similar to: non-linear curve fitting

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

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 list, I'm looking for a function or a combination of functions to do panel plotting of mixed graph types with the same x axis. I would like to construct a panel with 3 stacked windows with on top a histogram, below that 2 cdf plots. They all have the same x axis value but different y axis values. Is it possible to construct something like that? I've looked into the lattice package

Hi list, I would like to normalize a matrix (two actually for comparison) using iterative proportional fitting. Using ipf() would be the easiest way to do this, however I can't get my head around the use of the function. More specifically, the margins settings... for a matrix: mat <- matrix(c(65,4,22,24,6,81,5,8,0,11,85,19,4,7,3,90),4,4) using fit <-

Dear list, I've been messing around with coding functions in R and it just won't make sense to me. Running my analysis by hand on command line is fine and works but because of the repetitive nature of the job I would like to code a function for it. My problem: I would like to read in data from a file in my current working dir. so my code would look like: myanalysis <-

Hi list, I'm working on time series of (bio)physical data explaining (or not) the net ecosystem exchange of a system (+_ CO2 in versus CO2 out balance). I decomposed the time series of the various explaining variable according to scale (wavelet decomposition). With the coefficients I got from the wavelet decomposition I applied a (multiple) regression, giving some expected results. The net

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 list, Is there a package that covers the evaluation of spatial statistics on images and not on point data? I've converted an image matrix to x, y coordinates and a measurement value but evaluation with the package spdep (not really designed for image data I suppose) is unworkable. Any suggestions? Regards, Koen

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

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

I've written a piece of code (see below) to do a wavelet image decomposition, during the evaluation of this code I would like to write the results of some calculations back to the R root directory. I used assign() to do so because the names should vary when going thrue a while() loop. For some unknown reason I get an error that says: Error in assign(varname[i], imwrImage) :

Hi to all This is the first time I am quoting a question and I hope, my question is not too basic... For the following data, I wish to draw a fitted curve. x <- c(123,129,141,144,144,145,149,150,158,159,163,174,183,187,242,248) y <- c(14.42,26.96,31.3,19.95,36.36,15.4,24.76,35.39,28.07,40.97,26.23,42.83,46.53,14.79,49.18,48.08) If I plot the data, it looks somehow that a logistic

Hi list, I have a question regarding post-hoc extraction of data from a two-way categorical anova. I have a categorical anova of this form: width ~ steepness + patchiness (4 steepness levels, 4 patchiness levels) This simple setup answers if for the widths I collected across different levels of steepness and patchiness significant differences can be found. Is there a way to look at these

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.

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

Dear R Users: I have a list function as: Flat: y = 0 Linear: y = -(1.65/8)d Logistic: y = 0.015 - 1.73/{1+exp[1.2(4-d)]} Umbrella: y= -(1.65/3)d + (1.65/36)d^2 Emax: y = -1.81d/(0.79+d) Sigmoid Emax: y = -1.70d^5/(4^5+d^5) And want draw the figure as attached (those material are extracted from a paper). Could anyone give me a sample code to do this? Thanks

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- 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:

Hello, I am doing several self-starting growth functions for using with nls(). When I list the self-starting functions included in nls library, for instance, SSlogis, there is: > SSlogis function (input, Asym, xmid, scal) ... <environment: 03476D20> attr(,"class") [1] "selfStart" What is this <environment: 03476D20> instruction? By using deriv() and then

I'd like to understand better the sysadmin aspects of running a wiki. I don't have, and don't intend to at this stage, a web server, but I do have spare capacity on my LAN server box, where I'd like to install MediaWiki. I found this article: http://docs.fedoraproject.org/documentation-guide/en_US/sn-sandbox-setup.html Would it be straightforward to follow those