similar to: How to calculte the power of a matrix

Dear R family I have a following question. Suppose I have a matrix as follows, for instance: tau= 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 I want to have the inverse of the above matrix and then add some exponent to it. That is, I want to calculate tau to the (-m). For example, m=893. Thanks in advance Best regards Moohwan Kim

Dear R family When I run the command below, the error message came up. It seems like the problem is about computer capacity. It would be appreciated if anyone could give me a solution. ########### > N <- 415884 > tau <- diag(1, N)[c(N, 1:(N - 1)),] Error in array(0, c(n, p)) : 'dim' specifies too large an array Best Moohwan

Dear R family I am attempting to create a matrix. e.g., 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 How could I write a R program? Later I want to extend it to a N by N case. Thanks in advance best Moohwan

Dear R family, I have a question about how to detect some duplicate numeric observations. Suppose that I have two variables dataset. order value 1 0.52 2 0.23 3 0.43 4 0.21 5 0.32 6 0.32 7 0.32 8 0.32 9 0.32 10 0.12 11 0.46 12 0.09 13 0.32 14 0.25 ; Could you help me indicate where the duplicate observations in a row (e.g., 0.32) are? best, moohwan

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

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

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

Dear R family I have an arbitrary column vector. 1 2 4 0 7 5 0 0 0 9 11 12 When I attempt to take natural logarithm of the series, as you guess there is an error message. To overcome this problem, my idea is to replace a zero or zeros in a row with appropriate numbers. In order to implement it, I need to detect where zeros are. Then I am going to take the average of two adjacent neighbors. In the

Is there a way to do hierarchical (bayesian) logistic regression in R, the way we do it in BUGS? For example in BUGS we can have this model: model {for(i in 1:N) { y[i] ~ dbin(p[i],n[i]) logit(p[i]) <- beta0+beta1*x1[i]+beta2*x2[i]+beta3*x3[i] } sd ~ dunif(0,10) tau <- pow(sd, -2) beta0 ~ dnorm(0,0.1) beta1 ~ dnorm(0,tau) beta2 ~ dnorm(0,tau) beta3 ~

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 {

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.

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 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)

Dear R family, Suppose I have two series. order value 1 0.52 2 0.23 3 0.43 4 0.21 5 0.32 6 0.32 7 0.32 8 0.32 9 0.32 10 0.12 11 0.46 12 0.09 13 0.32 14 0.25 For these two series, I figured out the way to detect the locations of duplicate values. The next thing to do is remove the repeated values except for a value that would not be next to each other. In other words, while keeping the

Hi I have been wondering if there one can speed up calculating small powers of numbers such as x^8 using multiplication. In addition, one can be a bit clever and calculate x^8 using only 3 multiplies. look at this: > f1 <- function(x){x*x*x*x*x*x*x*x} > f2 <- function(x){x^8} > f3 <- function(x){x2 <- x*x;x4 <- x2*x2;return(x4*x4)} [so f1() and f2() and f3() are

Dear R family, I faced a technical problem in r coding. #s=t(dev)%*%dev/(nr-1) # dev (100,000 by 2) stands for deviation from the mean #sinv=solve(s) #t2=diag(dev%*%sinv%*%t(dev)) I got an error message at t2 statement: Error in diag(dev %*% si %*% t(dev)) : allocMatrix: too many elements specified Please let me know if there is a way to overcome this problem. best moohwan

Suppose I have a square matrix P P <- matrix(c(.3,.7, .7, .3), ncol=2) I know that > P * P Returns the element by element product, whereas > P%*%P Returns the matrix product. Now, P^2 also returns the element by element product. But, is there a slick way to write P %*% P %*% P Obviously, P^3 does not return the result I expect. Thanks, Harold [[alternative HTML version

a=[0.1,0.2,0.1,0.3,0.4] b=[0.2,0.3,0.1,0.2,0.5] c=[1,1,1,1,1] log(c+a-x*b) where x=unknown scale variable. int=$$log(c+a-x*b)dadb, where $ denotes integral sign. Actually, how could I calculate the integral's approximation? double summation? best, moohwan

I want to generate the following outcome using convolution of two sequences. x <- c(1,2,3,4,5) y <- c(6,7,8,9) The resulting convolution vector is 6 19 40 70 100 94 76 45 When using convolve(), it is hard to produce the result above. Would you help me out to get that? Best regards Moohwan Kim

Full_Name: Edward Huang Version: 2.6.1 OS: Mac OS 10.5.1 Leopard Submission from: (NULL) (71.198.106.232) I'm trying to run quantile regression on my data. I just couldn't make it work. The same dataset ran okay on STATA 10, tho. Would you please take a look at it? Here is the error message: *** caught segfault *** address 0x3ff00008, cause 'memory not mapped' Traceback: