search for: mu3

Dear all: Assume I have 3 distributions, x1, x2, and x3. x1 ~ normal(mu1, sd1) x2 ~ normal(mu2, sd2) x3 ~ normal(mu3, sd3) y1 = x1 + x2 y2 = x1 + x3 Now that the data I can observed is only y1 and y2. It is easy to estimate (mu1+m2), (mu1+mu3), (sd1^2+sd2^2) and (sd1^2+sd3^2) by EM algorithm since y1 ~ normal(mu1+mu2, sqrt(sd1^2+sd2^2)) and y2 ~ normal(mu1+mu3, sqrt(sd1^2+sd3^2)) However, I want to estimate mu1...

...re.eval("library(kappalab)", true); re.eval("library(methods)", true); re.eval("mu1 <- c(59.73, 27.94,89.67, 62.44)", true); re.eval("mu2<- c(57.81, 72.67, 82.97, 62.44)", true); re.eval("mu3<- c(45.66, 49.19, 53, 64.39)", true); re.eval("mu4<- c(70.39, 80.41, 89.67, 80)", true); re.eval("delta.C <- 1", true); re.eval("Acp <- rbind (c(mu4,mu2,delta.C), c(mu2,mu3,delta.C), c(mu3,mu1,delta.C))", true);...

...library(lattice) library(latticeExtra) sample1<-rnorm(100,10,2) sample2<-rnorm(100,50,3) sample3<-rnorm(100,20,2) sample4<-rnorm(100,40,1) mu1<-mean(sample1) ci.upper1<-mu1+2*2 ci.lower1<-mu1-2*2 mu2<-mean(sample2) ci.upper2<-mu2+2*3 ci.lower2<-mu2-2*3 mu3<-mean(sample3) ci.upper3<-mu3+2*2 ci.lower3<-mu3-2*2 mu4<-mean(sample4) ci.upper4<-mu4+2*1 ci.lower4<-mu4-2*1 categories<-c("A","B") mu<-cbind(mu1,mu2,mu3,mu4) ci.upper<-cbind(ci.upper1,ci.upper2,ci.upper3,ci.upper4) ci.lower<-cbind(ci.l...

...894.4272 130.390552 6 1264.9111 36.727069 7 1788.8544 52.848587 8 2449.4897 25.128742 9 3464.1016 7.531766 10 4472.1360 8.827367 11 6123.7244 6.600603 12 8660.2540 4.083339 I would like to fit N as a function of x according to a function depending on 9 parameters (A1,A2,A3,mu1,mu2,mu3,myvar1,myvar2,myvar3), namely N ~ (log(10)*A1/sqrt(2*pi)/log(myvar1)*exp(-((log(x/mu1))^2)/2/log(myvar1)/log(myvar1)) +log(10)*A2/sqrt(2*pi)/log(myvar2)*exp(-((log(x/mu2))^2)/2/log(myvar2)/log(myvar2)) +log(10)*A3/sqrt(2*pi)/log(myvar3)*exp(-((log(x/mu3))^2)/2/log(myvar3)/log(myvar3))) (i.e. N i...

...nnw$bp1000 > dur <- pennw$inuidur1 > fw <- factor(wage) > Ik <- model.matrix(~fw-1) > I1 <- Ik[,1] > I2 <- Ik[,2] > I3 <- Ik[,3] > I4 <- Ik[,4] > I5 <- Ik[,5] > yelmon <- function(theta,x){ + mu1 <- theta[1] + mu2 <- theta[2] + mu3 <- theta[3] + mu4 <- theta[4] + mu5 <- theta[5] + beta <- theta[6] + logL <- sum((Ik*log((exp(mu2-(x*beta))/(1+exp(mu2-(x*beta))))-(exp(mu1-(x*beta))/(1+exp(mu1-(x*beta))))))+(Ik*log((exp(mu3-(x*beta))/(1+exp(mu3-(x*beta))))-(exp(mu2-(x*beta))/(1+exp(mu2-(x*beta))))))+(Ik*log((ex...

