nlmrt: Functions for Nonlinear Least Squares Solutions
Version 2016.3.2

Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.

AuthorJohn C. Nash [aut, cre]
Date of publication2016-03-04 23:57:39
MaintainerJohn C. Nash <nashjc@uottawa.ca>
LicenseGPL-2
Version2016.3.2
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("nlmrt")

Getting started

Package overview

Popular man pages

coef.nlmrt: Output model coefficients for nlmrt object.
model2grfun: Generate a gradient function from a nonlinear model...
model2resfun: Generate a residual function from a nonlinear model...
modgr: Compute gradient from residuals and Jacobian.
nlfb: Nash variant of Marquardt nonlinear least squares solution...
summary.nlmrt: Summary output for nlmrt object.
wrapnls: Nash variant of Marquardt nonlinear least squares solution...
See all...

All man pages Function index File listing

Man pages

coef.nlmrt: Output model coefficients for nlmrt object.
model2grfun: Generate a gradient function from a nonlinear model...
model2jacfun: Generate a Jacobian matrix function from a nonlinear model...
model2resfun: Generate a residual function from a nonlinear model...
model2ssfun: Generate a sum of squares objective function from a nonlinear...
modgr: Compute gradient from residuals and Jacobian.
modss: Compute gradient from residuals and Jacobian.
nlfb: Nash variant of Marquardt nonlinear least squares solution...
nlmrt-package: Tools for solving nonlinear least squares problems. UNDER...
nlxb: Nash variant of Marquardt nonlinear least squares solution...
print.nlmrt: Print method for an object of class nlmrt.
summary.nlmrt: Summary output for nlmrt object.
wrapnls: Nash variant of Marquardt nonlinear least squares solution...

Functions

Files

inst
inst/examples
inst/examples/meyers.R
inst/examples/kannan.R
inst/examples/gfire.R
inst/examples/pasture.R
inst/examples/baglivo.R
inst/examples/ratgambier.csv
inst/examples/bass2.csv
inst/examples/bass2.R
inst/examples/ratvirginia.csv
inst/examples/fungi.R
inst/examples/raturadia.csv
inst/examples/eshl.R
inst/examples/hs25.R
inst/examples/ratpurnong.csv
inst/examples/onion.R
inst/dev-codes
inst/dev-codes/model2ssfun.R
inst/dev-codes/nlfb.R.130709
inst/dev-codes/nlsmn0.Rd
inst/dev-codes/form2resfromHadley.txt
inst/dev-codes/summary.pg.R
inst/dev-codes/makeFun.R
inst/dev-codes/nlmrttests.R
inst/dev-codes/nt.R
inst/dev-codes/f2ftext.R
inst/dev-codes/f2f.R
inst/dev-codes/mod2res120429.zip
inst/dev-codes/mytxtfn.R
inst/dev-codes/ntst.R
inst/dev-codes/nlsmnc.R
inst/dev-codes/nlsmnc.Rd
inst/dev-codes/nlxb.R.130710
inst/dev-codes/nlsmnq.Rd
inst/dev-codes/nlsmn0b.R
inst/dev-codes/nlxbreloff.R
inst/dev-codes/nlsmnq.R
inst/dev-codes/model2resfun.R.save
inst/dev-codes/model2resfun.R.120425
inst/dev-codes/nlsmn0.R
inst/dev-codes/seboundsnlmrtx.R
inst/doc
inst/doc/nlmrt-vignette.R
inst/doc/nlmrt-vignette.Rnw
inst/doc/nlmrt-vignette.pdf
NAMESPACE
NEWS
R
R/nlxb.R
R/model2ssfun.R
R/nlmrt.R
R/modgr.R
R/model2jacfun.R
R/modss.R
R/nlfb.R
R/model2resfun.R
R/model2grfun.R
R/wrapnls.R
vignettes
vignettes/nlpd.bib
vignettes/nlmrt-vignette.Rnw
MD5
build
build/vignette.rds
DESCRIPTION
man
man/model2ssfun.Rd
man/print.nlmrt.Rd
man/nlmrt-package.Rd
man/model2grfun.Rd
man/model2jacfun.Rd
man/model2resfun.Rd
man/modss.Rd
man/modgr.Rd
man/nlfb.Rd
man/nlxb.Rd
man/summary.nlmrt.Rd
man/wrapnls.Rd
man/coef.nlmrt.Rd
nlmrt documentation built on May 19, 2017, 12:41 p.m.

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