search for: wmat

...GM <- mat + cat("Gmat", dim(GM), "\n") + GM + }, + Vmat = function(tt, x, phi) { + VM <- mat + cat("Vmat", dim(VM), "\n") + VM + }, # var observation equation + Wmat = function(tt, x, phi) { + WM <- mat + cat("Wmat", dim(WM), "\n") + WM + }, + m0 = matrix(c(1, 2), nrow = 1), + C0 = mat) Gmat 2 2 Gmat 2 2 > > model.f <- kfilter(model) Gmat 2 2 Wmat 2 2 Fmat 2 2 Vmat 2 2 E...

....09857565 -0.33015962 0.09136359 -5.426254 -0.8201206 [5,] -0.68977432 0.01977374 0.61772506 3.751978 0.4348802 %Matlab Matrix PA9900 =[11/24 10/53 0/1 0/1 29/43 ;1/24 27/53 0/1 0/1 13/43 ;14/24 178/53 146/244 17/23 15/43 ;2/24 4/53 0/1 2/23 2/43 ;4/24 58/53 26/244 0/1 5/43] %MatLab-syntax [wmat,dmat]=eig(mat) %MatLab-output wmat = -0.2250 0.4330 -0.4998 -0.1795 -0.1854 -0.1083 0.1771 0.1599 -0.0614 -0.0583 -0.9403 -0.7191 -0.8457 0.9617 0.9708 -0.0327 -0.0752 -0.0967 -0.1750 -0.1160 -0.2289 -0.5083 0.0058 0.0928 0.0802 dma...

...rds,y.vel) kal.ts <- ts(kal.df,deltat=mean(diff(times))) my.Fmat <- function(tt,x,phi){ mf <- matrix(c(1,0,0,0,0,1,0,0),nrow=4,byrow=T) return(mf) } my.Gmat <- function(tt,x,phi){ mat <- matrix(c(1,x[[1]][tt],0,0,0,1,0,0,0,0,1,x[[1]][tt],0,0,0,1),nrow=4,byrow=T) return(mat) } my.Wmat <- function(tt,x,phi){ row.1 <- c((x[[1]][tt]^4)/4,(x[[1]][tt]^3)/2,(x[[1]][tt]^4)/4,(x[[1]][tt]^3)/2) row.2 <- c((x[[1]][tt]^3)/2,x[[1]][tt]^2,(x[[1]][tt]^3)/2,x[[1]][tt]^2) mat <- rbind(row.1,row.2,row.1,row.2) # no need for as.matrix mat <- mat * phi[1] return(mat) } kal.SS &...

...ook, but I haven't had a chance to give it a thorough read at this point. Any assistance would be greatly appreciated: Example code: #n x p output matrix FMat<-cbind(rnorm(123),rnorm(123)) #p x p state transition matrix GMat<-matrix(c(1,0,0,1),ncol=2) #p x p system noise distribution WMat<-matrix(c(0.02,0,0,0.02),ncol=2) # nxn measurement noise Vmat<-matrix(0,ncol=123,nrow=123) diag(Vmat)<-0.02 #initial state mean and variance m0Vec<-c(1,1) c0Vec<-c(0.05,0.05) ssMod<-dlm(FF=FMat,V=Vmat,GG=GMat,W=WMat,m0=m0Vec,C0=c0Vec) Resulting error: Error in dlm(FF = FMat, V...

...alue=paste0( "~ x",i," + dummy"))) # define the moment conditions for GMM } # Estimate a WLS-type weighting matrix to use as a user-specified weighting matrix in GMM m0 <- systemfit(formula=ES_g, method="OLS", data=data1) OLSmat <- diag(diag(m0$residCov)); Wmat <- solve(OLSmat) # Choose the type of covariance matrix in GMM vc1 <- "MDS" vc1 <- "CondHom" vc1 <- "HAC" #vc1 <- "TrueFixed" # Choose between restricted and unrestricted estimation cec1=NULL # unrestricted cec1=3 # restrict the coefficien...

...quot;,i," + dummy"))) # define the moment conditions for GMM > } > > # Estimate a WLS-type weighting matrix to use as a user-specified weighting > matrix in GMM > m0 <- systemfit(formula=ES_g, method="OLS", data=data1) > OLSmat <- diag(diag(m0$residCov)); Wmat <- solve(OLSmat) > > # Choose the type of covariance matrix in GMM > vc1 <- "MDS" > vc1 <- "CondHom" > vc1 <- "HAC" > #vc1 <- "TrueFixed" > > # Choose between restricted and unrestricted estimation > cec1=NULL # unres...

Dear R-Users, I'm using SSPIR package for a spatio-temporal application. Is it possible to modify the structure of the involved matrixes (Fmat, Gmat, Vmat,Wmat)? I want to create a model like this #y(t)=k*theta(t)+epsilon(t) #theta(t)=h*theta(t-1)+eta(t) #epsilon(t) N(0,V) V=sigma2*I #eta(t) N(0,W) W=sigma2_eta where the state variable theta has dimension 1(p=1) and at each time time the observed variable y has dimension equal to d=3....

...; > > ------------------------------ > > Message: 15 > Date: Mon, 2 Oct 2006 12:57:16 +0300 > From: "Marian Neagul" <marian@info.uvt.ro> > Subject: [Samba] Auth Problem > To: <samba@lists.samba.org> > Message-ID: <000301c6e609$2444a350$a50a0a0a@wmat> > Content-Type: text/plain; charset="US-ASCII" > > Hello, > > > > I have a problem related to user authentication: Users can not login from > Windows workstations. > > I get the following error: > > "The system could not log you in. Make...