similar to: Fitting difference models in R (nls, nlme)

I wish to fit a dynamical model in R and I am running in a problem that requires some of your wisdom to solve. For SAS users I am searching for the equivalent of the */retain/ *statement. For people that want to read complicated explanations to help me: I have a system of two equations written as difference equations here. To boil it down. I have a dataframe with three variables y, X1, X2

I am working on a two stage threshold quantile regression model in R, and my aim is to estimate the threshold of the reduced-form equation (call it rhohat), and the threshold of the structural equation (call it qhat), in two stages. On the first stage, i estimate rhohat by quantile regression and obtain the fitted values. I use these fitted values to estimate qhat on the second stage. The code is

I have a question about passing arguments to the function f that nlm minimizes. I have no problems if I do this: x<-seq(0,1,.1) y<-1.1*x + (1-1.1) + rnorm(length(x),0,.1) fn<-function(p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } out<-nlm(fn,p=1.5,hessian=TRUE) But I would like to define fn<-function(x,y,p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } so

first, apologies for so many posts yesterday and today. I am wrestling with nls() and nls2(). I have tried to whittle it down to a simple example that still has my problem, yet can be cut-and-pasted into R. here it is: library(nls2) options(digits=12); y= c(0.4334,0.3200,0.5848,0.6214,0.3890,0.5233,0.4753,0.2104,0.3240,0.2827,0.3847,0.5571,0.5432,0.1326,0.3481) x=

Hi R users Here is an example of my data (only a small part) Year Season A_Time R_Time O_Time All_Time AandR_Time A_number R_number O_number AandR_number all_number 1999 Winter 9.590741 35.312963 42.759524 23.228431 18.164815 18 9 7 27 34 2000 Winter 9.899537 47.945000 59.744444 20.301333 18.170290 36 10 3

> Hi R users > Here is an example of my data (only a small part) > > Year Season A_Time R_Time O_Time All_Time AandR_Time > A_number R_number O_number AandR_number all_number > 1999 Winter 9.590741 35.312963 42.759524 23.228431 18.164815 > 18 9 7 27 > 34 > 2000 Winter 9.899537 47.945000 59.744444 20.301333

Dear Colleagues, Our group is also working on implementing the use of R for pharmacokinetic compartmental analysis. Perhaps I have missed something, but > fit <- nls(noisy ~ lsoda(xstart, time, one.compartment.model, c(K1=0.5, k2=0.5)), + data=C1.lsoda, + start=list(K1=0.3, k2=0.7), + trace=T + ) Error in eval(as.name(varName), data) : Object

Dear List, I am having trouble with some R code I have written to perform Redundancy Analysis (RDA) on a matrix of species abundance data (Y) and a matrix of environmental data (X). RDA is a constrained form of PCA and can be thought of as a PCA of the fitted values of a regression of each variable in Y on all variables in X. For info, the first use of RDA is in: Rao, C.R, 1964. The use and

I am looking for R code to be able to fit a linear-linear piecewise model with person-specific changepoint. I have searched the web, but have not been able to locate any code. Below is my attempt at some code: chgpt = function(a1,a2,a3,gam,wave){ yht=numeric(10) y1=(wave <= gam)*(a1+(a2*wave)) y2=(wave > gam)*((a1+(a2-a3)*gam)+a3*wave) yhat=y1+y2 return(yht) } nl.dat <- nlme(y ~

I am running a 3-parameter nonlinear fit using the default Gauss-Newton method of nls. initialValues.L = list(b=4,d=0.04,t=180); fit.nls.L = nls( myModel.nlm , fData.L, start = initialValues.L, control = nls.control(warnOnly = TRUE), trace=T ); summary.nls.L = summary(fit.nls.L); I run the same analysis in SAS proc NLIN. proc nlin data=apples outest=a; parms b=4 d=.04 t=180; model Y =

I'm trying to use nls() to fit an exponential decay with an unknown offset in the time (independent variable). (Perhaps this is inherently very difficult?). > decay.pl <- nls (amp ~ expn(b0,b1,tau,t0,t), data = decay, + start = c(b0=1, b1=7.5, tau=3.5, t0=0.1), trace=T) Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates

Hi, I am using Splus to run a multiphase mixed-effects model. The quations of the models are as below: gf[ij]=b0[i]+b1[i]*age[ij]+b2[i]*max(0,(age[ij]-tau[i]))^2+e[ij] b0[i]=b00+e[i0] b1[i]=b10+e[i1] b2[i]=b20+e[i2] tau[i]=tau+e[i3] i: 1,2,...,100 subjects j: 1,2,...,6 occasions The main scripts of Splus is: simu1<-groupedData(gf~age|id) simu.nlme<-nlme(gf~(b0 + b1 * age + b2 *

Hi, Sorry to bother those who are not interested in the question. I am using R to run a multiphase mixed-effects model. I simulated a data set by using the following model. And now I want to use R to estimate the parameters and compare the results with the true values. The equations of the models are as below: gf[ij]=b0[i]+b1[i]*age[ij]+b2[i]*max(0,(age[ij]-tau[i]))+e[ij] b0[i]=b00+e[i0]

I'm trying to do a Hosmer-Lemeshow 'goodness of fit' test on my logistic regression model. I found some code here: http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html The R code is above is a little complicated for me but I'm having trouble with my answer: Hosmer-Lemeshow: p=0.6163585 le Cessie and Houwelingen test (Design library): p=0.2843620

Dear R Helpers, I try to write a small package that based on nlme however my code does not work. R always appears this message: Error in eval(expr, envir, enclos) : object "y" not found where y is the response variable. Please help me out! This is my code: require(nlme) AMPmixed<-function(y, x, S1=c("asymptotic","logistic"), random,data,

I am using nlm to maximize a likelihood function. When I call the likelihood function (garchLLH) via nlm however, nlm returns the wrong value of the function. When I test the likelihood function manually I get the correct answer. I'm probably doing something really stupid, maybe someone can point it out for me. ###############this is the function i am trying to minimize ############

I am relatively new to R and am looking for advice, ideas or both... I have a data set that consists of pathogen population sizes on individual plant units in an experimental field plot. However, in order to estimate the pathogen population sizes I had to destroy the plant unit and could not determine if that plant unit became diseased or to what extent it would have become diseased. I

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

I am trying to fit a nonlinear regression model to data. There are several predictor variables and 8 parameters. I will write the model as Y ~ Yhat(theta1,...,theta8) OK, I can do this using nls() - but "only just" as there are not as many observations as might be desired. Now the problem is that we have a factor "Site" and I want to include a corresponding error

Hi to everybody. I use the following routine (i found it in the internet) to compute the Hosmer-Lemeshow test in the framework of logistic regression. hosmerlemeshow = function(obj, g=10) { # first, check to see if we fed in the right kind of object stopifnot(family(obj)$family=="binomial" && family(obj)$link=="logit") y = obj$model[[1]] # the double bracket