nls2 (version 0.1-2)

nls2: Nonlinear Least Squares with Brute Force

Description

Determine the nonlinear least-squares estimates of the parameters of a nonlinear model.

Usage

nls2(formula, data = parent.frame(), start, control = nls.control(), 
        algorithm = c("default", "plinear", "port", "brute-force"), ..., 
	all = FALSE)

Arguments

formula

same as formula parameter in nls.

data

same as data parameter in nls.

start

same as start parameter in nls except that it may alternately be (1) a two row data frame in which case nls2 will start at each point on a grid chosen to have maxiter iterations (2) a data frame with more

control

same as control parameter in nls.

algorithm

same as algorithm parameter in nls with the addition of the "brute-force" method.

...

other arguments passed to nls.

all

if all is true then a list of nls objects is returned, one for each row in start; otherwise, only the one with least residual sum of squares is returned.

Details

Similar to nls except that start and algorithm have expanded values as indicated.

By using a data frame of starting values one can perform mulitple optimizations in one call returning the best one (or all of them if all = TRUE).

The "brute-force" algorithm just returns the nls object corresponding to the starting values. By using algorithm="brute-force" and a data frame of starting values one can get a brute force solution. This can then be fed as starting values into a subsequent invocation of nls2 (or nls).

See Also

nls.

Examples

richness <- c(44,36,31,39,38,26,37,33,34,48,25,22,44,5,9,13,17,15,21,10,16,22,
13,20,9,15,14,21,23,23,32,29,20,26,31,4,20,25,24,32,23,33,34,23,28,30,10,29,
40,10,8,12,13,14,56,47,44,37,27,17,32,31,26,23,31,34,37,32,26,37,28,38,35,27,
34,35,32,27,22,23,13,28,13,22,45,33,46,37,21,28,38,21,18,21,18,24,18,23,22,
38,40,52,31,38,15,21)

area <- c(26.22,20.45,128.68,117.24,19.61,295.21,31.83,30.36,13.57,60.47,
205.30,40.21,7.99,1.18,5.40,13.37,4.51,36.61,7.56,10.30,7.29,9.54,6.93,12.60,
2.43,18.89,15.03,14.49,28.46,36.03,38.52,45.16,58.27,67.13,92.33,1.17,
29.52,84.38,87.57,109.08,72.28,66.15,142.27,76.41,105.76,73.47,1.71,305.75,
325.78,3.71,6.48,19.26,3.69,6.27,1689.67,95.23,13.47,8.60,96.00,436.97,
472.78,441.01,467.24,1169.11,1309.10,1905.16,135.92,438.25,526.68,88.88,31.43,
21.22,640.88,14.09,28.91,103.38,178.99,120.76,161.15,137.38,158.31,179.36,
214.36,187.05,140.92,258.42,85.86,47.70,44.09,18.04,127.84,1694.32,34.27,
75.19,54.39,79.88,63.84,82.24,88.23,202.66,148.93,641.76,20.45,145.31,
27.52,30.70)

## Example 1

fo <- richness ~ Const + B * (area ^ A)
# use brute force to select best row in st

# pass our own set of starting values
st0 <- expand.grid(Const = seq(-100, 100, len = 4), 
	B = seq(-100, 100, len = 4), A = seq(-1, 1, len = 4))
mod0 <- nls2(fo, start = st0, algorithm = "brute-force")
mod0
# now use mod0 as starting value for optimization
nls2(fo, start = mod0) 

## Example 2

# pass a 2-row data frame and let nls2 calculate grid
st1 <- data.frame(Const = c(-100, 100), B = c(-100, 100), A = c(-1, 1))
mod1 <- nls2(fo, start = st1, algorithm = "brute-force")
mod1
nls2(fo, start = mod1)

## Example 3

# create same grid of starting values as in Example 2
# running an optimization from each one and picking the best
nls2(fo, start = st1, control = nls.control(warnOnly = TRUE))