试图将我的注意力集中在矢量化上,试图更快地进行一些模拟,我发现了这个非常基本的流行病模拟。代码来自书http://www.amazon.com/Introduction-Scientific-Programming-Simulation-Using/dp/1420068725/ref=sr_1_1?ie=UTF8&qid=1338069156&sr=8-1
#program spuRs/resources/scripts/SIRsim.r
SIRsim <- function(a, b, N, T) {
# Simulate an SIR epidemic
# a is infection rate, b is removal rate
# N initial susceptibles, 1 initial infected, simulation length T
# returns a matrix size (T+1)*3 with columns S, I, R respectively
S <- rep(0, T+1)
I <- rep(0, T+1)
R <- rep(0, T+1)
S[1] <- N
I[1] <- 1
R[1] <- 0
for (i in 1:T) {
S[i+1] <- rbinom(1, S[i], (1 - a)^I[i])
R[i+1] <- R[i] + rbinom(1, I[i], b)
I[i+1] <- N + 1 - R[i+1] - S[i+1]
}
return(matrix(c(S, I, R), ncol = 3))
}
模拟的核心是for
循环。我的问题是,因为代码从 S[i]
生成 S[i+1]
和 R[i+1]
值和 R[i]
值,是否可以使用 apply 函数对其进行矢量化?
非常感谢
最佳答案
迭代计算很难“矢量化”,但这是一种模拟,并且模拟可能会运行多次。因此,通过添加参数 M(要执行的模拟次数)、分配 M x (T + 1) 矩阵,然后填充连续的列,编写此代码以同时执行所有模拟每次模拟的(次)。这些更改似乎非常简单(所以我可能犯了一个错误;我特别关心 rbinom 的第二个和第三个参数中向量的使用,尽管这与文档)。
SIRsim <- function(a, b, N, T, M) {
## Simulate an SIR epidemic
## a is infection rate, b is removal rate
## N initial susceptibles, 1 initial infected, simulation length T
## M is the number of simulations to run
## returns a list of S, I, R matricies, each M simulation
## across T + 1 time points
S <- I <- R <- matrix(0, M, T + 1)
S[,1] <- N
I[,1] <- 1
for (i in seq_along(T)) {
S[,i+1] <- rbinom(M, S[,i], (1 - a)^I[,i])
R[,i+1] <- R[,i] + rbinom(M, I[,i], b)
I[,i+1] <- N + 1 - R[,i+1] - S[,i+1]
}
list(S=S, I=I, R=R)
}
关于r - 矢量化模拟,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/10770107/