Please help me in writing the R code for this problem. I've been solving this for 4 days. It was hard for me to solve it. It's a simulation problem in R. The problem is My true model is a normal mixture which is given as 0.5 N(-0.8,1) + 0.5 N(0.8,1). This model has two components. I will get a random sample of size 100 from this model. I will do this 300 times. That means, I will have

...62106, 1.03932953, -6.21987657, -0.54763352, 0.20263192) # data theta0= c(log(2),0,c(0,-.3,.3),log(c(10,.05,.05))) # initial value(In fact, true parameter value) n = length(x) fr2 = function(theta) { a1 = theta[1]; a2 = theta[2] mu1 = theta[3]; mu2 = theta[4]; mu3 = theta[5] g1 = theta[6]; g2 = theta[7]; g3 = theta[8] w1=exp(a1)/(1+exp(a1)+exp(a2)) w2=exp(a2)/(1+exp(a1)+exp(a2)) w3=1-w1-w2 obj =((w1^2)/(2*sqrt(exp(g1)*pi)) + (w2^2)/(2*sqrt(exp(g2)*pi)) + (w3^2)/(2*sqrt(exp(g2)*pi)) + 2*w1*w2*dnorm((mu1-mu2)/sqrt(exp(g1)+exp(g...

...62106, 1.03932953, -6.21987657, -0.54763352, 0.20263192) # data theta0= c(log(2),0,c(0,-.3,.3),log(c(10,.05,.05))) # initial value(In fact, true parameter value) n = length(x) fr2 = function(theta) { a1 = theta[1]; a2 = theta[2] mu1 = theta[3]; mu2 = theta[4]; mu3 = theta[5] g1 = theta[6]; g2 = theta[7]; g3 = theta[8] w1=exp(a1)/(1+exp(a1)+exp(a2)) w2=exp(a2)/(1+exp(a1)+exp(a2)) w3=1-w1-w2 obj =((w1^2)/(2*sqrt(exp(g1)*pi)) + (w2^2)/(2*sqrt(exp(g2)*pi)) + (w3^2)/(2*sqrt(exp(g2)*pi)) + 2*w1*w2*dnorm((mu1-mu2)/sqrt(exp(g1)+exp(g...

Dear list, i'm trying to test if a linear combination of coefficients of glm is equal to 0. For example : class 'cl' has 3 levels (1,2,3) and 'y' is a response variable. We want to test H0: mu1 + mu2 - mu3 =0 where mu1,mu2, and mu3 are the means for each level. for me, the question is how to get the covariance matrix of the estimated parameters from glm. but perhaps there is a direct solution in one of the packages. i know how to solve this particular problem (i wrote it below) but i'm curious...

...ep=",") > mnl.lik<-function(theta,y){ + th1<-theta[1] + th2<-theta[2] + tha<-theta[3] + thb<-theta[4] + thc<-theta[5] + thp<-theta[6] + thmu<-theta[7] + alfz<-theta[8] + alfp<-theta[9] + mu1<- alfz*y$z_a1 + alfp*y$p1 + mu2<- alfz*y$z_a2 + alfp*y$p2 + mu3<- alfz*y$z_a3 + alfp*y$p3 + U1<- th1 +tha*y$a1 +thb*y$b1 +thc*y$c1 +thp*y$p1 +thmu*mu1 + U2<- th2 +tha*y$a2 +thb*y$b2 +thc*y$c2 +thp*y$p2 +thmu*mu2 + U3<- +tha*y$a3 +thb*y$b3 +thc*y$c3 +thp*y$p3 +thmu*mu3 + Usum<- (exp(U1)+exp(U2)+exp(U3)) + arg1<- function(mu1) {(exp(U1)/...

...p1n)*mu0)+((p1)/(p1n))*ybar1 sigma1 = 1/sqrt(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(...

