[英]Incorporating splitstackshape into loop
I have the following code that selects (4 rows of iris x 1000) *100 and calculates the bias of each column.我有以下代码选择(4 行虹膜 x 1000)*100 并计算每列的偏差。
library(SimDesign)
library(data.table)
do.call(rbind,lapply(1:100, function(x) {
bias(
setDT(copy(iris))[as.vector(sapply(1:1000, function(X) sample(1:nrow(iris),4)))][
, lapply(.SD, mean), by=rep(c(1:1000),4), .SDcols=c(1:4)][,c(2:5)],
parameter=c(5,3,2,1), #parameter is the true population value used to calculate bias
type='relative' #denotes the type of bias being calculated
)
}))
This takes 1000 samples of 4 rows, calculates the mean by sample #, giving me 1000 means.这需要 4 行的 1000 个样本,通过样本 # 计算平均值,给我 1000 个平均值。 The bias for the 1000 means is found for each column, and then is done 99 more times giving me a distribution of bias estimates for each column.
为每一列找到 1000 均值的偏差,然后再进行 99 次,为我提供每一列的偏差估计分布。 This is mimicking a random sampling design.
这是在模仿随机抽样设计。 However, I also want to do this for a stratified design.
但是,我也想为分层设计这样做。 So I use
splitstackshape
's stratified
function.所以我使用
splitstackshape
的stratified
function。
do.call(rbind,lapply(1:100, function(x) {
bias(
setDT(copy(iris))[as.vector(sapply(1:1000, function(X) stratified(iris,group="Species", size=1)))][
, lapply(.SD, mean), by=rep(c(1:1000),4), .SDcols=c(1:4)][,c(2:5)],
parameter=c(5,3,2,1),
type='relative'
)
}))
I would've thought that it is just a matter of swapping out the functions, but I keep on getting errors (i is invalid type (matrix)) .我原以为这只是换出函数的问题,但我不断收到错误(i is invalid type (matrix)) 。 Perhaps in future a 2 column matrix could return a list of elements of DT.
也许将来一个 2 列矩阵可以返回 DT 的元素列表。 I think it might be something related to setDT, but I'm not really sure how to fix it.
我认为这可能与 setDT 有关,但我不确定如何修复它。 Anybody know where I'm going wrong?
有人知道我哪里出错了吗?
I've split into a couple of functions for you我为你分成了几个功能
library(SimDesign)
library(data.table)
library(splitstackshape)
n
stratified samples of size sampsize
and return column means of those samples n
大小为sampsize
的分层样本并返回这些样本的列均值get_samples <- function(n, sampsize=4) {
rbindlist(lapply(1:n, function(x) {
splitstackshape::stratified(iris, group="Species",sampsize)[, id:=x]
}))[, lapply(.SD, mean), by=.(Species, id)]
}
y
such iterations of these samplesy
个这样的迭代中的偏差分布get_bias_distribution <- function(y=100, samples_per_iter=50, size_per_iter=4) {
rbindlist(lapply(1:y, function(y) {
samples = get_samples(samples_per_iter, sampsize=size_per_iter)[, id:=NULL]
samples[, as.list(bias(
estimate=.SD,parameter=c(5,3,2,1),type="relative")*100),
by=.(Species)][, iter:=y]
}))
}
get_bias_distribution()
Output: Output:
Species Sepal.Length Sepal.Width Petal.Length Petal.Width iter
1: setosa -1.236667 22.61833 -26.70000 -39.69667 1
2: versicolor 46.476667 -11.99500 115.12833 16.82167 1
3: virginica 80.596667 -0.20000 180.21833 53.89000 1
4: setosa -1.513333 20.87000 -27.46167 -38.83667 2
5: versicolor 45.333333 -11.34833 112.84833 17.84500 2
---
296: versicolor 48.250000 -12.26833 113.37000 17.71167 99
297: virginica 77.366667 -2.87000 175.60000 53.07167 99
298: setosa -1.005000 22.67500 -27.02833 -39.69500 100
299: versicolor 47.921667 -10.28333 110.97833 16.86833 100
300: virginica 76.153333 -2.44000 174.46167 52.62167 100
stratified(iris,group="Species", size=1)
, you will get a 3 row data.table, because you are effectively selecting one row at random from each of the three Speciesstratified(iris,group="Species", size=1)
时,您将得到 3 行 data.table,因为您实际上是从三个物种中的每一个中随机选择一行 Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1: 4.9 3.6 1.4 0.1 setosa
2: 6.3 2.5 4.9 1.5 versicolor
3: 7.7 2.8 6.7 2.0 virginica
sapply(1:1000, function(x)...)
, you get 5 x 1000 column matrix, where each column is contains 5 lists of length 3.. Below, I'm showing you what this looks like if you did sapply(1:6, function(x)...)
sapply(1:1000, function(x)...)
中时,您会得到 5 x 1000 列矩阵,其中每列包含 5 个长度为 3 的列表。下面,我将向您展示它的外观就像你做了sapply(1:6, function(x)...)
[,1] [,2] [,3] [,4] [,5] [,6]
Sepal.Length numeric,3 numeric,3 numeric,3 numeric,3 numeric,3 numeric,3
Sepal.Width numeric,3 numeric,3 numeric,3 numeric,3 numeric,3 numeric,3
Petal.Length numeric,3 numeric,3 numeric,3 numeric,3 numeric,3 numeric,3
Petal.Width numeric,3 numeric,3 numeric,3 numeric,3 numeric,3 numeric,3
Species factor,3 factor,3 factor,3 factor,3 factor,3 factor,3
This is not really what you want, because you cannot then lapply
over these the way you then intended.这并不是你真正想要的,因为你不能按照你当时
lapply
的方式应用这些。 What you want to do instead is use lapply(1:1000, function(x)...)
to create a list of such 3-row datatables, and then bind them together (after adding an id
column to each one).您要做的是使用
lapply(1:1000, function(x)...)
创建此类 3 行数据表的列表,然后将它们绑定在一起(在为每个数据表添加一个id
列之后)。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.