search for: itermax

...K*Time + M*S*log(1+ Fpi/(M*S)) while((Fpt-Fpi) > tolerance) { Fpi = Fpt Fpt = K*Time + M*S*log(1+ Fpi/(M*S)) Fp0 = Fpt } return(Fpt) } x<- iter(Fpi = 0.224, Time = 0.2, tolerance = 0.000001) But I want do something like this ( conceptually) for( i in 2:itermax) { Fpt[i] = K*Time + M*S*log(1+ Fpi/(M*S)) if((Fpt[i]- Fpt[i-1])<= tolerance) break print(Fpt[i] } something like this. any kind of help is highly appreciated. thank you -- Acharya, Subodh [[alternative HTML version deleted]]

...y; Cov_xy = Mxx*Myy - Mxy*Mxy; Mxz2 = Mxz*Mxz; Myz2 = Myz*Myz; A2 = 4*Cov_xy - 3*Mz*Mz - Mzz; A1 = Mzz*Mz + 4*Cov_xy*Mz - Mxz2 - Myz2 - Mz*Mz*Mz; A0 = Mxz2*Myy + Myz2*Mxx - Mzz*Cov_xy - 2*Mxz*Myz*Mxy + Mz*Mz*Cov_xy; A22 = A2 + A2; epsilon=1e-12; ynew=1e+20; IterMax=20; xnew = 0; # Newton's method starting at x=0 epsilon=1e-12; ynew=1e+20; IterMax=20; xnew = 0; iter=1:IterMax for (i in 1:IterMax){ yold = ynew; ynew = A0 + xnew*(A1 + xnew*(A2 + 4.*xnew*xnew)); if (abs(ynew) > abs(yold)){...

...] A2=A[2] A3=A[3] regm1 <- nnet(Xcc,Ycr,entropy=T,size=A1,decay=A2,maxit=2000,trace=F,Hess=T,rang=A3,skip=T) dif=sum((predict(regm1,Xcc)-Ycr)^2) return(dif) } somar=DEoptim(pred_regm1,c(1,0.00001,0.01), c(25,0.999,0.95), control = DEoptim.control(steptol=25,trace = FALSE,itermax=500,parallelType=1)) but implemented parallel process, i have next error: Error en checkForRemoteErrors(val) : 4 nodes produced errors; first error: could not find function "nnet" how I can solve my mistake? Thanks and kind regards -- David Zamora Ávila (I.C.) Estudiante de la M...

...he Calinski index ( [tr(b)/(k-1)]/[tr(w)/(k-1)] ) which i try to maximize to have the best number of clusters. A function is already implemented in R to calculate this index : clustIndex(cl,x, index="calinski") where cl is the result of a clustering method , for instance: cclust(x,k,itermax,verbose=TRUE,method="kmeans") My probleme is that I can't calculate the Calinski's index when a cluster contains only one datapoint : Error in cov(x[cluster == l, ]) : supply both x and y or a matrix-like x Is there a way to solve this? thanx for your help, David -.-.-.-.-...

...clusters. > A function is already implemented in R to calculate this index : > > clustIndex(cl,x, index="calinski") Where is that from? It's not part of R -- package cclust, perhaps? > where cl is the result of a clustering method , for instance: > > cclust(x,k,itermax,verbose=TRUE,method="kmeans") > > My probleme is that I can't calculate the Calinski's index when a cluster > contains only one datapoint : > > Error in cov(x[cluster == l, ]) : supply both x and y or a matrix-like x The programmer forgot drop=FALSE, it would se...

Hello -- The question I have is about the gmm() function from the 'gmm' package (v. 1.4-5). The manual accompanying the package says that the gmm() function is programmed to use either of four numerical solvers -- optim, optimize, constrOptim, or nlminb -- for the minimization of the GMM objective function. I wonder whether there is a way to pass controls to a solver used while calling

...gt; > > homals(x, sets=0, ndim=3, active=T, rank=3, level="NO", starplots=FALSE, > + catplots=FALSE, trfplots=FALSE, lossplots=FALSE, hullplots=FALSE, > + spanplots=FALSE, graphplot=TRUE, objplot=TRUE, objscores=TRUE, > + objlabel=TRUE, offset=1.20, eps1 = -Inf, eps2=1e-5, itermax=32, > + voronoi=FALSE, saveMe=TRUE, demo=FALSE, timer=FALSE, tk=FALSE) > Iteration: 1 Eigenvalues: 0.275420 0.182711 0.117240 Gain: 0.575371 > 0.981319 > Iteration: 2 Eigenvalues: 0.284051 0.185338 0.129728 Gain: 0.599117 > 0.992531 > Iteration: 3 Eig...

...highlight.3d=TRUE, col.axis="blue", col.grid="lightblue",main="val iniz mistura normali multivariata", angle=120, pch=20) #verosimiglianza iniziale l0<-sum(log(p)) l1<-l0 alpha<-alpha0 mu1<-mu01 mu2<-mu02 sd1<-sd01 sd2<-sd02 for (iter in 1:itermax) { #passo E for (i in 1:n) { tau[i,1]<-(alpha*f1[i])/p[i] tau[i,2]<-((1-alpha)*f2[i])/p[i] } #passo M alpha= mean(tau[,1]) mu1=colSums(tau[,1]*y)/sum(tau[,1]) mu2=colSums(tau[,2]*y)/sum(tau[,2]) ycen1<-(y-mu1) ycen2<-(y-mu2) cov1<-matrix(0,2,2) cov2<-matrix(0,2,2) for (i...

...highlight.3d=TRUE, col.axis="blue", col.grid="lightblue",main="val iniz mistura normali multivariata", angle=120, pch=20) #verosimiglianza iniziale l0<-sum(log(p)) l1<-l0 alpha<-alpha0 mu1<-mu01 mu2<-mu02 sd1<-sd01 sd2<-sd02 for (iter in 1:itermax) { #passo E for (i in 1:n) { tau[i,1]<-(alpha*f1[i])/p[i] tau[i,2]<-((1-alpha)*f2[i])/p[i] } #passo M alpha= mean(tau[,1]) mu1=colSums(tau[,1]*y)/sum(tau[,1]) mu2=colSums(tau[,2]*y)/sum(tau[,2]) ycen1<-(y-mu1) ycen2<-(y-mu2) cov1<-matrix(0,2,2) cov2<-matrix(0,2,2) for (i...