我试图弄清楚如何存储将要进行多次迭代的模拟输出。我在这里有一个简化的示例，其中我模拟了50年期间的冬季温度变化，我想这样做3次：
# function to draw the winter temperature in a particular year
draw_winter_temperature <- function(){
intercept <- winterIntercept
beta1 <- rnorm(1, winterBeta, winterBetaSD)
val = rnorm(1, intercept + beta1 * years[i], winterSigma)
return(val)
}
# parameters for winter temperature function
winterIntercept <- -0.633
winterBeta <- 0.08
winterBetaSD <- 0.04
winterSigma <- 1.30
# number of iterations
iter = seq(1,3,1)
# number of years to run in each simulation
years <- seq(1,50,1)
# matrix to store 50 winter temperatures for each iteration? Not sure best approach
temps <- matrix(NA, nrow = length(years), ncol = length(iter))
# start of nested for loop - loop over each iteration
for (i in seq_along(iter)) {
# within each iteration, loop over each year
for (j in seq_along(years)) {
# draw winter temp. anomaly
winterTemp <- draw_winter_temperature()
# store winter temps, not sure what to do here
temps <- winterTemp
}
}
我想我不确定这里的索引是将输出保存在我的空矩阵中（还是以其他格式保存）。我应该说，多年来，我的循环中还存储着其他函数和数据，因此，我通常对每次迭代中存储数据的最佳方法感到困惑。
答案 0 :(得分：1)
我会完全避免循环。创建包含迭代和年份的数据框：
data1 <- data.frame(iter = rep(1:3, each = 50),
year = rep(1:50, 3))
对函数的定义稍有不同，因此提供了所有默认参数：
draw_winter_temperature <- function(intercept = -0.633,
winterbeta = 0.08,
winterbetasd = 0.04,
wintersigma = 1.30,
year = 1){
beta1 <- rnorm(1, winterbeta, winterbetasd)
val <- rnorm(1, intercept + beta1 * year, wintersigma)
val
}
使用purrr::map_dbl()和dplyr::mutate()创建一个包含每年采样值的列：
library(dplyr)
library(purrr)
data1 <- data1 %>%
mutate(winterTemp = map_dbl(year, ~draw_winter_temperature(year = .)))
答案 1 :(得分：0)
我认为您临时需要[j，i]子集。另外，重新定义函数以跨年份参数移动：
> rm(list = ls())
>
> # parameters for winter temperature function
> winterIntercept <- -0.633
> winterBeta <- 0.08
> winterBetaSD <- 0.04
> winterSigma <- 1.30
>
> # number of iterations
> iter = seq(1,3,1)
>
> # number of years to run in each simulation
> years <- seq(1,50,1)
>
> # matrix to store 50 winter temperatures for each iteration? Not sure best approach
> temps <- matrix(NA, nrow = length(years), ncol = length(iter))
>
> draw_winter_temperature <- function(i) {
+ intercept <- winterIntercept
+ beta1 <- rnorm(1, winterBeta, winterBetaSD)
+ val = rnorm(1, intercept + beta1 * years[i], winterSigma)
+ return(val)
+ }
>
>
> # start of nested for loop - loop over each iteration
> for (i in seq_along(iter)) {
+
+ # within each iteration, loop over each year
+ for (j in seq_along(years)) {
+
+ # draw winter temp. anomaly
+ winterTemp <- draw_winter_temperature(j)
+
+ # store winter temps, not sure what to do here
+ temps[j, i] <- winterTemp
+
+ }
+ }
>
> temps
[,1] [,2] [,3]
[1,] 0.07484416 -2.09659305 1.36146643
[2,] 0.91077388 0.91630545 0.15919461
[3,] 2.07646459 1.47967364 -0.80436293
[4,] -1.06727442 -1.35760417 -2.23396119
[5,] 0.67833459 -0.49733006 -0.08458830
[6,] -2.94754201 -0.83298461 -0.09547269
[7,] -0.27718212 -1.26709144 1.07166328
[8,] -2.48913267 1.15023058 -0.45768205
[9,] 0.81201831 1.73791432 -1.12876304
[10,] -1.26716059 -0.59580422 -1.28796699
[11,] 1.73244202 1.45400888 -0.13374732
[12,] 0.06181381 1.42056333 -0.28493854
[13,] 1.32197105 -0.26822322 -0.64071024
[14,] 0.31681581 2.17959219 1.05139421
[15,] -0.40649165 0.40584711 -1.24801466
[16,] -0.37852770 -0.01912727 -1.57738425
[17,] 1.07342597 -0.37121912 -1.78913387
[18,] 2.37652192 2.34135284 1.57626300
