search for: sigmoidal

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

...rious 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: http://www.nabble.com/Fitting-data-to-a-sigmoidal-curve-tp20466784p20466784.html Sent from the R help mailing list archive at Nabble.com.

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

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

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 there, I don't have much experience with fitting at all and I'd like to get some advice how to use the "weights"-argument with nls correctly. I have created some data with a sigmoidal curve shape. Each y-Value was generated by the mean of three values. A standard deviation was calculated too. Now, I'd like to weight the data points respective to its standard deviation. If this is very high, the datapoint should be considered less important than a datapoint with a low st...

Hi everyone, I'm trying to perform the following regressions in order to compare linear vs. sigmoidal fit of the relationship between my dependent variable (y) and one explaining parameter (x2), both including the confounding effects of a third variable (x1): quasi-pois-lin <- glm(y ~ x1 + x2, family = quasipoisson(link="identity"), data=fit) quasi-pois-sig <- gnm(y ~ x1 + Mult(1,...

I'm trying to plot an extrapolated logistic sigmoid curve using glm(..., family=binomial) as follows, but neither the fitted() points or the predict()ed curve are plotting correctly: > year <- c(2003+(6/12), 2004+(2/12), 2004+(10/12), 2005+(4/12)) > percent <- c(0.31, 0.43, 0.47, 0.50) > plot(year, percent, xlim=c(2003, 2007), ylim=c(0, 1)) > lm <- lm(percent ~ year)

Hi I am struggling to fit a non-linear trend using the likfit function in geoR. Specifically I want a sigmoidal function, something like SSfpl in the nls package to fit the trend. But it seems trend.spatial in geoR only works with lm or glm type models. Any ideas how I can specify the model to calculate the kriging parameters using REML, including the parameters of a sigmoidal trend function (such as SSfpl...

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

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.

...lowing object(s) are masked from package:plotrix : plotCI The following object(s) are masked from package:stats : lowess 'qpcR' has been loaded. Please cite R and the following if used for a publication: Spiess AN, Feig C, Ritz Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry. BMC Bioinformatics 2008, 29:221 or Ritz C, Spiess AN. qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis. Bioinformatics 2008, 24:1549-1551 Newest versio...

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

...the parameter change? 3. Lagvalue. This might take care of the time lag component, but I am still struggling to understand (1) how lagvalue works and (2) how to integrate both the threshold and timer components. if ( D < D_T ) m<- ifelse(lagvalue(t-delay)<=0, 6.8, 10) end 4. Sigmoidal function. Someone suggested this to me earlier, but I am not sure how this would work. Is it possible to have a sigmoidal function that switches repeatedly between two states (as opposed to the characteristic S curve that transitions once from one state to another)? 5. Mathmematica has a WhenEvent...

Hi everyone I am trying to use grep in a for loop to compare a string value. It works if I use the actual index value but when I use the for loop index, it doesn't work. Any suggestions plz. Here is the code: data <- read.table(file="Sigmoid.csv", head=FALSE, sep=","); c1 <- data$V1 c2 <- data$V2 c3 <- data$V3 c1data <- data.frame(c1); c2data <-

Hi, I created a function for obtaining the normal cumulative distribution (I know all this already exists in R, I just wanted to verify my understanding of it). below is the code I came up with. cdf<-function(x) { erf<-function(x) { # approximation to the error function (erf) of the # normal cumulative distribution function # from Winitzki

...This might take care of the time lag component, but I am still > struggling to understand (1) how lagvalue works and (2) how to integrate > both the threshold and timer components. > > if ( D < D_T ) > m<- ifelse(lagvalue(t-delay)<=0, 6.8, 10) > end > > 4. Sigmoidal function. Someone suggested this to me earlier, but I am not > sure how this would work. Is it possible to have a sigmoidal function that > switches repeatedly between two states (as opposed to the characteristic S > curve that transitions once from one state to another)? > > 5. Mat...

Hi, I am trying to fit a sigmoid curve to some data with nls but I am getting into some trouble. Seems that the optimization method is getting down to some parameter estimates that make the equation unsolvable. This is an example: >growth<-data.frame(Time=c(5,7,9,11,13,15,17,19,21,23,25,27),BodyMass=c(45,85,125,210,300,485,570,700,830,940,1030,1120))

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

Hello, First of all I am french, so please forgive me, if there are some big language mistakes in my sentences. I think, it is the good mail address to send my question, if not please tell me and forgive me. I am working on a project, and I use the nnet library. Our customers do not want us to install R on their machine, so we just use R for making the training of our neurons network. This