to a cumulative mortality curve (logistic) where y is the cumulative proportion of mortalities, and t is the time in hours (see below). Asym. at 0 and 1 > y [1] 0.00000000 0.04853859 0.08303777 0.15201970 0.40995074 0.46444992 0.62862069 0.95885057 1.00000000 [10] 1.00000000 1.00000000 > t [1] 0 13 20 24 37 42 48 61 72 86 90 I tried to find starting values for

Dear all, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

I would like to be able, inside a function, to create a new function, and use it as part of a formula as an argument to, say, gnls or nlme. for example: MyTop <- function(data=dta) { Cexp <- function(dose,A,B,m){...} Model <- as.formula(paste("y","~ Cexp(",paste(formals(Cexp),collapse =", "),")")) MyCall <-

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),

Add PowerPC 64bit little endian support. Signed-off-by: Anton Blanchard <anton at samba.org> --- Makefile | 7 +++++-- usr/klibc/arch/ppc64/MCONFIG | 4 ++-- usr/klibc/arch/ppc64/crt0.S | 34 ++++++++++++++++++++----------- usr/klibc/arch/ppc64/setjmp.S | 40 ++++++++++++++++++++++++++---------- usr/klibc/arch/ppc64/sysstub.ph | 45