[19,] 2.26526300 -0.22457651 1.22608845
[20,] 0.43476700 1.20513841 3.15556064
[21,] 2.59591314 -0.30772162 -0.18688845
[22,] 1.06465892 1.71525510 -1.96024033
[23,] 0.33484399 1.81225896 2.11832405
[24,] 1.90063594 3.51556209 1.07306117
[25,] -0.01940422 2.28460208 3.21963212
[26,] 3.28816991 2.95440512 3.24107428
[27,] 0.96753190 -0.10513793 -1.21244216
[28,] 2.54233795 -0.71844973 1.81812858
[29,] 2.17954621 2.98536138 3.66748584
[30,] 0.81877427 -1.35965678 2.56338093
[31,] 0.29897273 1.11832993 0.87086081
[32,] 3.41605791 4.72562882 4.07046433
[33,] 3.48441756 -0.09328612 1.64075691
[34,] -1.45902220 -0.38229884 4.62577573
[35,] 0.76568048 3.05618914 -2.04568644
[36,] 0.51939827 0.46442133 2.45079102
[37,] 3.31122478 3.72294771 3.79475270
[38,] 3.22139644 2.33785724 -0.38787472
[39,] 2.95662591 1.14475461 2.97768620
[40,] 0.41263989 3.12365715 -0.05141565
[41,] 1.38138984 3.76526281 1.96114126
[42,] -0.18932163 3.05150559 1.01252868
[43,] 2.60596068 4.58785954 -0.62515705
[44,] 4.11088547 1.61733269 2.51478319
[45,] 2.54634136 5.03258017 3.69739035
[46,] 3.90668226 5.10453707 9.44805817
[47,] -0.01088235 -0.72829163 5.23958084
[48,] 5.69817388 0.12111318 4.43908874
[49,] 0.62863949 1.44337538 6.37749772
[50,] 6.03095905 -1.06661804 2.96625244
也可以使用嵌套的sapply完成
：> sapply(seq(1, 3, 1), function(x){
+ sapply(seq(1, 50, 1), function(i) {draw_winter_temperature(i)})
+ })
[,1] [,2] [,3]
[1,] -0.89724777 -0.3248484 -0.90899374
[2,] -1.94179327 -1.1282839 0.31149541
[3,] -0.42025521 0.7724711 0.74888190
[4,] -1.33109020 1.9129040 0.87848969
[5,] -0.47958978 0.0847167 0.41101536
[6,] -0.79725942 -0.3091579 -1.24232376
[7,] 1.77518276 -0.1922316 -1.47421230
[8,] 0.69493304 -0.2019549 0.13570145
[9,] -1.30457205 0.3104541 0.42987422
[10,] 0.17036195 -1.7092747 -0.83657188
[11,] 0.32147101 -0.9007392 -1.19378838
[12,] 0.67253811 1.3546455 0.65988074
[13,] 1.50350007 0.5492711 1.79631547
[14,] -2.51666246 -0.9415391 -0.29810167
[15,] -0.40993616 -0.4815619 -0.64542730
[16,] 1.77118058 0.1315932 -0.75448968
[17,] 0.50048465 2.7956318 -0.87173764
[18,] -3.21569892 -0.3323585 -1.23359412
[19,] 0.31825702 0.3584551 0.06093396
[20,] 3.89144244 -2.4149095 1.78682882
[21,] -0.43390072 0.7793848 -0.10369739
[22,] -2.86730666 -0.7670008 1.52314298
[23,] -1.79228394 0.9572841 0.48042120
[24,] -0.43718475 -0.1275890 1.21647106
[25,] -1.15994958 1.6406028 4.60036997
[26,] 0.01317481 1.4994584 2.66739115
[27,] 4.17871392 0.7907429 2.73681377
[28,] 4.28581141 1.6371291 0.68691671
[29,] 1.93210763 2.9950647 4.21374229
[30,] 2.58606149 1.0610567 -0.85244266
[31,] 1.80716913 4.5783347 2.52572309
[32,] -0.59753770 1.4036211 4.67023114
[33,] -0.62456359 3.8556583 1.45738330
[34,] 3.97709081 1.9414286 2.13243487
[35,] -0.44078059 0.1845302 2.93266443
[36,] 3.95186775 0.6522162 1.33296220
[37,] 2.44364830 2.8547030 1.20660793
[38,] 4.41777409 1.9200504 2.71936027
[39,] 0.28099830 2.7876076 3.92335543
[40,] 3.40916089 0.1663190 0.42249808
[41,] 4.14691320 2.2004945 2.03032191
[42,] 0.61603992 4.6339235 2.82358688
[43,] 3.15565113 4.2119661 5.04334175
[44,] 2.63957253 5.9546210 2.77191218
[45,] -1.50822755 0.9417640 6.71999760
[46,] 1.29101617 3.2551090 1.59456129
[47,] 5.98411236 2.0040345 4.03826529
[48,] 2.33718663 -3.1029647 2.84654953
[49,] -1.77860722 4.4929658 2.93310143
[50,] 7.12512178 5.3284199 2.97240287
希望有帮助。