# NISTO-package: NIST nonlinear regression problems recast as function... In NISTO: Nonlinear least squares and function minimization examples from NIST

## Description

The NIST nonlinear regression test problems from

http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml

are provided as data sets plus functions to perform the following calculations:

setup

in a function with form NAME.setup

test

in a function with form NAME.test

residuals

in a function with form NAME.res

sum of squares function

in a function with form NAME.f

gradient of the sum of squares

in a function with form NAME.g

Jacobian of the residuals

in a function with form NAME.jac

data

in a data frame with a given NAME

The problems are as follows:

`Chwirut1`: Ultrasonic calibration study 1

The `Chwirut1` data frame has 214 rows and 2 columns.

y

A numeric vector of ultrasonic response values

x

A numeric vector or metal distance values

These data are the result of a NIST study involving ultrasonic calibration. The response variable is ultrasonic response, and the predictor variable is metal distance.

Source: Chwirut, D., NIST (197?). Ultrasonic Reference Block Study.

`DanielWood`: Radiated energy

The `DanielWood` data frame has 6 rows and 2 columns giving the energy radiated from a carbon filament versus the absolute temperature of the filament.

y

A numeric vector of the energy radiated from a carbon filament lamp.

x

A numeric vector of the temperature of the filament (1000 K).

These data and model are described in Daniel and Wood (1980), and originally published in E.S.Keeping, "Introduction to Statistical Inference," Van Nostrand Company, Princeton, NJ, 1962, p. 354. The response variable is energy radiated from a carbon filament lamp per cm**2 per second, and the predictor variable is the absolute temperature of the filament in 1000 degrees Kelvin.

Source: Daniel, C. and F. S. Wood (1980). Fitting Equations to Data, Second Edition. New York, NY: John Wiley and Sons, pp. 428-431.

`Ratkowsky2`: Pasture yield data

The `Ratkowsky2` data frame has 9 rows and 2 columns.

y

A numeric vector of pasture yields.

x

A numeric vector of growing times.

This model and data are an example of fitting sigmoidal growth curves taken from Ratkowsky (1983). The response variable is pasture yield, and the predictor variable is growing time.

Source: Ratkowsky, D.A. (1983). Nonlinear Regression Modeling. New York, NY: Marcel Dekker, pp. 61 and 88.

NISTO documentation built on Nov. 24, 2016, 1:19 a.m.