我正在重寫一些代碼，我目前正在創建一個小型人口模型。我從一本書中重新創建了下面的當前模型函式，它是一個基于幾個引數的簡單人口模型。我將它們保留為默認值并回傳資料框。一切正常。但是，我想知道是否可以以某種方式從函式中排除回圈。
我知道 R 因為矢量化計算而很棒，但我不確定在這種情況下是否可能。我想過使用諸如領先/滯后之類的東西來做到這一點，但這行得通嗎？也許不是因為事情需要按順序計算？
# Nt numbers at start of time t
# Ct = removed at the end of time t
# Nt0 = numbers at time 0
# r = intrinsic rate of population growth
# K = carrying capacity
mod_fun = function (r = 0.5, K = 1000, N0 = 50, Ct = 0, Yrs = 10, p = 1)
{
# sets years to year value plus 1
yr1 <- Yrs 1
# creates sequence of length years from year 1 to Yrs value !
years <- seq(1, yr1, 1)
# uses years length to create a vector of length Yrs 1
pop <- numeric(yr1)
# sets population at time 0
pop[1] <- N0
# creates a loop that calculates model for each year after first year
for (i in 2:yr1) {
# sets starting value of population for step to one calculated previous step
# thus Nt is always the previous step pop size
Nt <- pop[i - 1]
pop[i] <- max((Nt (r * Nt/p) * (1 - (Nt/K)^p) -
Ct), 0)
}
# sets pop2 to original pop length
pop2 <- pop[2:yr1]
# binds together years (sequence from 1 to length Yrs),
# pop which is created in loop and is the population at the start of step t
# pop2 which is the population at the end of step t
out <- cbind(year = years, nt = pop, nt1 = c(pop2, NA))
# sets row names to
rownames(out) <- years
out <- out[-yr1, ]
#returns data.frame
return(out)
}
result = mod_fun()
這就是輸出的樣子。基本上從第 1 行開始，給定 50 的起始人口，回圈計算 nt1，然后將下一個 nt 行設定為 lag(nt1)，然后事情以類似的方式繼續。
result
#> year nt nt1
#> 1 1 50.0000 73.7500
#> 2 2 73.7500 107.9055
#> 3 3 107.9055 156.0364
#> 4 4 156.0364 221.8809
#> 5 5 221.8809 308.2058
#> 6 6 308.2058 414.8133
#> 7 7 414.8133 536.1849
#> 8 8 536.1849 660.5303
#> 9 9 660.5303 772.6453
#> 10 10 772.6453 860.4776
Created on 2022-04-24 by the reprex package (v2.0.1)
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mod_fun = function (r = 0.5, K = 1000, N0 = 50, Ct = 0, Yrs = 10, p = 1)
{
years <- seq_len(Yrs)
pop <- Reduce(function(Nt, y)max((Nt (r * Nt/p) * (1 - (Nt/K)^p) - Ct), 0),
years, init = N0, accumulate = TRUE)
data.frame(year = years, nt = head(pop,-1), nt1 = pop[-1])
}
year nt nt1
1 1 50.0000 73.7500
2 2 73.7500 107.9055
3 3 107.9055 156.0364
4 4 156.0364 221.8809
5 5 221.8809 308.2058
6 6 308.2058 414.8133
7 7 414.8133 536.1849
8 8 536.1849 660.5303
9 9 660.5303 772.6453
10 10 772.6453 860.4776
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