# Gauss2: Generated data In NISTnls: Nonlinear least squares examples from NIST

## Description

The `Gauss2` data frame has 250 rows and 2 columns giving

## Format

This data frame contains the following columns:

y

A numeric vector of generated response values.

x

A numeric vector of generated input values.

## Details

The data are two slightly-blended Gaussians on a decaying exponential baseline plus normally distributed zero-mean noise with variance = 6.25.

## Source

Rust, B., NIST (1996)

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```Try <- function(expr) if (!inherits(val <- try(expr), "try-error")) val plot(y ~ x, data = Gauss2) Try(fm1 <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss2, trace = TRUE, start = c(b1 = 96, b2 = 0.009, b3 = 103, b4 = 106, b5 = 18, b6 = 72, b7 = 151, b8 = 18))) Try(fm1a <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss2, trace = TRUE, start = c(b1 = 96, b2 = 0.009, b3 = 103, b4 = 106, b5 = 18, b6 = 72, b7 = 151, b8 = 18), alg = "port")) Try(fm2 <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss2, trace = TRUE, start = c(b1 = 98, b2 = 0.0105, b3 = 103, b4 = 105, b5 = 20, b6 = 73, b7 = 150, b8 = 20))) Try(fm2a <- nls(y ~ b1*exp( -b2*x ) + b3*exp( -(x-b4)**2 / b5**2 ) + b6*exp( -(x-b7)**2 / b8**2 ), data = Gauss2, trace = TRUE, start = c(b1 = 98, b2 = 0.0105, b3 = 103, b4 = 105, b5 = 20, b6 = 73, b7 = 150, b8 = 20), alg = "port")) Try(fm3 <- nls(y ~ cbind(exp(-b2*x), exp(-(x-b4)**2/b5**2), exp(-(x-b7)**2/b8**2)), data = Gauss2, trace = TRUE, start = c(b2 = 0.009, b4 = 106, b5 = 18, b7 = 151, b8 = 18), algorithm = "plinear")) Try(fm4 <- nls(y ~ cbind(exp(-b2*x), exp(-(x-b4)**2/b5**2), exp(-(x-b7)**2/b8**2)), data = Gauss2, trace = TRUE, start = c(b2 = 0.0105, b4 = 105, b5 = 20, b7 = 150, b8 = 20), algorithm = "plinear")) ```

NISTnls documentation built on May 29, 2017, 3:49 p.m.