similar to: solving system of nonlinear equations

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

I can't run a function which generates random numbrers using linear congruential generator. My multiplier is a=5+8^6, increment is b=1 and modulo is m=2^30. the code I have written works for modulo upto m=2^28. For m= 2^29 , it says, can not allocate memory for the vector or something like that. For m= 2^31 or more, its says the argument "for i in 1:m " is too large in

Dear friends - I'm on winXP, R 2.71 - I have with some help dveloped this multivariate normal model, which gives very plausible results in WinBUGS even without any initial values specified. However, when I then try to run the same model via the bugs function in R2WinBUGS with inits specified as inits=NULL the program stops in a dead end. So I have tried to make inits for the bugs function

Dear List: the CODA and BOA packages for the analysis of MCMC output yield different results on two dignostic test of convergence: 1) Geweke's convergence diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that imply that the CODA and BOA packages implement different ``flavors'' of the same test? I paste below an example. Geweke's test

Dear R users: I was trying to fit a HMM with mixture of Gaussian into the dataset, and I tried to implement it by R2Winbugs. But I got the following errer. * Error in FUN(X[[1L]], ...) : .C(..): 'type' must be "real" for this format* Does anybody know what's the problem? Does R2Winbugs accept some matrix as inits? I would really appreciate your help. Thank you very much.

Hi, I am trying to do run the following model saved in "C:/bugs/sus.bug" model { for (i in 1:n){ y[i] ~ dpois(lamdba[i]) log(lambda[i]) <- mu+bmale[male[i]]+bschn[schn[i]]+epsilon[i] # epsilon[i] ~ dnorm(0,tau.epsilon) } mu ~ dnorm(0,.0001) bmale ~ dnorm(0,.0001) tau.epsilon <- pow(sigma.epsilon, -2) sigma.epsilon ~ dunif(0,100) for (j in

I'm trying to call BUGS from R. But it's not working. R freezes up and BUGS gives me a strange output in the log. Just to know, BUGS is registered. The modified date on the keys file is today (Dec. 9th). It should be fully registered so that I can use it fully. And, the BUGS model is syntactically correct. Any suggestions would be very helpful. Here is my BUGS model: model {

Hi all, Does anyone have an example of an autoregressive (AR) time-series model specified as a state space model in R? That is, I want to go beyond the locally linear (constant) model, and fit the following Gaussian AR state process model: Xt = a + (1+b)*Xt-1 + epsilon ,where the model for the observation process is Yt = Xt + tau I have information of the tau's (observation variance)

I am running a BUGS function with following   schools.sim <-bugs(data,inits,                              parameters,                              model.file="schools.txt",                              n.chains=3,                              n.iter=1000,                              bugs.directory="E:/Rprograms")   My model.file IS in the directory

I'm new to bugs, so please bear with me. Can someone tell me if the following two models are doing the same thing? The reason I ask is that with the same data, the first (based on 4 separate coeffs a1--a4) takes about 50 secs, while the second (based on a vectorized form, a[]) takes about 300. The means are about the same, though R-hat's in the second version are quite a bit better.

I wish to fit a dynamical model in R and I am running in a problem that requires some of your wisdom to solve. For SAS users I am searching for the equivalent of the */retain/ *statement. For people that want to read complicated explanations to help me: I have a system of two equations written as difference equations here. To boil it down. I have a dataframe with three variables y, X1, X2

Hello, I have problem running WinBUGS from R. The following example works in WinBUGS but it does not work in R through package R2WinBUGS. Does anyone know what the problem is? x <- c(0.2, 1.1, 1, 2.2, 2.5, 2.9, 2.9, 3.6, 3.8, 0.6, 1, 2, 2.4, 2.6, 2.8, 3.2, 3.9, 3.5) y <- c(0.5, 1.3, 0.1, 0.7, -0.4, 0.5, -0.9, -0.3, -0.3, 0.6, 0.4, 0.9, -0.1, -0.4, -0.5, -0.2, 0.3, -1.5) eco <- c(1, 3,

Hi, I'm working on the codes below however every time I run them when they get to OpenBUGS I keep getting the error message: array index is greater than array upper bound for hab. Any help would be greatly appreciated, Suzie Codes: ungulate <- read.csv(file.choose ()) #ungulate ungulate <- as.matrix(ungulate);colnames(ungulate)<-NULL;rownames(ungulate)<-NULL

Dear R-help, I'm using the R2WinBUGS package and getting an error message: Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file 'codaIndex.txt', reason 'No such file or directory' I'm using R 2.2.1 and WinBUGS 1.4.1 on a windows machine (XP). My R code and WinBUGS code is given below.

Hi All: I had a basic question to ask. I am running R2WinBUGS so that I could automate the running of my model using 1000 simulated datasets. Below is the code I am using. The only problem I am having is the bugs output that comes out shows my parameters as nos with 1 decimal place after. I would want to have the parameters with 5 places after decimal. How would I specify that in my code for

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

Dear all, how can I perform a repeated measures ANOVA using a covariance matrix as input? E.g., I have four repeated measures (N = 200) with mean vector tau (no BS factor): tau <- rbind(1.10, 2.51, 2.76, 3.52) and covariance matrix (sigma): sigma <- matrix(c(0.523, 0.268, 0.267, 0.211, 0.268, 0.444, 0.492, 0.571, 0.267, 0.492, 1.213,

Hi R users, where can I find the equations used by acf function to calculate autocorrelation? I think I misunderstand acf. Doesn't acf use following equation to calculate autocorrelation? [image: R(\tau) = \frac{\operatorname{E}[(X_t - \mu)(X_{t+\tau} - \mu)]}{\sigma^2}\, ,] If it does, then the autocorrelation of a sine function should give a cosine; however, the following code gives a

I have downloaded the Umacs (Universal Markov chain sampler) and submitted the following sample code from Kerman and Gelman.   s <-Sampler( J=8, sigma.y  =c(15,10,16,11,9,11,10,18),           y  =c(28, 8,-3,7,-1,1,18,12),      theta =Gibbs(theta.update,theta.init),           V =Gibbs(V.update,mu.init),         mu =Gibbs(mu.update,mu.init),         tau =Gibbs(tau.update,tau.init),