search for: tau1

Hi I need to find the Hessian matrix for a complicated function from a certain kind of data but i keep getting this error Error in f1 - f2 : non-numeric argument to binary operator the data is given by U<-runif(n) Us<-sort(U) tau1<- 2 F1tau<- pgamma((tau1/theta1),shape,1) N1<-sum(Us<F1tau) X1<- Us[1:N1] Ti<- theta1*qgamma(Us[1:N1],shape,1) w=N1+1 V<- Us[w:r] N2<- length(V) V1<-theta2*qgamma(V,shape,1) Tj<-V...

...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 > while(abs(loglik[k+1]-loglik[k])>= 0.00001) { > > # E step > > tau1<-pi1*dnorm(dat,mean=mu1,sd=sigma1)/(pi1*dnorm(x,mean=mu1,sd=sigma1)+pi2*dnorm(dat,mean=mu2,sd=sigma2)) > > tau2<-pi2*dnorm(dat,...

...t;, "x2", "x3") mydta <- readin[myvars] # remove all missings mydta <- na.omit(mydta) # theta is the parameter vector ologit.lf <- function(theta, y, X) { n <- nrow(X) k <- ncol(X) # b is the coefficient vector for independent variables b <- theta[1:k] # tau1 is cut-point 1 tau1 <- theta [k+1] # tau2 is cut-point 2 tau2 <- theta [k+2] # tau3 is cut-point 1 tau3 <- theta [k+3] if (y == 1){ p <- (1/(1+exp( - tau1 + X %*% b))) } if (y == 2) { p <- (1/(1+exp( - tau2 + X %*% b))) - (1/(1+exp( - tau1 + X %*% b))) } if (...

...k for my code. After I simulate data from the model and estimate the data, I can't get precise estimation for my setting parameters. This is my simulation code: ======================= sim<-function(theta) { omega = theta[1] bet = theta[2] gam = theta[3] xi = theta[4] phi = theta[5] tau1 = theta[6] tau2 = theta[7] sigma.u = theta[8] n = theta[9] n.warm = 500 n = n+n.warm z = rnorm(n+1) u = rnorm((n+1),0,sigma.u) logh.pre = 1 logx.pre = xi+phi*logh.pre+tau1*z[1]+tau2*(z[1]^2-1)+u[1] logh = c(logh.pre,rep(0,n)) r = c(rep(0,(n+1))) logx = c(logx.pre,rep(0,n)) for(i in 2:(...

Why fails nls with "singular gradient" here? I post a minimal example on the bottom and would be very happy if someone could help me. Kind regards, ########### # define some constants smallc <- 0.0001 t <- seq(0,1,0.001) t0 <- 0.5 tau1 <- 0.02 # generate yy(t) yy <- 1/2 * ( 1- tanh((t - t0)/smallc) * exp(-t / tau1) ) + rnorm(length(t))*0.01 # show the curve plot(x=t, y=yy, pch=18) # prepare data dd <- data.frame(y=yy, x=t) nlsfit <- nls(data=dd, y ~ 1/2 * ( 1- tanh((x - ttt)/smallc) * exp(-x / tau2) ), start=...

I have a file: x y z 0.025 0.025 1.65775 0.025 0.050 1.62602 0.025 0.075 1.63683 0.025 0.100 1.91847 0.025 0.125 2.00913 0.025 0.150 1.82222 0.025 0.175 1.70901 0.025 0.200 1.39759 0.025 0.225 1.39089 0.025 0.250 1.04762 If I read the file like this: data<-read.table("file.dat") How do I access the vectors x,y,z that are inside the dataframe data? I studied Venables and

...lags:n if (lags > 1) { z.diff.lag = x[, 2:lags] if (type == "none") { result <- lm(z.diff ~ z.lag.1 - 1 + z.diff.lag) tau <- coef(summary(result))[1, 3] teststat <- as.matrix(tau) colnames(teststat) <- 'tau1' } if (type == "drift") { result <- lm(z.diff ~ z.lag.1 + 1 + z.diff.lag) tau <- coef(summary(result))[2, 3] phi1.reg <- lm(z.diff ~ -1 + z.diff.lag) phi1 <- anova(phi1.reg, result)$F[2] teststat...

Hi, I posted this message earlier in "Rmetrics" and I don't know whether I posted in the wrong place, so I'm posting it again in Rhelp. I have a function in x and y and let's call it f(x,y). I need to get the Hessian matrix. i.e I need (d^2f/dx^2), (d^2f/dxdy), (d^2f/dydx), (d^2f/dy^2).I can get these using the D function. now I need to evaluste the hessian matrix for

...theta1 theta2 x1 x2 x3 I try to follow McNeil (2006) http://www.ma.hw.ac.uk/~mcneil/ftp/NestedArchimedean.pdf with R as the programming language. My code so far is as follows: n = 10000 #parent tau1 = 0.60 theta1 = (2*tau1)/(1-tau1) #child tau2 = 0.80 theta2 = (2*tau2)/(1-tau2) #parent V1 = rgamma(n, 1/theta1,1) #child V2 = rgamma(n, 1/theta2,1) u1 = runif(n) u2 = runif(n) u3 = runif(n) u = cbind(u2,u3) #Create bivariate child copula u.inner = (1-log(u)/V2)^(-1/theta2) #Child inner gene...

