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Dear Experts, The following is my "Iterator". When I try to write a new function with itel, I got error. This is what I have: > supDist<-function(x,y) return(max(abs(x-y))) > > myIterator <- function(xinit,f,data=NULL,eps=1e-6,itmax=5,verbose=FALSE) { + xold<-xinit + itel<-0 + repeat { + xnew<-f(xold,data) + if (verbose) { + cat( + "Iteration: ",formatC(itel,width=3, format="d"), + "xold: ",formatC(xold,digits=8,width=12,format="f"), + "xnew: ",form...

...peed of converge. Show first that which implies superlinear convergence, and then show which means convergence is quadratic. Coding. (1) Write an R function newton() that applies Newton¡¯s method to a general univariate polynomial equation f (x) = 0. Use the standard iterative setup with itmax, eps, verbose. (2) Use the R package polynom to handle polynomials and their derivatives. If it is not there, install it. You construct a polynomial p from a vector a as p <- polynomial(a) (try this out for various a !) and then you can use predict (p,x) to evaluate the polynomial at x and y...

...100-point scale) of the same 10 faces (analogous to the bread data). I would like to get an individual differences scaling jointly for the individual judges and the faces, and plot them on the same axes. This is the example: library(smacof) data(breakfast) res.rect<-smacofRect(breakfast, itmax = 1000) plot(res.rect, joint = TRUE, xlim = c(-10, 10)) However, smacofIndDiff() doesn't produce the desired form of output. This is the bread example, which has data in roughly the same form as mine: data(bread) res.uc <- smacofIndDiff(bread, itmax = 1000) Thanks, __________________...

Y have only continuos quantitative data and when I trie the function I get next message > res<-clues(C,quiet=TRUE) Error en ChooseK_sil(y, y2 = y, n0, alpha, eps, itmax, second, K2.vec, : el objeto (list) no puede ser coercionado a 'double' Last line would be the object could (list) not be coerced to ´double´ What can be the problem? Yhanks _________________________________________________________________ Facebook. cial-network-basic...

...(x[3]^(1-y[5])))-((y[1]^y[5])*(y[3]^(1-y[5]))) h[2]=((x[2]^y[6])*(x[4]^(1-y[6])))-((y[2]^y[6])*(y[4]^(1-y[6]))) return(h) } heq=function(x,...){ h=rep(NA,2) h[1]=x[1]+x[2]-y[1]-y[2] h[2]=x[3]+x[4]-y[3]-y[4] return(h) } ans2=constrOptim.nl(par=p1,fn=fn,hin=hin,heq=heq,control.outer=list(itmax= 1000,mu0=.00001),list(y,y,y)) Any advice or explanation of my errors would be greatly appreciated! -- Erik O. Kimbrough Department of Economics (AE1) School of Business and Economics Maastricht University [[alternative HTML version deleted]]

Not an R question directly, but has anyone got a method for placing a moderately large number of (near) equi-spaced points on a sphere? I have a nasty feeling platonic solids are needed for exact solutions and I'm thinking of samplings involving around 200 - 1000 regularly-spaced points, Thanks, Richard Rowe Richard Rowe Senior Lecturer Department of Zoology and Tropical Ecology, James