gLVlinearRegression: Parameter estimation of algebraic linear discrete gLV model

In lkshrsch/gLVInterNetworks: Inference of interaction networks based on generalised Lotka Volterra dynamics

Description

Given multivariatic time series data, this function fits a linear and discrete generalized Lotka Volterra model of the form \delta x_i = \alpha_i + \sum\beta_ij * x_j

Usage

gLVlinearRegression(data, regularization = FALSE, alpha = 0)

Arguments

data	Matrix or table containing time series of measurements in longitudinal form where first column corresponds to the time points and subsequent columns correspond to each model variable
regularization	Boolean flag if regularization of the parameter matrix should be forced
alpha	Regularization parameter for the elastic net. It ranges from 0 (= Ridge regression) to 1 (= LASSO regression) with values in between corresponding to both L1 and L2 penalties weighted by alpha

Details

Some theory and formulas on elastic net

lkshrsch/gLVInterNetworks documentation built on May 21, 2019, 7:33 a.m.