Hi Can we get the result of an intigration without the absolute error? for example f1<-function(x1){(1/gamma(alpha))*x1^(alpha-1)*exp(-x1)*log(x1)} I1<-integrate(f1, 0, (max(cc)-tau1+(theta2/theta1)*tau1)/theta2) I1 0.08007414 with absolute error < 7.2e-05 I need the answer 0.08007414 withou the other part(with absolute error <7.2e-05) how can we do that? thank you and take care Laila [[alternative HTML version deleted]]

...in=1,bugs.directory="C:/Users/t-wec/Desktop/WinBUGS14",codaPkg=T,debug=T) mcmcout=read.bugs(model.sim) summary(mcmcout) ------------------------------------------------------------------------------------------------------------------------ model { # cluster parameters mu and tau tau1 <- (1-a*a)*taue taue <- 1/sigmae for (k in 1:K) { # cluster mean mu mu[k,1] ~ dnorm(m,tau1) for (t in 2:T) { mu[k,t] ~ dnorm(meanmu[k,t],taue) meanmu[k,t] <- m*(1-a)+a*mu[k,t-1] } # cluster varicance ta...

...don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau) 2[i] ~ dnorm(a21[i],tau1) a3[i] ~ dnorm(a31[i],tau2) a11[i]<-sm[i]*S1*m1 a21[i]<-sm[i]*S1*(1-m1)*S2*m2 a31[i]<-sm[i]*S1*(1-m1)*S2*(1-m2)*S3 sum[i]<-a11[i]+a21[i]+a31[i] } #priors #priors are dbeta(0.5,0.5), uniform is dbeta(1,1) S1~dbeta(1,1)#I(0,0.2) # S2~dbeta(1,1)#I(0.15,0.5) # S3~dbeta(1,1)#I(0.3,1) m1~db...

...don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau) 2[i] ~ dnorm(a21[i],tau1) a3[i] ~ dnorm(a31[i],tau2) a11[i]<-sm[i]*S1*m1 a21[i]<-sm[i]*S1*(1-m1)*S2*m2 a31[i]<-sm[i]*S1*(1-m1)*S2*(1-m2)*S3 sum[i]<-a11[i]+a21[i]+a31[i] } #priors #priors are dbeta(0.5,0.5), uniform is dbeta(1,1) S1~dbeta(1,1)#I(0,0.2) # S2~dbeta(1,1)#I(0.15,0.5) # S3~dbeta(1,1)#I(0.3,1) m1~db...

...MLE. There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between 1 to 24. Instead of listing all the parameters, one by one in the function definition, is there a neat way to do it in R ? The example is as follows: ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7) { if (tau1>0 && ro<1 && ro>-1) -sum(dmnorm(cbind(x,y),c(mu1,b*mu1),matrix(c(tau_1^2,ro_1*tau_1*sigma_1, ro_1*tau_1*sigma_1,sigma_1^2),nrow=2),log=T)) else NA } but now I need to have the sum of 24 of these negative log-likelihood. Thanks. Yue Notice: This e-mail message, tog...

...using forward and backward techniques and see if they are close 2)present the annual percent change (APC) for each of the intervals, ie my present data (1950-2010 in 5 year intervals) is giving me Variables Coef b0 Intercept -131.20404630 g0 t 0.06146463 g1 max(t-tau1,0) -0.51582466 g2 max(t-tau2,0) 0.43429615 Joinpoints: 1 tau1= 1990.5 2 tau2= 1995.5 APC 1950->1990=exp(0.06)=1.06-->6% 1990-1995=exp(0.06-0.51)=exp(-0.45)=0.63--> -37% 1995-2010=exp(0.06-0.51+0.43)-->-2% 3) Preferably a confidence interval for the APC should be g...

...an=c(20,1,110), sigma=sigma) rnormm<-rbind(rnormm,rnormm2) #with the following priors # Prior information s2<-matrix(c(2,0,0,0,2,0,0,0,2),ncol=3) m2<-c(5,10,15) psiinv2<-solve(matrix(c(10000,0,0,0,1,0,0,0,2),ncol=3)) prior<-list(a0=1,b0=1/5,nu1=4,nu2=4,s2=s2, m2=m2,psiinv2=psiinv2,tau1=0.01,tau2=0.01) state <- NULL # MCMC parameters nburn<-1000 nsave<-5000 nskip<-10 ndisplay<-1000 mcmc <- list(nburn=nburn,nsave=nsave,nskip=nskip,ndisplay=ndisplay) # Fit the model fit3<-DPdensity(y=rnormm,mcmc=mcmc, prior=prior, state=state,status=TRUE,na.action=na.omit)...

...'m trying to invoke the function bond_prices from termstrc package. Here is the code snippet: library(termstrc) data(eurobonds) b <- matrix(rep(c(0,0,0, 1),1),nrow=1,byrow=TRUE) group<-c("GERMANY") colnames(b) <- c("beta0","beta1","beta2","tau1") germaturities<-create_maturities_matrix(eurobonds$GERMANY) bp<-bond_prices(method = "Nelson/Siegel",b,germaturities,eurobonds$GERMANY$CASHFLOWS$CF) bp Running this, I get the following warning message: In cf * discount_factors : longer object length is not a multiple of sh...