Fitting growth models and goodness of fit

[paviaishu16]

Using Numpy and Scipy, one could fit the growth curve to different models. In this model we are expecting to fit the curve to Richards and Gompertz model. We can use the growth parameters obtained for this task.

Steps to Fit a Growth Model and Check Goodness of Fit:

• [ ] Predict Values based on the Growth model (Richards and Gompertz): Use the growth model with the growth parameter estimates to predict the values for the dependent variable based on the independent variable.

• [ ] Calculate Residuals: Compute the residuals, which are the differences between the observed values and the predicted values.

• [ ] Minimize Squared Differences: Use an optimization method (e.g., least squares) to adjust the model parameters to minimize the squared differences (sum of squared residuals) between predicted and observed values.

• [ ] Refit the Model: Refit the model using the optimized parameters and generate new predictions.

• [ ] Evaluate Goodness of Fit: Assess the goodness of fit using metrics such as: R-squared (𝑅^2 ) Root Mean Squared Error (RMSE) Akaike Information Criterion (AIC) Visual inspections of residuals and predicted vs. observed plots.

[gevhaz]

What should the output of this task be @paviaishu16?

R-squared, RMSE, AIC and plots for each well?

[paviaishu16]

Let us just consider this as a steps for the next issues! Fitting the model is the first part which is gompertz and richards model and get the plots with the predicted and actual data. And goodness of fit is the second part from which is decided by AIC