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which happens to fit perfectly to the Michaelis-Menten equation: Resp = max_value * conc / (conc_value_at_half_max + conc) Even though it is something else entirely importantly, the response increases …

We have fitted a nonlinear function to observed data. The next step should be the assessment of the goodness of fit of this function (like $R^2$ for linear models). … (The distribution is nonlinear and has variable C as an input.) Assess goodness of fit of nonlinear distribution by comparing estimated to observed data. …

I was wondering how can I best fit nonlinear regression model to this data, using an R package. … How can I check if model is good fitted since $R^2$ value is not returned in most functions for nonlinear models? (source: tarchomin.pl) …

I have a reproducible example here with an attempt to use nls to fit a nonlinear function: y = ax/(b+x) + c Even when I set the starting values to be a good, known optimization, the nls call fails with …

I have some data that I have fitted to a Weibull distribution and now I want to calculate the goodness of fit. … Then I considered calculation the $R^2$ coefficient but then I also read that this can't be used to asses the goodness of a nonlinear fit. …

I am using nonlinear regression to fit data. Can I use central moments of residuals to estimate the quality of my fit? Especially 2nd central moment, third and fourth? … Is good fit characterized by zero higher order central moments? (I mean second, third and fourth order) …

I am struggling to find a concrete formula for the Hessian or Jacobian in respects to fitting parameters. I have implemented some fitting in Java using the Apache Common Maths package for the functi …

I'm attempting to fit this model: $P = C_0 + C_1*U^r$ Given known vectors of observations $P$ and $U$, I want to fit values for $C_0$, $C_1$ and $r$. How do I make this fit in R? …

I've been trying to find a way to fit a generalized nonlinear model in R, with little success. … sim binomial(n, pi)$$ The parameters being b1 and $\gamma$ The systematic component is the following: $$ pi = \frac{e^{b1*(conc - \gamma)}}{1+e^{b1*(conc - \gamma)}} $$ In SAS this model can be fit …

The curve was fit by nonlinear regression using the equation above to determine the $V_{max}$ and $K_m$. … More generally, what method can be used to derive the distribution of any parameter fit by nonlinear regression? …

I have a colleague that wants to fit a nonlinear model to the independent variables X (X is an n x k matrix) and the dependent variables Y (Y is an n x 1 matrix). … In the past, the nonlinear model between X1 and the T1 was fit and then the parameters fit in that first regression were fixed for the nonlinear regression between the X2 (note that this is a different …

I have parameter estimates fitting a particular nonlinear model for thousands of experimental cycles. My goal is to find a nice way to tag those cycles which didn't fit the model very well. … I plotted the histogram of RMSEs and found that they fit a log-normal distribution. However, I was informed that the proper distribution to use in this case is $\chi^2$. …

I have a nonlinear model $y=\Phi(f(x,a)) + \varepsilon$, where $\Phi$ is the cdf of the standard normal distribution and f is nonlinear (see below). … I would like to use this test to label a bad fit as bad and determine whether more data should be collected. …

Then I tried to use nonlinear regression using curve_fit in python to estimate the parameters. … Y_obs = np.random.poisson(Y_exp + abs(Y_exp.min()) + 0.01) # Initial guess for our parameters (V, phi, A) guess = [0.5, np.pi, 5.0] # Nonlinear regression: Fit the function to the data to get best fit …

Is nonlinear regression (always?) better than linear regression? How can I decide which model to use? … , is calculating AIC, BIC, and adjusted r-squared a good way to select the model between linear and nonlinear regressions? …