search for: sigma3

...t(p1n) #posterior 2 var2 = sd2^2 #sample variance p2 = n2/var2 #sample precision p2n = p1n+p2 mu2 = ((p1n)/(p2n)*mu1)+((p2)/(p2n))*ybar2 sigma2 = 1/sqrt(p2n) #posterior 3 var3 = sd3^2 #sample variance p3 = n3/var3 #sample precision p3n = p2n+p3 mu3 = ((p2n)/(p3n)*mu2)+((p3)/(p3n))*ybar3 sigma3 = 1/sqrt(p3n) posterior<-cbind(rbind(mu1,mu2,mu3),rbind(sigma1,sigma2,sigma3)) rownames(posterior)<-c("Posterior 1", "Posterior 2", "Posterior 3") colnames(posterior)<-c("Mu", "SD") return(posterior)} -------------------------------...

...<- lm(Y1 ~ Xi) fit2 <- lm(Y2 ~ Xi) fit3 <- lm(Y3~ Xi) resid1 <- Y1-as.matrix(cbind( 1,Xi))%*%fit1$coef resid2 <- Y2-as.matrix(cbind( 1,Xi))%*%fit2$coef resid3 <- Y3-as.matrix(cbind( 1,Xi))%*%fit3$coef sigma1<-sum(resid1*resid1)/nrow(Xi) sigma2<-sum(resid2*resid2)/nrow(Xi) sigma3<-sum(resid3*resid3)/nrow(Xi) rho12<- cor(resid1, resid2) rho13<- cor(resid1, resid3) rho23<- cor(resid2, resid3) library("copula") library(mvtnorm) x<-as.matrix(cbind( 1,Xi)) km<- as.numeric(ncol(x)) #Parameters beta <- rbind(rep(0,km)) sigma <- 1 rho <- 0.5 #u...

...andom forest. But I has not a clear understanding on random forest. Here is some question: As for an image, for example its size is 512x512 and has only one variable -- gray level. The histogram of the image looks like mixture Gaussian Model, say Gauss distribution (u1,sigma1), (u2,sigma2),(u3,sigma3). And a image classified by K-means or EM algorithm, so the class label image is also 512x512 and has 0, 1, 2 value. I read the binary image data as follows: datafile <- file("bone.img","rb") img <- readBin(datafile,size=2,what="integer",n=512*512,signed=FALS...

Hello, I am facing a problem with optimization in R from 2-3 weeks. I have some Gaussian mixtures parameters and I want to find the maximum in that *Parameters are in the form * mean1 mean2 mean3 sigma1 sigma2 sigma3 c1 c2 c3 506.8644 672.8448 829.902 61.02859 9.149168 74.84682 0.1241933 0.6329082 0.2428986 I have used optima and optimx to find the maxima, but it gives me value near by the highest mean as an output, for example 830 in the above parameters. The code for my optim function is...

Hi Now a more theoretical question. I have two correlation matrices - one of a set of variables under a particular condition, the other of the same set of variables under a different condition. Is there a statistical test I can use to see if these correlation matrices are "different"? Thanks Mick