best-fit-curve

I am trying to find a trend in several datasets. The trends involve finding the best fit line, but if i imagine the procedure would not be too different for any other model (just possibly more time consuming). There are 3 conceivable scenarios: All good data where all the data fits a single trend with a low variability All bad data where all or most of the data exhibits tremendous variability and the entire dataset must be discarded. Partial good data where some of the data may be good while the rest needs to be discarded. If the net percentage of data with extreme variability is too high then

I've been reading about a few methods to fit a circle to data (like this ). I would like to see how the methods work on real data and thought of using R for this. I tried searching rseek for packages that can help with this but came up with nothing useful. So, are there packages that help to easily compute the best fit circle for a given data set (similar to how lm() will fit a linear model to a data set)? Otherwise, how might one perform such a task in R? Here's a fairly naive implementation of a function that minimises SS(a,b,r) from that paper: fitSS <- function(xy, a0=mean(xy[,1]), b0=mean

I am trying to fit a power-law function, and in order to find the best fit parameter. However, I find that if the initial guess of parameter is different, the "best fit" output is different. Unless I find the right initial guess, I can get the best optimizing, instead of local optimizing. Is there any way to find the **appropriate initial guess ** ????. My code is listed below. Please feel free make any input. Thanks! import numpy as np import pandas as pd from scipy.optimize import curve_fit import matplotlib.pyplot as plt %matplotlib inline # power law function def func_powerlaw(x,a,b,c):

Question: How do you fit a curve to points on a plane if they aren't single valued? For the example shown, how would one fit a curve (like the black one) to the noisy blue data? It's similar to spline smoothing, but I don't know the order of the data. Matlab would be preferred, but pseudocode is fine. Or a pointer to what the correct terminology for this problem is would be great. Thanks Your data look like a two-dimensional parametric plot of (x,y) as a function of some underlying parameter t . As such, it may be possible to do a least-squares fit of x(t) and y(t) if you can come up with a

This question already has an answer here: How to fit a smooth curve to my data in R? 8 answers Right now i have a large data set with temperature going up and down all the time. I want to smoothen my data and plot the best fit line with all the temperature, Here is the data: weather.data date mtemp 1 2008-01-01 12.9 2 2008-01-02 12.9 3 2008-01-03 14.5 4 2008-01-04 15.7 5 2008-01-05 17.0 6 2008-01-06 17.8 7 2008-01-07 20.2 8 2008-01-08 20.8 9 2008-01-09 21.4 10 2008-01-10 20.8 11 2008-01-11 21.4 12 2008-01-12 22.0 and so on............... til 2009 Dec 31 My current graph looks like this and my

I have a bunch of data, generally in the form a, b, c, ..., y where y = f(a, b, c...) Most of them are three and four variables, and have 10k - 10M records. My general assumption is that they are algebraic in nature, something like: y = P1 a^E1 + P2 b^E2 + P3 c^E3 Unfortunately, my last statistical analysis class was 20 years ago. What is the easiest way to get a good approximation of f? Open source tools with a very minimal learning curve (i.e. something where I could get a decent approximation in an hour or so) would be ideal. Thanks! David Z In case it's useful, here's a Numpy/Scipy (Python