curve-fitting

What functions do you use in R to fit a curve to your data and test how well that curve fits? What results are considered good? Dirk Eddelbuettel Just the first part of that question can fill entire books. Just some quick choices: lm() for standard linear models glm() for generalised linear models (eg for logistic regression) rlm() from package MASS for robust linear models lmrob() from package robustbase for robust linear models loess() for non-linear / non-parametric models Then there are domain-specific models as e.g. time series, micro-econometrics, mixed-effects and much more. Several of

Possible Duplicate: Fitting a density curve to a histogram in R best fitting curve from plot in R I have a set of data that when plotted in a histogram is a Gaussian distribution. I have read the data in from a .csv file that looks like this: "values" 1.29989 1.15652 1.27818 1.19699 1.28243 1.19433 1.10991 ... using data<-read.csv("~/peak.csv") ... so I create a histogram using hist(data$values) . I want to be able to fit a Gaussian to this histogram and compare the fitted function's sigma and mean to the sigma and mean calculated from the data. Everywhere that I have looked mentions nls and

For a few days I've been working on this problem and I'm stuck ... I have performed a number of Monte Carlo simulations in R which gives an output y for each input x and there is clearly some simple relationship between x and y, so I want to identify the formula and its parameters. But I can't seem to get a good overall fit for both the 'Low x' and 'High x' series, e.g. using a logarithm like this: dat = data.frame(x=x, y=y) fit = nls(y~a*log10(x)+b, data=dat, start=list(a=-0.8,b=-2), trace=TRUE) I have also tried to fit (log10(x), 10^y) instead, which gives a good fit but the reverse

I'm trying to fit a Boltzmann sigmoid 1/(1+exp((x-p1)/p2)) to this small experimental dataset: xdata <- c(-60,-50,-40,-30,-20,-10,-0,10) ydata <- c(0.04, 0.09, 0.38, 0.63, 0.79, 1, 0.83, 0.56) I know that it is pretty simple to do it. For example, using nls : fit <-nls(ydata ~ 1/(1+exp((xdata-p1)/p2)),start=list(p1=mean(xdata),p2=-5)) I get the following results: Formula: ydata ~ 1/(1 + exp((xdata - p1)/p2)) Parameters: Estimate Std. Error t value Pr(>|t|) p1 -33.671 4.755 -7.081 0.000398 *** p2 -10.336 4.312 -2.397 0.053490 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

I try to fit a function to my data using scipy.optimize.curvefit . Q=optimization.curve_fit(func,X,Y, x0,ERR) and it works well. However, now I am trying to use an asymmetric error and I have no idea how to do that - or even if it is possible. By asymmetric error I mean that the error is not for example: 3+-0.5 but 3 +0.6 -0.2 . So that ERR is an array with two columns. It would be great if somebody had an idea how to do that - or could me point to a different Python routine which might be able to do it. That a snippet of the code I am using - but I am not sure it makes it clearer: A=numpy

While trying to create an optimization algorithm, I had to put constraints on the curve fitting of my set. Here is my problem, I have an array : Z = [10.3, 10, 10.2, ...] L = [0, 20, 40, ...] I need to find a function that fits Z with condition on slope which is the derivative of the function I'm looking for. Suppose f is my function, f should fit Z and have a condition on f its derivative, it shouldnt exceed a special value. Are there any libraries in python that can help me achieve this task ? The COBYLA minimzer can handle such problems. In the following example a polynomial of degree 3 is

I am trying to get a correct way of fitting a beta distribution. It's not a real world problem i am just testing the effects of a few different methods, and in doing this something is puzzling me. Here is the python code I am working on, in which I tested 3 different approaches: 1>: fit using moments (sample mean and variance). 2>: fit by minimizing the negative log-likelihood (by using scipy.optimize.fmin()). 3>: simply call scipy.stats.beta.fit() from scipy.optimize import fmin from scipy.stats import beta from scipy.special import gamma as gammaf import matplotlib.pyplot as plt import numpy