search for: mu2

Hello R Help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and number of success in ca...

Dear R help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and number of success in ca...

...0L, 0L, 0L, 0L, 0L)), .Names = c("tv", "n1", "n2", "n3"), row.names = c(NA, -20L), class = "data.frame") # Negative log likelihood functions NLL1 <- function(par, y){ p1 <- par[1] p2 <- par[2] mu1 <- par[3] mu2 <- par[4] n1 <- y$n1 n2 <- y$n2 n3 <- y$n3 t <- y$tv P1 <- (p1*((-1 + exp(sqrt((mu1 + mu2 + p1 + p2)^2 - 4*(mu2*p1 + mu1*(mu2 + p2)))*t))*((-mu2)*(mu2 - p1 + p2) + mu1*(mu2 + 2*p2)) - mu2*sqrt((mu1 + mu2 + p1 + p2)^2 - 4*(mu2*p1 + mu1*(mu2 + p2))) -...

Hello, I have written the function I would like to integrate in two ways: denfxn <- function(yy,vv,a2,b2,mu2) { pp <- 1-pnorm(yy/sqrt(vv)) part1 <- pp^(a2-1) part2 <- (1-pp)^(b2-1) part3 <- dnorm(yy,mu2,sqrt(vv)) return(part1*part2*part3) } denfxnorg <- function(yy,vv,a2,b2,mu2) { pp <- 1-pnorm(yy/sqrt(vv)) pp <- if (pp < .001) .001 else if (pp > ....

...duced I know from the help files that for dnbinom "Invalid size or prob will result in return value NaN, with a warning", but I am not able to find a workaround for this in my code to avoid it. I appreciate the help. Thanks. Below is the reproducible code: mixnbinom=function(y,k1,mu1,k2,mu2,prob,eps= 1/100000){ new.parms=c(k1,mu1,k2,mu2,prob) err=1 iter=1 maxiter=100 hist(y,probability=T,nclass=30,col="lightgrey",main="The EM algorithm") xvals=seq(min(y),max(y),1) lines(xvals,prob*dnbinom(xvals,size=k1,mu=mu1)+ (1-prob)*dnbinom(xvals,size=k2,mu=mu2),col="green...

...6 5 894.4272 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....

Dear R users, I have two nonlinear equations, f1(x1,x2)=0 and f2(x1,x2)=0. I try to use optim command by minimize f1^2+f2^2 to find x1 and x2. I found the optimal solution changes when I change initial values. How to solve this? BTW, I also try to use grid searching. But I have no information on ranges of x1 and x2, respectively. Any suggestion to solve this question? Thanks, Kate

...ogram is to compare means of two independent samples that results in Beherens-Fisher posterior and in the model.file of WinBUGS "d2.bug" there is the following codes: model { for( i in 1 : 30 ) { x[i] ~ dnorm(mu1,sig1) } for( i in 1 : 30 ) { y[i] ~ dnorm(mu2,sig2) } mu1 ~ dnorm(50,1.0E-6) sig1 ~ dgamma(0.001,0.001) mu2 ~ dnorm(50,1.0E-6) sig2 ~ dgamma(0.001,0.001) lbda <- mu1 - mu2 } I??m a new user of WinBUGS and if someone detect error in the model codes too, I??m grateful. Thanks for help! Gilberto Matos. -----...

...I have two questions, (i) how is the function printing when there is no code to print and (ii) how is n1 which equals 1 being reported as 2? I suspect there is something fundamental about R that I don't know. Thank you for the help. John library(epicalc) n.for.2means.js <- function (mu1, mu2, sd1, sd2, ratio = 1, alpha = 0.05, power = 0.8) { n1 <- (sd1^2 + sd2^2/ratio) * (qnorm(1 - alpha/2) - qnorm(1 - power))^2/(mu1 - mu2)^2 n1 <- round(n1) n2 <- ratio * n1 if (length(alpha) == 1) { alpha1 <- NULL } else { alpha1 <- alpha } if (length(power) ==...

Hi, My name is Laura. I'm a PhD student at Queen's University Belfast and have just started learning R. I was wondering if somebody could help me to see where I am going wrong in my code for estimating the parameters [mu1, mu2, lambda1] of a 2-phase Coxian Distribution. cox2.lik<-function(theta, y){ mu1<-theta[1] mu2<-theta[2] lambda1<-theta[3] p<-Matrix(c(1, 0), nrow=1, ncol=2) Q<-Matrix(c(-(lambda1 + mu1), 0, lambda1, -mu2), nrow=2, ncol=2) q<-Matr...

hello: I have 3 data.frame objects. First df object: Of dim (149,31). Columns 2:31 are marked as T1..T14 and N1..N16. Name T1 T2 N1 T3 N2 N3 N4 T4 mu1 10 10 9 10 9 9 8 10 mu2 11 11 9 11 9 9 9 11 ... muN 12 12 9 11 9 9 8 12 Second df object: of Dim (50000,31). Columns 2:31 are maked as T1...T14 and N1..N16. Name T1 T2 N1 T3 N2 N3 N4 T4 J1 2 3 20 2 22 21 29 3 J2 4 1 20...

I am using JRI (Java R Interface) library in order to call R from within my Java application. But since the "rmu1" and "rmu2" ,see the following code, are objects of type S4 once i run the application the value of Null will be returned for both of them. On this regard, i would appreciate it if anyone can tell me how i am going to display and/ or convert these objects to Java objects or any other way to get the resul...

...gt; likelihood of some observations is zero and therefore the log is -Inf. > > Where is my mistake? > > My code: > > # EM algorithm manually > # dat is the data > > > # initial values > pi1<-0.5 > pi2<-0.5 > mu1<--0.01 > mu2<-0.01 > sigma1<-0.01 > sigma2<-0.02 > loglik[1]<-0 > loglik[2]<-sum(pi1*(log(pi1)+ > log(dnorm(dat,mu1,sigma1))))+sum(pi2*(log(pi2)+log(dnorm(dat,mu2,sigma2)))) > > > > tau1<-0 > tau2<-0 > k<-1 > > # loop...

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,...

...to have to the left side of the formula. This is the example code: 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<-cb...

...age > bpw <- pennw$bp1000 > dur <- pennw$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*...

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

...ha0*f1+(1-alpha0)*f2 scatterplot3d(y[,1], y[,2], p , 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-m...

...11764954, 5.92662106, 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)/sq...

...11764954, 5.92662106, 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)/sq...