[英]Is there any way to combine two different functions to one function in R?
[英]Make my Two Different R Functions to be Just One Function
我想在R
中的MonteCarlo
包中使用MonteCarlo
函数,它有一个要求,即just one single function
向MonteCarlo
包提供just one single function
。
要运行模拟研究,用户必须在单个函数中嵌套(生成样本和从该样本计算所需统计数据)。 这个函数被传递给 MonteCarlo()。 不需要额外的编程( 小插图:蒙特卡罗包)。
与这个重要条件相反,我有两个不同的函数适合我的算法。 我已使用此问题中正确答案提供的MonteCarlo
函数作为方法。
因此,我想使用不同的方法,我编写了以下函数(function1 和 function2)以将其传递给MonteCarlo
函数,如下所示:
这是我想用R
做什么的算法:
arima.sim()
函数模拟来自ARIMA
模型的 10 个时间序列数据集2s
、 3s
、 4s
、 5s
、 6s
、 7s
、 8s
和9s
重叠子系列。auto.arima()
函数从每个块大小的子系列中获得最佳ARIMA
模型。RMSE
。 下面的R
函数完成了这项工作。
library(MonteCarlo)
library(forecast)
library(Metrics)
############################################
function1 <- function(lb, ov, n) {
starts <- unique(sort(c(seq(1, n, lb), seq(lb-ov+1, n, lb))))
ends <- pmin(starts + lb - 1, n)
# truncate starts and ends to the first num elements
num <- match(n, ends)
head(data.frame(starts, ends), num)
}
#############################################
# parameter grids
n <- 10 # length of time series
lb <- seq(n-2) + 1 # vector of block sizes
phi <- 0.6 # autoregressive parameter
reps <- 3 # monte carlo replications
# simulation function
function2 <- function(n, lb, phi) {
#### simulate ####
ov <- ceiling(lb/2)
vblocks <- Vectorize(function1, c("lb", "ov"), SIMPLIFY = FALSE)
d <- vblocks(lb = lb, ov = ov, n = n)
ts <- arima.sim(n, model = list(ar = phi, order = c(1, 0, 0)), sd = 1)
#### devide ####
blk <- lapply(d, function(x) with(x, Map(function(i, j) ts[i:j], starts, ends)))
#### resample ####
res <- sample(blk, replace = TRUE, 10) # resamples the blocks
res.unlist <- unlist(res, use.names = FALSE) # unlist the bootstrap series
#### train, forecast ####
train <- head(res.unlist, round(length(res.unlist) - 10)) # train set
test <- tail(res.unlist, length(res.unlist) - length(train)) # test set
nfuture <- forecast(train, # forecast
model = auto.arima(train),
lambda = 0, biasadj = TRUE, h = length(test))$mean
### metric ####
RMSE <- rmse(test, nfuture) # return RMSE
return(
list("RMSE" = RMSE)
)
}
param_list = list("n" = n, "lb" = lb, "phi" = phi)
set.seed(123, kind = "L'Ecuyer-CMRG")
MC_result <- MonteCarlo(func = bootstrap4,
nrep = reps,
ncpus = parallel::detectCores() - 1,
param_list = param_list,
export_also = list(
"packages" = c("forecast", "Metrics")
),
raw = T)
我在运行上面的代码时遇到了这个错误:
in snowfall::sfExport("func2", "func", "libloc_strings", "function1", : 未知/未找到的变量以导出结束。(local=TRUE)
我想集成function1
到function2
的方式, function1
不会在函数2的功能。
这是我的审判
function2 <- function(n, lb, phi) {
#### simulate ####
ov <- ceiling(lb/2)
function1 <- head(data.frame(unique(sort(c(seq(1, n, lb), seq(lb-ov+1, n, lb)))), pmin(unique(sort(c(seq(1, n, lb), seq(lb-ov+1, n, lb)))) + lb - 1, n)), match(n, pmin(unique(sort(c(seq(1, n, lb), seq(lb-ov+1, n, lb)))) + lb - 1, n)))
vblocks <- Vectorize(function1, c("lb", "ov"), SIMPLIFY = FALSE)
d <- vblocks(lb = lb, ov = ov, n = n)
ts <- arima.sim(n, model = list(ar = phi, order = c(1, 0, 0)), sd = 1)
#### devide ####
blk <- lapply(d, function(x) with(x, Map(function(i, j) ts[i:j], unique(sort(c(seq(1, n, lb), seq(lb-ov+1, n, lb)))), pmin(unique(sort(c(seq(1, n, lb), seq(lb-ov+1, n, lb)))) + lb - 1, n))))
#### resample ####
res <- sample(blk, replace = TRUE, 10) # resamples the blocks
res.unlist <- unlist(res, use.names = FALSE) # unlist the bootstrap series
#### train, forecast ####
train <- head(res.unlist, round(length(res.unlist) - 10)) # train set
test <- tail(res.unlist, length(res.unlist) - length(train)) # test set
nfuture <- forecast(train, # forecast
model = auto.arima(train),
lambda = 0, biasadj = TRUE, h = length(test))$mean
### metric ####
RMSE <- rmse(test, nfuture) # return RMSE
return(
list("RMSE" = RMSE)
)
}
当我把它传递给这个时:
set.seed(123, kind = "L'Ecuyer-CMRG")
MC_result <- MonteCarlo(func = function2,
nrep = reps,
ncpus = parallel::detectCores() - 1,
param_list = param_list,
export_also = list(
"packages" = c("forecast", "Metrics")
),
raw = T)
我收到此错误消息:
3个节点产生错误; 第一个错误:找不到函数“vblocks”
我在试验中所做的只是将整个function1
作为单个语句放在function2
你可以把内容function1
成的身体function2
-包括变量赋值等。
library(MonteCarlo)
library(forecast)
library(ModelMetrics)
mc_f <- function(n, lb, phi) {
# Generate data
ov <- ceiling(lb / 2)
starts <- unique(sort(c(seq(1, n, lb), seq(lb - ov + 1, n, lb))))
ends <- pmin(starts + lb - 1, n)
num <- match(n, ends)
d <- head(data.frame(starts, ends), num)
ts <- arima.sim(n, model = list(ar = phi, order = c(1, 0, 0)), sd = 1)
blk <- mapply(
function(start, end) ts[start:end],
d$starts,
d$ends,
SIMPLIFY = FALSE
)
# Resample
res <- sample(blk, replace = TRUE, 10)
res.unlist <- unlist(res, use.names = FALSE)
# Train and forecast
train <- head(res.unlist, round(length(res.unlist) - 10))
test <- tail(res.unlist, length(res.unlist) - length(train))
nfuture <- forecast(train,
model = auto.arima(train),
lambda = 0, biasadj = TRUE, h = length(test))$mean
# Extract metric
RMSE <- rmse(test, nfuture)
list("RMSE" = RMSE)
}
reps <- 3
param_list <- list(n = 10, lb = seq(n - 2) + 1, phi = 0.6)
mc_result <- MonteCarlo(
func = mc_f,
nrep = reps,
ncpus = parallel::detectCores() - 1,
param_list = param_list
)
#> Grid of 8 parameter constellations to be evaluated.
#>
#> Simulation parallelized using 3 cpus.
#>
#> Progress:
#>
#> |==================================================================================| 100%
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