Gnls: Generalized nonlinear least squares parameter estimates

In baccione-eawag/EawagSchoolTools: Miscellaneous Tools for Eawag Summer School in Environmental Systems Analysis

Description

Calculate the generalized least squares parameter estimates of a nonlinear model.

Usage

Gnls(model.name,y.obs,var.inv,par,x=list(),...)

Arguments

model.name	name of the function representing the model (note that this model requires the parameters to be specified explicitly in the function headers)
y.obs	observed data corresponding to model output
var.inv	inverse variance-covariance matrix of the multivariate normal distribution that characterizes the error model
par	named parameter vector with initial values passed to the model as separate arguments
x	list of named inputs passed to the model
...	additional arguments passed to nls

Value

Returns a list containing:

model.name	name of the function representing the model
par	named parameter vector with parameter estimates
y.obs	observed data corresponding to model output
y.det	deterministic model results corresponding to the estimated parameters
resid	residuals
var.inv	inverse variance-covariance matrix of the multivariate normal distribution that characterizes the error model
x	input object passed as first argument to the model
res.nls	results from application of the nls function

Author(s)

Peter Reichert <[e-mail]>

See Also

Gnls, Gnls.diag, Gnls.test, Gnls.predict, Confint

baccione-eawag/EawagSchoolTools documentation built on Dec. 19, 2021, 6:38 a.m.