starr {accuracy} | R Documentation |
Implements the Starr test for identification of the global optimum of a likelihood surface.
starr(betas, tol=.0001, dmethod="euclidean")
betas |
Vector of parameter values |
tol |
Tolerance distance between two parameter vectors to consider as "unique" optimum |
dmethod |
method used to compute distance between two parameter vectors |
texttt{starr} computes the probability that a local optimum, which may or may not be the global optimum, of a likelihood function has not been observed. The probability is generated by counting the observed number of ``basins of attraction'' - starting values that lead to an local optimum.
texttt{starr} is a class "double" with value equal to the probability than a local optimum is unobserved.
texttt{starr} is given a vector of texttt{betas} parameter values identifying local optima resultant of a search algorithm, such as texttt{mle} or texttt{nls}. The starting values used to generate texttt{betas} can be a grid, or for larger n-dimenional parameter spaces, may be randomly chosen. The parameter values identifying a local optimum are passed to texttt{starr} for each set of starting values that lead to that local optimum.
Micah Altman Micah_Altman@harvard.edu http://www.hmdc.harvard.edu/micah_altman/ , Michael McDonald
Altman, M., J. Gill and M. P. McDonald. 2003. Numerical Issues in Statistical Computing for the Social Scientist. John Wiley & Sons. http://www.hmdc.harvard.edu/numerical_issues/
Finch, S. J., Mendell, N. R., Thode, H. C., Jr. 1989. ``Probabilistic Measures of Adequacy of a Numerical Search for a Global Maximum.'' Journal of the American Statistical Association {bf 84}, 1020-3.
Starr, N. 1979. ``Linear Estimation of the Probability of Discovering a New Species.'' Annals of Statistics {bf 7}, 644-52.
x=rbind(c(1,1,1), c(1,2,1), c(1,1.1,1), c(1,2,1), c(3,4,5)); starr(rbind(1,1,2,2)); #BODmodel BOD <- structure(list(Time = c(1, 2, 3, 4, 5, 7), demand = c(8.3, 10.3, 19, 16, 15.6, 19.8)), .Names = c("Time", "demand"), row.names = c("1", "2", "3", "4", "5", "6"), class = "data.frame", reference = "A1.4, p. 270") stval=expand.grid(A = seq(10, 100, 10), lrc = seq(.5, .8, .1)) llfun<-function(A,lrc,BOD) -sum((BOD$demand - A*(1-exp(-exp(lrc)*BOD$Time)))^2) lls=NULL for (i in 1:nrow(stval)) { lls = rbind(lls, llfun(stval[i,1], stval[i,2],BOD)) } fm1 <- nls(demand ~ A*(1-exp(-exp(lrc)*Time)), data = BOD, start = c(A = 20, lrc = log(.35))) ss = -sum(resid(fm1)^2) dehaan(lls, ss) llb=NULL for (i in 1:nrow(stval)) { llb = rbind(llb,coef( nls(demand ~ A*(1-exp(-exp(lrc)*Time)), data = BOD, start = c(A=stval[i,1], lrc = stval[i,2])))) } starr(llb)