R Graphical Manual

Last data update: 2014.03.03

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nlsLM (Package: minpack.lm) : Standard 'nls' framework that uses 'nls.lm' for fitting

nlsLM is a modified version of nls that uses nls.lm for fitting. Since an object of class 'nls' is returned, all generic functions such as anova, coef, confint, deviance, df.residual, fitted, formula, logLik, predict, print, profile, residuals, summary, update, vcov and weights are applicable.

● Data Source: CranContrib
● Keywords: nonlinear, optimize, regression
● Alias: nlsLM
● 0 images

wfct (Package: minpack.lm) : Weighting function that can be supplied to the code{weights

wfct can be supplied to the weights argument of nlsLM or nls, and facilitates specification of weighting schemes.

● Data Source: CranContrib
● Keywords: nonlinear, optimize, regression
● Alias: wfct
● 0 images

nls.lm.control (Package: minpack.lm) : Control various aspects of the Levenberg-Marquardt algorithm

Allow the user to set some characteristics Levenberg-Marquardt nonlinear least squares algorithm implemented in nls.lm.

● Data Source: CranContrib
● Keywords: nonlinear, optimize, regression
● Alias: nls.lm.control
● 0 images

nls.lm (Package: minpack.lm) : Addresses NLS problems with the Levenberg-Marquardt algorithm

The purpose of nls.lm is to minimize the sum square of the vector returned by the function fn, by a modification of the Levenberg-Marquardt algorithm. The user may also provide a function jac which calculates the Jacobian.

● Data Source: CranContrib
● Keywords: nonlinear, optimize, regression
● Alias: nls.lm
● 0 images