similar to: help on sigmoid curve fitting

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

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'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 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 <-

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

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 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 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

well if you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will

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

Hi! I have a problem of curve fitting. I use the following data : - vector of predictor data : 0 0.4 0.8 1.2 1.6 - vector of response data : 0.81954 0.64592 0.51247 0.42831 0.35371 I perform parametric fits using custom equations when I use this equation : y = yo + K *(1/(1+exp(-(a+b*ln(x))))) the fitting result is OK but when I use this more general equation : y = yo + K

Hello, I want to perform a nonlinear curve fit in order to obtain parameter estimates from experimentally determined data (y in dependence of x), but with an implicit function, thus, a function of which I cannot isolate y on the left-hand side of the equation. As far as I understand, the functions I found up to now (nls, optim) all work only for explicit functions. My data looks like

Hi lily, You can get fairly good starting values just by eyeballing the curves: plot(y) lines(supsmu(1:20,y)) lines(0.6*cos((1:20)/3+0.6*pi)+17.2) Jim On Wed, Jun 21, 2017 at 9:17 AM, lily li <chocold12 at gmail.com> wrote: > Hi R users, > > I have a question about fitting a cosine curve. I don't know how to set the > approximate starting values. Besides, does the method

Hi I'd like to fit an asymmetrical curve function to some physiological data. I've been told a weibull curve is a good place to start, but I'm having trouble specifying and fitting the function with nls and was wondering if someone could help. After some reading, I think the function specification I want is y=c*(x/a)^(b-1)*e^(-(x/a)^b) (from

Hello, I'm trying to fit a logistic curve to data but I'm having a hard time discovering how. Every tutorial I've come across either assumes the logistic curve has 0<y<1 or assumes I have multiple categories of data I simply have two vectors, a and b, of equal length with no missing data, and I suspect they follow a logistic curve. The vectors are a<-c(39609, 39643,

Hi all, I have just got going with R and think it is really nice however, as far as I can see, it can't do what I got it for: I have some output from a computer simulation of mutating genes represented by a biological statistic - so the graph looks roughly like the top of: x = y^2 - with oscillations around the general curve. If I use scatter.smooth() I get a nice curve representing

Hello everyone, I am looking into time series data compression at the moment. The idea is to fit a curve on a time series of n points so that the maximum deviation on any of the points is not greater than a given threshold. In other words, none of the values that the curve takes at the points where the time series is defined, should be "further away" than a certain threshold from the

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

I am new to R and am trying to fit a survival curve with a weibull hazard function to a set of data giving the probability of survival to age x, given the year of birth, in the form: Probability of survival: Birth year 1980 1981 ... 2003 .2 0.90 0.89 ... 0.87 1 0.80 0.81 ... 0.79 age 2 0.75 0.74 ... 0.73 3 0.70 0.69 ... 0.68 5 0.50 0.49 ... 0.43 10 0.30 0.31 ... 0.26 I would like to be

Working through the R archives and webspace, I've mostly proved to myself that I don't know enough about what statisticians call "Curve Fitting" to even begin translating the basics. I'm a sysadmin, and have collected a variety of measurements of my systems, and I can draw pretty pictures in R showing what has happened. People are happy, customers feel empowered. Whee!