将数据帧拆分为重叠的数据帧

| 我正在尝试编写一个行为如下的函数，但事实证明这非常困难： 
DF <- data.frame(x = seq(1,10), y = rep(c(\'a\',\'b\',\'c\',\'d\',\'e\'),2))
> DF
    x y
1   1 a
2   2 b
3   3 c
4   4 d
5   5 e
6   6 a
7   7 b
8   8 c
9   9 d
10 10 e

>OverLapSplit(DF,nsplits=2,overlap=2)
[[1]]
  x y
1 1 a
2 2 b
3 3 c
4 4 d
5 5 e
6 6 a

[[2]]
   x y
1  5 a
2  6 b
3  7 c
4  8 d
5  9 e
6 10 a

>OverLapSplit(DF,nsplits=1)
[[1]]
    x y
1   1 a
2   2 b
3   3 c
4   4 d
5   5 e
6   6 a
7   7 b
8   8 c
9   9 d
10 10 e

>OverLapSplit(DF,nsplits=2,overlap=4)
[[1]]
  x y
1 1 a
2 2 b
3 3 c
4 4 d
5 5 e
6 6 a
7 7 b

[[2]]
   x y
1  4 e
2  5 a
3  6 b
4  7 c
5  8 d
6  9 e
7 10 a

>OverLapSplit(DF,nsplits=5,overlap=1)
[[1]]
  x y
1 1 a
2 2 b
3 3 c

[[2]]
  x y
1 3 c
2 4 d
3 5 e

[[3]]
  x y
1 5 e
2 6 a
3 7 b

[[4]]
  x y
1 7 b
2 8 c
3 9 d

[[5]]
   x y
1  8 d
2  9 e
3 10 f
我没想过如果尝试ѭ1之类的东西会发生什么 可能是以下情况： 
[[1]]
  x y
1 1 a
2 2 b
3 3 c
4 4 d
5 5 e

[[2]]
   x y
1  5 a
2  6 b
3  7 c
4  8 d
5  9 e
6 10 a
谢谢！     
2020-03-27 4 条评论
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没有找到相关结果

    已邀请:

    蕉衫

    尝试类似的东西： 
    OverlapSplit <- function(x,nsplit=1,overlap=2){
    nrows <- NROW(x)
    nperdf <- ceiling( (nrows + overlap*nsplit) / (nsplit+1) )
    start <- seq(1, nsplit*(nperdf-overlap)+1, by= nperdf-overlap )

    if( start[nsplit+1] + nperdf != nrows )
        warning(\"Returning an incomplete dataframe.\")

    lapply(start, function(i) x[c(i:(i+nperdf-1)),])
}
    用nsplit分割数！ （nsplit = 1返回2个数据帧）。万一重叠拆分不真正适合数据帧，这将呈现不完整的最后一个数据帧，并发出警告。 
    > OverlapSplit(DF,nsplit=3,overlap=2)
[[1]]
  x y
1 1 a
2 2 b
3 3 c
4 4 d

[[2]]
  x y
3 3 c
4 4 d
5 5 e
6 6 a

[[3]]
  x y
5 5 e
6 6 a
7 7 b
8 8 c

[[4]]
    x y
7   7 b
8   8 c
9   9 d
10 10 e
    还有一个警告 
    > OverlapSplit(DF,nsplit=1,overlap=1)
[[1]]
  x y
1 1 a
2 2 b
3 3 c
4 4 d
5 5 e
6 6 a

[[2]]
    x    y
6   6    a
7   7    b
8   8    c
9   9    d
10 10    e
NA NA <NA>

Warning message:
In OverlapSplit(DF, nsplit = 1, overlap = 1) :
  Returning an incomplete dataframe.
        
    2020-03-27 0 0
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    贸会

    这使用了莱迪思图形中的带状点子，因此利用软件包package6ѭ中的代码生成间隔，然后使用循环将原始DF分解为正确的子集。 我不确定ѭ7的含义-我想您的意思是1个样本/观测值重叠。如果是这样，下面的代码将执行此操作。 
    OverlapSplit <- function(x, nsplits = 1, overlap = 0) {
    stopifnot(require(lattice))
    N <- seq_len(nr <- nrow(x))
    interv <- co.intervals(N, nsplits, overlap / nr)
    out <- vector(mode = \"list\", length = nrow(interv))
    for(i in seq_along(out)) {
        out[[i]] <- x[interv[i,1] < N & N < interv[i,2], , drop = FALSE]
    }
    out
}
    这使： 
    > OverlapSplit(DF, 2, 2)
[[1]]
  x y
1 1 a
2 2 b
3 3 c
4 4 d
5 5 e
6 6 a

[[2]]
    x y
5   5 e
6   6 a
7   7 b
8   8 c
9   9 d
10 10 e

> OverlapSplit(DF)
[[1]]
    x y
1   1 a
2   2 b
3   3 c
4   4 d
5   5 e
6   6 a
7   7 b
8   8 c
9   9 d
10 10 e

> OverlapSplit(DF, 4, 1)
[[1]]
  x y
1 1 a
2 2 b
3 3 c

[[2]]
  x y
3 3 c
4 4 d
5 5 e

[[3]]
  x y
6 6 a
7 7 b
8 8 c

[[4]]
    x y
8   8 c
9   9 d
10 10 e
        
    2020-03-27 0 0
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    艾食魄轻县

    只是为了弄清楚我在这里做什么： 
    #Load Libraries
library(PerformanceAnalytics)
library(quantmod)

#Function to Split Data Frame
OverlapSplit <- function(x,nsplit=1,overlap=0){
    nrows <- NROW(x)
    nperdf <- ceiling( (nrows + overlap*nsplit) / (nsplit+1) )
    start <- seq(1, nsplit*(nperdf-overlap)+1, by= nperdf-overlap )

    if( start[nsplit+1] + nperdf != nrows )
        warning(\"Returning an incomplete dataframe.\")

    lapply(start, function(i) x[c(i:(i+nperdf-1)),])
}

#Function to run regression on 30 days to predict the next day
FL <- as.formula(Next(HAM1)~HAM1+HAM2+HAM3+HAM4)
MyRegression <- function(df,FL) {
  df <- as.data.frame(df)
  model <- lm(FL,data=df[1:30,])
  predict(model,newdata=df[31,])
}

#Function to roll the regression
RollMyRegression <- function(data,ModelFUN,FL) {
  rollapply(data, width=31,FUN=ModelFUN,FL,
    by.column = FALSE, align = \"right\", na.pad = FALSE)
}

#Load Data
data(managers)

#Split Dataset
split.data <- OverlapSplit(managers,2,30)
sapply(split.data,dim)

#Run rolling regression on each split
output <- lapply(split.data,RollMyRegression,MyRegression,FL)
output
unlist(output)
    这样，您可以在末尾用并行版本的lapply替换ѭ11，并稍微提高速度。 当然，考虑到处理器数量和数据集大小，现在存在优化拆分/重叠的问题。     
    2020-03-27 0 0
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