This section introduces the algorithms implemented in Loglet Lab for fitting logistics with arbitrary numbers of components to a data set. We include examples using hypothetical and historical data, as well as descriptions of the corresponding operations in Loglet Lab.
Because growth over time of either organisms or the diffusion of some other variable such as number of cars or length of canals is the usual subject of loglet analysis, vector notation is convenient to represent the data values, the parameters, and the fitted Loglet model.