curve-fitting

I am using R. I would like to use a specific equation to fit a curve to one of my data sets (attached) > dput(data) structure(list(Gossypol = c(1036.331811, 4171.427741, 6039.995102, 5909.068158, 4140.242559, 4854.985845, 6982.035521, 6132.876396, 948.2418407, 3618.448997, 3130.376482, 5113.942098, 1180.171957, 1500.863038, 4576.787021, 5629.979049, 3378.151945, 3589.187889, 2508.417927, 1989.576826, 5972.926124, 2867.610671, 450.7205451, 1120.955, 3470.09352, 3575.043632, 2952.931863, 349.0864019, 1013.807628, 910.8879471, 3743.331903, 3350.203452, 592.3403778, 1517.045807, 1504.491931, 3736

I have a set (at least 3) of curves (xy-data). For each curve the parameters E and T are constant but different. I'm searching the coefficients a,n and m for the best fit over all curves. y= x/E + (a/n+1)*T^(n+1)*x^m I tried curve_fit, but I have no idea how to get the parameters E and T into the function f (see curve_fit documentation). Furthermore I'm not sure if I understand xdata correctly. Doc says: An M-length sequence or an (k,M)-shaped array for functions with k predictors. What's a predictor? As ydata has only one dimension I obviously can't feed multiple curves into the routine. So

This is a follow-up to my previous couple of questions. Here's the code I'm playing with: import pandas as pd import matplotlib.pyplot as plt import scipy.stats as stats import numpy as np dictOne = {'Name':['First', 'Second', 'Third', 'Fourth', 'Fifth', 'Sixth', 'Seventh', 'Eighth', 'Ninth'], "A":[1, 2, -3, 4, 5, np.nan, 7, np.nan, 9], "B":[4, 5, 6, 5, 3, np.nan, 2, 9, 5], "C":[7, np.nan, 10, 5, 8, 6, 8, 2, 4]} df2 = pd.DataFrame(dictOne) column = 'B' df2[df2[column] > -999].hist(column, alpha = 0.5) param = stats.norm.fit(df2[column].dropna()) # Fit a normal distribution to the data print

I have the x and y coordinates of the following graph as two different lists s and k. If I want to determine the equation of the parabola that best encloses these points, how do I do it? Thanks! I am not sure how to link to an external data set. What is the best way to add your dataset to a question? This was the best I could find. So, I am putting up a sample data set here. I will be removing this later, though. K S 0 9.500000e-01 -6.500000e-01 1 8.500000e-01 -6.000000e-01 2 9.000000e-01 -6.000000e-01 3 9.500000e-01 -6.000000e-01 4 7.000000e-01 -5.500000e-01 5 7.500000e-01 -5.500000e-01 6 8

I want to use y=a^(b^x) to fit the data below, y <- c(1.0385, 1.0195, 1.0176, 1.0100, 1.0090, 1.0079, 1.0068, 1.0099, 1.0038) x <- c(3,4,5,6,7,8,9,10,11) data <- data.frame(x,y) When I use the non-linear least squares procedure, f <- function(x,a,b) {a^(b^x)} (m <- nls(y ~ f(x,a,b), data = data, start = c(a=1, b=0.5))) it produces an error: singular gradient matrix at initial parameter estimates. The result is roughly a = 1.1466, b = 0.6415, so there shouldn't be a problem with intial parameter estimates as I have defined them as a=1, b=0.5. I have read in other topics that it is convenient to

I have the following equation: I want to do a exponential curve fitting using MATLAB for the above equation, where y = f(u,a) . y is my output while (u,a) are my inputs. I want to find the coefficients A,B for a set of provided data. I know how to do this for simple polynomials by defining states. As an example, if states= (ones(size(u)), u u.^2) , this will give me L+Mu+Nu^2 , with L , M and N being regression coefficients. However, this is not the case for the above equation. How could I do this in MATLAB? rayryeng Building on what @eigenchris said, simply take the natural logarithm ( log in

I have two data sets obtained at different days. The results I got from two different data sets have similar shape but different values (see fig1). I am trying to match the second data set (x2,y2) to the first one (x1,y1) by multiply a constant A in x and B in y of the second data (see fig2). for example: data1: x1=[-0.3:0.06:2.1]'; y1=[ 0.001 0.001 0.004 0.014 0.052 0.166 0.330 0.416 0.340 0.247 0.194 0.197 0.237 0.330 0.428 0.542 0.669 0.767 0.855 0.900 0.913 0.904 0.873 0.811 0.765 0.694 0.631 0.585 0.514 0.449 0.398 0.351 0.309 0.273 0.233 0.211 0.182 0.154 0.137 0.117 0.101 ]'; data2 x2=[

I can't get this boxcar fit working...I get " OptimizeWarning: Covariance of the parameters could not be estimated category=OptimizeWarning)" , and the output coefficients are not improved beyond the starting guess. import numpy as np from scipy.optimize import curve_fit def box(x, *p): height, center, width = p return height*(center-width/2 < x)*(x < center+width/2) x = np.linspace(-5,5) y = (-2.5<x)*(x<2.5) + np.random.random(len(x))*.1 coeff, var_matrix = curve_fit(box, x, y, p0=[1,0,2]) The output coefficients are [ 1.04499699, 0., 2.], not that the third one has not even been changed. I