[R] speed issue in simulating a stochastic process
Thomas Adams
tea3rd at gmail.com
Thu Nov 6 17:42:43 CET 2014
Matteo,
Ah — OK, N=20, I did not catch that. You have nested for loops, which R is
known to be exceedingly slow at handling — if you can reorganize the code
to eliminate the loops, your performance will increase significantly.
Tom
On Thu, Nov 6, 2014 at 7:47 AM, Matteo Richiardi <matteo.richiardi at gmail.com
> wrote:
> I wish to simulate the following stochastic process, for i = 1...N
> individuals and t=1...T periods:
>
> y_{i,t} = y_0 + lambda Ey_{t-1} + epsilon_{i,t}
>
> where Ey_{t-1} is the average of y over the N individuals computed at time
> t-1.
>
> My solution (below) works but is incredibly slow. Is there a faster but
> still clear and readable alternative?
>
> Thanks a lot. Matteo
>
> rm(list=ls())
> library(plyr)
> y0 = 0
> lambda = 0.1
> N = 20
> T = 100
> m_e = 0
> sd_e = 1
>
> # construct the data frame and initialize y
> D = data.frame(
> id = rep(1:N,T),
> t = rep(1:T, each = N),
> y = rep(y0,N*T)
> )
>
> # update y
> for(t in 2:T){
> ybar.L1 = mean(D[D$t==t-1,"y"])
> for(i in 1:N){
> epsilon = rnorm(1,mean=m_e,sd=sd_e)
> D[D$id==i & D$t==t,]$y = lambda*y0+(1-lambda)*ybar.L1+epsilon
> }
> }
>
> ybar <- ddply(D,~t,summarise,mean=mean(y))
>
> plot(ybar, col = "blue", type = "l")
>
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>
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