gLVnonlinearRegression: Parameter estimation through gradient search of continuous...

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

Usage

gLVnonlinearRegression(data, parms0 = NULL, ftol = 1e-8 , ptol = 1e-8, maxiter = 100, lowerbound = rep(-20,ncol(data$obs)*(ncol(data$obs)-1)), upperbound = rep(20,ncol(data$obs)*(ncol(data$obs)-1)), method = "Marq")

Arguments

data	Data input containing a time series of observations in longitudinal matrix form
parms0	Optional. Starting parameter vector. Default = Zero vector
ftol	Minimum change in the value of the objective function (sum of squared residuals) between two consecutive steps before stopping iterative optimization
ptol	Minimum change in the value of parameters being estimated between two consecutive steps before stopping iterative optimization
maxiter	Maximal number of iterations allowed before breaking the optimization algorithm
lowerbound	Numerical vector of equal length as parameter vector describing lower bound for constrained parameter search
upperbound	Numerical vector of equal length as parameter vector describing upper bound for constrained parameter search
method	Method used for optimization of the objective function. Default is "Marq" for Leverberg-Marquandt

Value

Parms	Estimated parameter matrix
SSR	The value of the sum of squared residuals of the found solution
residual_SD	The estimated standard deviation of the data stochasticity in respect to the solution found
SE	A matrix contatining the standard errors of each estimated parameter
residuals_t.test	The result of a t test with the null hypothesis of the residuals from the model solution being normally distributed with standard error 'residual_SD'
message	A text message related to the reason why the optimization algorithm stopped
obs	A matrix with the calculated abundances on each timestep according to the estimated parameters
Fit	The object returned by the optimization algorithm. It is a list containing further details of the optimization algorithm run
df	The degrees of freedom
quantitative	Only when validating against in Silico data: The ratio of estimated parameters that contain the original value within the 95% confidence interval given by their standard error (displayed in 'SE')
qualitative1	Only when validating against in Silico data: The ratio of correctly retrieved edges from the original interaction network
qualititative2	Only when validating against in Silico data: The ratio of correctly retrieved edges from the original interaction network after setting estimated parameters that are not significantly different from zero to zero (i.e. parameters that contain the zero in their 95% confidence interval as given by their standard error)

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