MultiStart {Dark}

MultiStart

Description

Given a dark object, obj, this function repeatedly optimises the parameters in the vicinity of the seed array. The width of the search is dependent upon the value of spread.

Usage

MultiStart(obj, repeats, draw, spread, debug)

Arguments

obj

A dark object containing at least;

obj$time	time
obj$thrs	thresholds
obj$init	an initial estimate of the parameters of dark adaptation.

repeats

The number of times the algorithm is repeated

draw

A flag indicating whether a figure should be drawn.

spread

The amount by which the seed array should be varied. A larger value gives a greater range of possible starting points.

debug

A flag used in debugging the software.

Details

To reduce the possibility of selecting non-optimal parameter estimates, the optimisation is repeated in the region of initial estimates. The

Value

Returns a list;

time

times of threshold setting

out$thrs

observed thresholds

out$resid

residuals

out$fit

optimal fitted values

out$thet

seed parameters if test data

out$sse

sum of squared residuals if test data

out$data

source of the data

out$opt

optimal parameter estimates of the chosen model

out$Mod

name of the optimal model

out$Pn

the number of parameters needed to describe the data

out$AIC

array of AICc scores

out$val

calculated sum of squared residuals

out$R2

the coefficient of determination

out$warning

if none of the nearby values converge

out$call

updates the function call label

Author(s)

Jeremiah MF Kelly

Faculty of Life Sciences, The University of Manchester, M13 9PL, UK

References

Nelder, J.A.; Mead, R. 1965: A simplex for function minimization. Comput. J. 7, 308-313

Examples

set.seed(1234)
Time<- seq(0,20)
tmp<- TestData(Time)
P<-Start(tmp,1000)
MSC<-ModelSelect(tmp, P)
tmp2<-BestFit(tmp, MSC)
tmp3<-MultiStart(tmp2,10)