On 11.11.2011 18:48, Gyanendra Pokharel wrote:
Following is my code, can some one help on the error at the bottom?
mh<-function(iterations,alpha,beta){
+ data<-read.table("epidemic.txt",header = TRUE)
+ attach(data, warn.conflicts = F)
+ k<-97
+ d<- (sqrt((x-x[k])^2 + (y-y[k])^2))
+
Following is my code, can some one help on the error at the bottom?
> mh<-function(iterations,alpha,beta){
+ data<-read.table("epidemic.txt",header = TRUE)
+ attach(data, warn.conflicts = F)
+ k<-97
+ d <- (sqrt((x-x[k])^2 + (y-y[k])^2))
+ p <- 1-exp(-alpha*d^(-beta))
+ p.alpha<-1
Oops, I forgot the 'cumsum' stuff. Here it is again, hopefully working
this time.
> require(MASS)
> fz <- function(n, t, rho){
+ f <- array(dim = c(t, 2, n))
+ V <- matrix(c(1, rho, rho, 1), ncol = 2)
+ for(i in 1 : n){
+ f[,, i] <- apply(mvrnorm(n = t, mu = c(0,0), Sigma = V
Dear Giovanni,
Thanks so much for your answer, but your script returns gaussian white
noise not a random walk, at least the time series generated don't have
the expected periodogram for a random walk. That's the reason why I use
cumsum, the sum of a white noise is a easy way produce a random
Hi Sergio,
Your function does not estimate what you want. In fact it does not
estimate anything useful. A random walk is not stationary; in
particular, the variance at time t is t. Therefore, estimating variances
based on one run, averaging over time, does not make any sense. This is
what you are
On May 10, 2010, at 2:55 PM, Sergio Andrés Estay Cabrera wrote:
Dear R users and specially Albyn and Giovanni,
thanks for your answers, but in fact I supposed the same at the
beginning of my problem. However, when I generate the data seldom I
obtain the expected correlation. For example us
Dear R users and specially Albyn and Giovanni,
thanks for your answers, but in fact I supposed the same at the
beginning of my problem. However, when I generate the data seldom I
obtain the expected correlation. For example using this code:
fz<-function(n,t,rho){
f<-NULL
for(i in 1:n){
s<-rmv
Sums of correlated increments have the same correlation as the original
variables...
library(mvtnorm)
X<- matrix(0,nrow=1000,ncol=2)
for(i in 1:1000){
Y <- rmvnorm(1000,mean=mu,sigma=S)
X[i,] <- apply(Y,2,sum)
}
cor(Y)
[,1] [,2]
[1,] 1.000 0.4909281
[2,] 0.4909281 1.0
Hi everybody,
I am trying to generate two random walks with an specific correlation,
for example, two random walks of 200 time steps with a correlation 0.7.
I built the random walks with:
x<-cumsum(rnorm(200, mean=0,sd=1))
y<-cumsum(rnorm(200, mean=0,sd=1))
but I don't know how to fix the c
This is beautiful, thank you!
Greetings
Johannes
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'l')
> lines(walls, col = 'red')
>
> # shift the walk to be centred around 0. **The walk doesn't start at
> zero any more** walls <- walls - wid/2
>
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Charles C.
On Wed, 24 Oct 2007, Johannes H?sing wrote:
Dear expeRts,
recently I asked for a nice way to re-program a problem
without using control constructs such as "for" or
"sapply(1:length(x), ...". Is there a way to program
a random walk with a reflecting boundary without resorting
to such constructs?
Dear expeRts,
recently I asked for a nice way to re-program a problem
without using control constructs such as "for" or
"sapply(1:length(x), ...". Is there a way to program
a random walk with a reflecting boundary without resorting
to such constructs? A working solution is
ranwalk <- function(leng
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