similar to: glm family=binomial logistic sigmoid curve problem

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

I thought the point of adjusting the R^2 for degrees of freedom is to allow comparisons about goodness of fit between similar models with different numbers of data points. Someone has suggested to me off-list that this might not be the case. Is an ADJUSTED R^2 for a four-parameter, five-point model reliably comparable to the adjusted R^2 of a four-parameter, 100-point model? If such values

Hi list, I was wondering how I should go about fitting a sigmoid curve to a dataset. More specifically how I estimate parameters a and b in the following equation: 1 / 1+exp(-(x-a)*b) with b the steepness of the sigmoid curve and a the shift of the center of the sigmoid curve relative to the center of your dataframe. The fit is in function of x, the location within the input vector and y, an

Dear R helpers, I am not a statistician and right now struggling with Richards curve. Wikipedia says (http://en.wikipedia.org/wiki/Generalised_logistic_function) The "generalized logistic curve or function", also known as Richard's curve is a widely-used and flexible sigmoid function for growth modelling, extending the well-known logistic curve. Now I am confused and will like to

How can ordinary polynomial coefficients be calculated from an orthogonal polynomial fit? I'm trying to do something like find a,b,c,d from lm(billions ~ a+b*decade+c*decade^2+d*decade^3) but that gives: "Error in eval(expr, envir, enclos) : Object "a" not found" > decade <- c(1950, 1960, 1970, 1980, 1990) > billions <- c(3.5, 5, 7.5, 13, 40) > #

Dear R experts: I need to get this plot, but also with 95% confidence interval bands: hour <- c(1, 2, 3, 4, 5, 6) millivolts <- c(3.5, 5, 7.5, 13, 40, 58) plot(hour, millivolts, xlim=c(1,10), ylim=c(0,1000)) pm <- lm(millivolts ~ poly(hour, 3)) curve(predict(pm, data.frame(hour=x)), add=TRUE) How can the 95% confidence interval band curves be plotted too? Sincerely,

Hi. guys, I am trying to write my own Stochastic Gradient Ascent for logistic regression in R. But it seems that I am having convergence problem. Am I doing anything wrong, or just the data is off? Here is my code in R - lbw <- read.table("http://www.biostat.jhsph.edu/~ririzarr/Teaching/754/lbw.dat" , header=TRUE) attach(lbw) lbw[1:2,] low age lwt race smoke ptl ht ui ftv

Hi there, I just don't find the solution on the following problem. :( Suppose I have a dataframe with two predictor variables (x1,x2) and one depend binary variable (y). How is it possible to define a glm object (family="binomial") with a user defined logistic function like p(y) = exp(a + c1*x1 + c2*x2) where c1,c2 are the coefficents which I define. So I would like to do no

Hi- I'm a biologist trying to figure out the growth rate of salamanders in different ponds. I collected individuals from various populations at different dates, and using the size and date collected, I want to figure out the growth curve of each population. My question is: How do I fit my data to a Gompertz function in R? Thank you so much! Sarah -- View this message in context:

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

Per http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2010 -- and http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2010:syrfr -- I am applying to mentor the "Symbolic Regression for R" (syrfr) package for the Google Summer of Code 2010. I propose the following test which an applicant would have to pass in order to qualify for the topic: 1. Describe each of the

Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between

Dear list, I'm trying to fit a chapman-richards equation to my data, only I cannot interpret the parameters a, b and d. I know that the parameter b denotes the asymptote, but for the others I couldn't figure out. But I do need to know this in order to set my starting values. Here's the model: modPoplar<- nls(Diameter ~ d*(1-exp(-b *Age))^a ,start=list(a=20,b=0.9,d=33)) I attached

I am trying to write a function that fits a sigmoid given a X and Y vector guessing the start parameters. I use nls. What I did (enclosed) seems to work well with many data points but if I want to fit small vectors like : pressure <- c(5,15,9,35,45) gas <- c(1000,2000,3000,4000,5000) it do not work. The help page says that it do no not work on zero residual data. Massimo Cressoni

Hi everyone, I would like to fit a predictive model to my data in order to compare absorbance readings to a toxin standard. This data was obtained by exposing red blood cells to different toxin concentrations, which lead to the lysis of the red blood cells, increasing the absorbance (hemoglobin is freed). The data has a sigmoid shape (see below), so I thought about fitting a logistic model to the

Hi, is there any package (or source code snippet) that will evaluate the Frechet distance for curves represented as sets of points? Searching around only threw up references to a Frechet distribution. Thanks, ------------------------------------------------------------------- Rajarshi Guha <rxg218 at psu.edu> <http://jijo.cjb.net> GPG Fingerprint: 0CCA 8EE2 2EEB 25E2 AB04 06F7 1BB9

> Date: Tue, 30 Mar 2010 19:52:42 +0400 > From: Max Lapshin <max.lapshin at gmail.com> > Subject: Re: [Speex-dev] Speex in flash player: how to work with? > > _microphone = Microphone.getMicrophone(); > _microphone.setSilenceLevel(0); > _microphone.codec = SoundCodec.SPEEX; > _microphone.encodeQuality = 10; > _microphone.rate = 16; > _microphone.framesPerPacket

hello mailing list! i still consider myself an R beginner, so please bear with me if my questions seems strange. i'm in the field of biology, and have done consecutive hydraulic conductivity measurements in three parallels ("Sample"), resulting in three sets of conductivity values ("PLC" for percent loss of conductivity, relative to 100%) at multiple pressures

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

Hi, I want to fit some psychophysical data with cumulative gaussians. There is quite a convenient toolbox for matlab called 'psignifit' (formerly known as 'psychofit'). It allows the lower bound of the sigmoid to vary slightly from zero, aswell as the upper bound to vary from one. with these two free parameters, the fitted function is less sensitive to noisy data and outliers.