similar to: Non linear fitting of 2 distributions with shared parameters

Oups, I sent the email by error, as I was still writing my reply… Spencer, Le 22-avr.-09 à 03:33, spencerg a écrit : > Is your first model a special case of the second with eta1 = 0? > If yes, what about using 2*log(likelihood ratio) being approximately > chi-square? Yes, the first model is a special case of the second with eta1=0… Could you give me more explanation about

Hi, I'm using a non linear model to fit experimental survival curves. This model describes the fraction of "still active" experiments as a function of time t as follows: f(t)=(1+exp(-etaD*cD)) / (1+exp(etaD(t-cD))) Moreover, when experiments are still active, they may change of state (from 0 to 1). But they may fall inactive before changing their state (their state still

Hi, I'm analyzing experimental results where two different events ("T1" and "T2") can occur or not during an experiment. I made my experiments with one factor ("Substrate") with two levels ("Sand" and "Clay"). I would like to know wether or not "Substrate" affects the occurrence probability of the two events. Moreover, for each

Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope coefficients and sb1,b2 the pooled standard error of the slope (b) which can be calculated in R this way: > df1 <-

Hi, A have small technical question about the calculation of R-squared using lm(). In a study case with experimental values, it seems more logical to force the regression line to pass through origin with lm(y ~ x +0). However, R-squared values are higher in this case than when I compute the linear regression with lm(y ~ x). It seems to be surprising to me: is this result normal ? Is there

Dear R users Does anyone happen to know a function to fit a Gaussian mixture using *non-linear* constraints?between the parameters? (An EM the allows that will do the job obviously). Thank you in advance -- Jonathan Rosenblatt www.john-ros.com

Dear All, In the past I have often used minpack (http://bit.ly/zXVls3) relying on the Levenberg-Marquardt algorithm to perform non-linear fittings. However, I have always dealt with a function of a single variable. Is there any difference if the function depends on two variables? To fix the ideas, please consider the function f(R,N)=(a/(log(2*N))+b)*R+c*N^d, where a,b,c,d are fit parameters. For

Hello, I have trouble with a data set that comprises 2136 lines of 20 columns. I would like to do a hierarchical clustering and I tried the following: ages.hclust <- hclust(dist(ages, method="euclidean"), "ward") but I get the following error message: Error: cannot allocate vector of size 17797 Kb When I try to do the dist() alone first without the hclust(), I get the

kmeans and clara work great. Thank you for the tip. I have another question: Is it possible to weight the observations in a cluster analysis ? I haven't found any mention of this in the kmeans of clara help texts. Moritz Lennert Charg? de recherche IGEAT - ULB t?l: 32-2-650.65.16 fax: 32-2-650.50.92 email: mlennert at ulb.ac.be > On Wed, 25 Apr 2001, Moritz Lennert wrote: >

Hi All, I have a parameter that is bimodal, and I want to get some sort of linear model done with it results = some.linear.function(bimodal.param ~ factor1 + some other stuff, mydata) I want to see if factor 1 matters (it has 3 levels, of of which can be taken as baseline), i.e: summary(results) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.108522

Hello, I'm fitting linear mixed models to gene-expression data from microarrays, in a data set where 4608 genes are studied. For a sample of 5 subjects and for each gene we observe the expression level (Intensity) in four different tissues: N, Tp, Tx and M. I want to test whether the expression level is different accross tissues. Between-subject variability is modeled with a random

Dear R-help, After two days of search on the archive of your web-site, I found partial answers to the problem that I want to solve, but this is not concluent to me and I am thinking that perhaps someone could answer exactly my problem: I have a theoretical Model (for the prediction of some physical quantity) which can be seen as a linear Model with 2 parameters. On the other side the measured

Hello Everybody, I am running R 2.2.0 with Windows XP i am trying to fit nonlinear differential equation to data sets which looks like this: Week N C 0 1 1 1 5 6 2 6.2 12.2 3 59 71.2 4 39 110.2 5 38 148.2 6 44 192.2 7 20.4 212.6 8 19.4 232 9 34.2 266.2 10 35.4 301.6 and i need to fit these data to the following diff equation: dNdt=a*N-b*N*C, dCdt=N^2, Where a=birth rate, b=death rate and N=

Hi all! i'm trying to use nls for fitting my data. I wrote this code to find some minimum, but it fails, returning 0 every time..... and i can't figure out the problem... any advice? grid <- expand.grid(A0 = seq(1000,10000,1000), A1 = seq(0,2,0.1)) exp.approx <- function(x,A0,A1) { A0 * exp(- x*A1) } ss <- function(p) { sum((durata.h.freq -

Hi list, I have a little curve fitting problem. I would like to fit a sigmoid curve to my data using the following equation: f(x) = 1/(1 + exp(-(x-c)*b)) (or any other form for that matter) Where x is the distance/location within the dataframe, c is the shift of the curve across the dataframe and b is the steepness of the curve. I've been playing with glm() and glm.fit() but without

Dear R and S users, I've been working on fitting finite mixture of negative exponential distributions using maximum likelihood based on the example given in MASS. So far I had much success in fitting two components. The problem started when I tried to extend the procedure to fit three components. More specifically, likelihood = sum( ln(c1*exp(-x/lambda1)/lambda1 + c2*exp(-x/lambda2)/lambda2

Hello! I have a set of data of the form (x, y1, y2) where x is the independent variable and (y1, y2) is the response pair. The model is some messy non-linear function: (y1, y2) = f(x; param1, param2, ..., paramk) + (y1error, y2error) where the parameters param1, ..., paramk are to be estimated, and I'll assume the errors to be normal for sake of simplicity. If there were only one

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

A non-text attachment was scrubbed... Name: not available Type: text/enriched Size: 1284 bytes Desc: not available Url : https://stat.ethz.ch/pipermail/r-help/attachments/20020911/e68e2ecc/attachment.bin

dear list members, I apologize in advance for posting a second time, but probably after one week chances are, the first try went down the sink.. my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as expected, as is detailed in my original post below. any remarks appreciated. greetings