[英]Using apply for simulation instead of nested for loops
我用R復制了最初在Stata中完成的模擬。 我使用了“ for”循環,因為這是我知道如何進行此工作的唯一方法。 它需要很長時間才能運行,因此我想使用“ apply”命令之一來查看它是否更快,但是我不知道該怎么做。 有人可以幫忙嗎? 這是代碼:
simdiffuse <- function(a, b, c, d) {
endo <- 1/a # innovation endogenous effect
endomacro <- 1/b # category endogenous effect
appeal <- c # innovation's ex ante appeal
ninnov <- d # number of innovations in category
results <- data.frame(catdensity = rep(0:ninnov, each = 25), t = 1:25, endo = endo, endomacro = endomacro, appeal = appeal, adopt = NA)
prop <- rnorm(1000)
diff <- data.frame(prop)
diff$adopt <- 0
diff$adopt[1:5] <- 1
for (catdensity in 0:ninnov) {
diff$adopt <- 0
diff$adopt[1:5] <- 1
for (t in 1:25) {
results[results$catdensity == catdensity & results$t == t,]$adopt <- mean(diff$adopt)
for (obs in 1:nrow(diff)) {
if(appeal+(mean(diff$adopt)*endo)+(catdensity*endomacro) > rnorm(1, diff[obs,]$prop)) diff[obs,]$adopt <- 1
}
}
}
return(results)
}
results <- simdiffuse(.2, 20, -3, 60)
您可以使用data.table
來提高功能的速度。 但是,您仍然必須使用for
循環(這不是一件壞事)。
library(data.table)
simdiffuse <- function(a, b, c, d) {
endo <- 1/a # innovation endogenous effect
endomacro <- 1/b # category endogenous effect
appeal <- c # innovation's ex ante appeal
ninnov <- d # number of innovations in category
results <- data.table(catdensity = rep(0:ninnov, each = 25), t = 1:25,
endo = endo, endomacro = endomacro, appeal = appeal,
adopt = as.numeric(NA))
for (cc in 0:ninnov) {
diff <- data.table(prop = rnorm(1000), adopt = c(rep(1,5), rep(0, 995)))
for (tt in 1:25) {
results[catdensity == cc & t == tt, adopt := diff[, mean(adopt)]]
diff[, rr := rnorm(1, prop), by="prop"]
diff[appeal + mean(adopt) * endo + cc * endomacro > rr, adopt := 1]
}
}
return(results)
}
results <- simdiffuse(.2, 20, -3, 60)
我沒有足夠的時間等到您的代碼執行完畢,但是這是我得到的adopt
vs. t
的圖表:
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