将n个函数的列表应用于数据帧的每一行？

I have a list of functions

我有一个功能列表

funs <- list(fn1 = function(x) x^2,
             fn2 = function(x) x^3,               
             fn3 = function(x) sin(x),
             fn4 = function(x) x+1)
#in reality these are all f = splinefun()

And I have a dataframe:

我有一个数据帧：

mydata <- data.frame(x1 = c(1, 2, 3, 2),
                     x2 = c(3, 2, 1, 0),
                     x3 = c(1, 2, 2, 3),
                     x4 = c(1, 2, 1, 2))
#actually a 500x15 dataframe of 500 samples from 15 parameters

For each of i rows, I would like to evaluate function j on each of the j columns and sum the results:

对于i行中的每一行，我想评估每个j列上的函数j并对结果求和：

unlist(funs)
attach(mydata)
a <- rep(NA,4)
for (i in 1:4) {
     a[i] <- sum(fn1(x1[i]), fn2(x2[i]), fn3(x3[i]), fn4(x4[i]))
}

How can I do this efficiently? Is this an appropriate occasion to implement plyr functions? If so, how?

我怎样才能有效地做到这一点？这是实施plyr功能的适当时机吗？如果是这样，怎么样？

bonus question: why is a[4] NA?

奖金问题：为什么是[4] NA？

Is this an appropriate time to use functions from plyr, if so, how can I do so?

这是使用plyr函数的合适时间，如果是，我该怎么办？

3 个解决方案

#1

Ignoring your code snippet and sticking to your initial specification that you want to apply function j on the column number j and then "sum the results"... you can do:

忽略你的代码片段并坚持你想要在列号j上应用函数j的初始规范然后“求和结果”......你可以这样做：

mapply( do.call, funs, lapply( mydata, list))
#      [,1] [,2]      [,3] [,4]
# [1,]    1   27 0.8414710    2
# [2,]    4    8 0.9092974    3
# [3,]    9    1 0.9092974    3

I wasn't sure which way you want to now add the results (i.e. row-wise or column-wise), so you could either do rowSums or colSums on this matrix. E.g:

我不确定你现在想要以哪种方式添加结果（即逐行或逐列），因此你可以在这个矩阵上做rowSums或colSums。例如：

colSums( mapply( do.call, funs,  lapply( mydata, list)) )
# [1] 14.000000 36.000000  2.660066  8.000000

#2

Why don't just write one function for all 4 and apply it to the data frame? All your functions are vectorized, and so is splinefun, and this will work:

为什么不为所有4编写一个函数并将其应用于数据框？你的所有函数都是矢量化的，splinefun也是如此，这样就可以了：

fun <-  function(df)
    cbind(df[, 1]^2, df[, 2]^3, sin(df[, 3]), df[, 4] + 1)

rowSums(fun(mydata))

This is considerably more efficient than "foring" or "applying" over the rows.

这比在行上“强制”或“应用”要高效得多。

#3

I tried using plyr::each:

我尝试使用plyr :: each：

library(plyr)
sapply(mydata, each(min, max))
    x1 x2 x3 x4
min  1  0  1  1
max  3  3  3  2

and it works fine, but when I pass custom functions I get:

它工作正常，但当我通过自定义函数时，我得到：

sapply(mydata, each(fn1, fn2))
Error in proto[[i]] <- fs[[i]](x, ...) :
  more elements supplied than there are to replace

each has very brief documentation, I don't quite get what's the problem.

每个都有非常简短的文档，我不太明白这是什么问题。

#1