...reading comprehension) on 6 separate occasions. Initially (square zero) I want to treat the test time as a factor (with 6 levels). The students are of course ``random effects''. Later I want to look at reduced models, with the means at the 6 times following patterns: (mu1, mu2, mu2, mu3, mu3, mu4) or (mu1, mu1+theta, mu1+theta, mu1+2*theta, mu1+2*theta, mu1+3*theta) the idea begin that the tests are given ``in pairs'' at the beginning and end of the school year. Progress is expected over the school year, no progress over the two intervening summers. The summers fal...

...s to tree states<-structure(states$PC, names=states$Species) names(states)<-tree$tip.label #MuSSE diversitree:::argnames.musse(NULL, 3) #number of states lik<-make.musse(tree, states, 3) argnames(lik) #contstrain lambda lik.base<-constrain(lik, lambda2~lambda1, lambda3~lambda1, mu2~mu1,mu3~mu1,q13~q12,q21~q12,q23~q12,q31~q12,q32~q12) #find ML point for this model p<-starting.point.musse(tree, 3) fit.base<-find.mle(lik.base, p[argnames(lik.base)]) #unconstrained lik.lambda<-constrain(lik,mu2~mu1,mu3~mu1,q13~q12,q21~q12,q23~q12,q31~q12,q32~q12) fit.lambda<-find.mle(lik.lam...

...eachother, but conditional on the residual variance. So now I have a data frame of say 10.000 iterations, and k+1 parameters. I am especially interested in the posterior distribution of the mu parameters, because I want to test the support for an inequalty constrained model (e.g. mu1 > mu2 > mu3). I wish to derive the multivariate 95% highest density parameter space for the mu parameters. For example, if I had a posterior distirbution with 2 means, this should somehow result in the circle or elipse that contains the 95% highest density area. Is something like this possible in R? All tips...

...with formulae if the variance # did not depend on the mean #asymptote depends on variety? mu2 <- function(p) (p[1]+p[4]*variety)/(1+exp((p[2]-times)/p[3])) elliptic(data,model=mu2,preg=c(17,52,7.5,0),varfn=shape,pvar=c(1.8,-2.2), shfn=T,par=0.9,dist="Student",pell=6.5) # also on year? mu3 <- function(p) (p[1]+p[4]*variety+p[5]*year2+p[6]*year3)/ (1+exp((p[2]-times)/p[3])) elliptic(data,model=mu3,preg=c(14.5,52,7.5,4.5,0,0),varfn=shape, pvar=c(1.8,-2.2),shfn=T,par=0.9,dist="Student",pell=8) # etc. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

I get the following error when attempting to build wine 20010326 on Solaris 8, MU3 ./tools/wineinstall ... cd `dirname winebuild/__depend__` && make depend ../../tools/makedep -I. -I. -I../../include -I../../include -C. import.c main.c parser.c relay.c res16.c res32.c spec16.c spec32.c utils.c -C. cd `dirname wmc/__depend__` && make depend ../../tools/...

Hi, I'm not subscribed, so keep me in cc. And thanks for having mailing-list open for posting. I had a couple of problems with OpenSSH on Solaris 8/MU3 + recent patches. 1) When I tried to use scp from any other host, sshd on Solaris host crashed with SIGSEGV. Here's the stack trace: core 'core.sshd.7637' of 7637: ./sshd -d -d -d fefb393c strncpy (ffbee074, 5, 7, 0, 21f2c, ffbee074) + 65c ff0561a8 pam_sm_open_session (4, 0, 3e...

...Value Std. Error t value ##(Intercept):1 -1.1527 0.33113 -3.4810 ##(Intercept):2 -1.2657 0.34139 -3.7073 ##(Intercept):3 -0.6633 0.29854 -2.2218 ##(Intercept):4 -1.3863 0.35355 -3.9210 ## ##Number of linear predictors: 4 ##Names of linear predictors: logit(mu1), ## logit(mu2), logit(mu3), logit(mu4) but that seems to be going to greater lengths than one should need to! Any comments and suggestions will be welcome! Best wishes to all, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk> Fax-to-em...

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