我正在使用 R 编程语言。假设我有以下数据:
library("dplyr")
df <- data.frame(b = rnorm(100,5,5), d = rnorm(100,2,2),
c = rnorm(100,10,10))
a <- c("a", "b", "c", "d", "e")
a <- sample(a, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))
a<- as.factor(a)
df$a = a
> head(df)
b d c a
1 3.1316480 0.5032860 4.7362991 a
2 4.3111450 -0.1142736 -0.5841322 c
3 2.8291346 3.6107839 16.0684492 a
4 14.2142245 4.9893987 -1.8145138 a
5 -6.7381302 0.0416782 -7.7675387 c
6 0.4481874 0.3370716 17.4260801 a
我还有以下函数(“my_subset_mean”),它在给定特定输入选择的情况下评估“列 c”的平均值:
my_subset_mean <- function(r1, r2, r3){
subset <- df %>% filter(a %in% r1, b > r2, d < r3)
return(mean(subset$c))
}
my_subset_mean(r1 = c("a", "b"), r2 = 5, r3 = 1 )
[1] 5.682513
我的问题:我试图以“r1”、“r2”和“r3”的随机组合评估函数“my_subset_mean”。例如:
my_subset_mean(r1 = c("a", "b"), r2 = 5, r3 = 1 )
[1] 11.46365
my_subset_mean(r1 = c("a", "b"), r2 = 5, r3 = 1 )
[1] 11.46365
my_subset_mean(r1 = c("a"), r2 = 2, r3 = 0 )
[1] 14.59809
my_subset_mean(r1 = c("a", "b", "c"), r2 = 3.1, r3 = 0 )
[1] 11.26508
#I am not sure how to get this one to work (i.e. ignore "r1" all together and only calculate the mean using r2 and r3)
my_subset_mean(r1 = "NA", r2 = 3.1, r3 = 0 )
[1] NaN
etc.
是否可以制作一个“网格”,其中包含“r2”和“r3”的随机值(例如“r2”和“r3”的随机值介于 0 和 5 之间)以及“r1”的随机子集(例如“a ", "c, d", "b, a, e", "d"):
> head(my_grid)
r2 r3 r1
1 3.1316480 0.5032860 a, b
2 4.3111450 -0.1142736 c, d, e
3 2.8291346 3.6107839 a
4 14.2142245 4.9893987 b, e
5 -6.7381302 0.0416782 NA
6 0.4481874 0.3370716 e
然后在“my_grid”的每一行评估“my_subset_mean”?例如
#desired result
> head(final_answer)
r2 r3 r1 my_subset_mean
1 3.1316480 0.5032860 a, b 0.3
2 4.3111450 -0.1142736 c, d, e 0.1
3 2.8291346 3.6107839 a 0.55
4 14.2142245 4.9893987 b, e 0.6
5 -6.7381302 0.0416782 NA 0.51
6 0.4481874 0.3370716 e 0.16
如果不涉及“因子变量”,我想我可以使用迭代“for 循环”来完成此操作。但我不确定如何使用“my_grid”“馈送”函数(“my_subset_mean”)。有人可以告诉我如何做到这一点吗?
谢谢!
您可以编写一个函数来为 选择随机值r1
,r2
并r3
基于您拥有的数据。runif
将帮助您创建范围内的随机数。
create_output <- function() {
uv <- levels(df$a)
r1 <- sample(uv, sample(length(uv)))
rgb <- range(df$b)
rgd <- range(df$d)
r2 <- runif(1, rgb[1], rgb[2])
r3 <- runif(1, rgd[1], rgd[2])
my_subset_mean <- my_subset_mean(r1, r2, r3)
data.frame(r1 = toString(r1), r2, r3, my_subset_mean)
}
运行一次
create_output()
# r1 r2 r3 my_subset_mean
#1 d, c, e, a -0.5762248 -0.3233672 0.3470009
运行 100 次并绑定结果。
out <- do.call(rbind, replicate(100, create_output(), simplify = FALSE))
head(out)
# r1 r2 r3 my_subset_mean
#1 e, d -6.870120 4.9283288 12.604477
#2 d, c, b, e 13.730295 4.0619485 7.749107
#3 e -4.990023 5.4652763 13.441422
#4 c, a 2.095414 5.4337308 10.603865
#5 d, c, b, e -6.614294 -0.4182057 6.703294
#6 a, c, d, b, e 17.369292 3.9566795 7.749107
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