如何在排除NaN的情况下计算矩阵列表的元素均值

Question

假设我有一个矩阵列表。如何计算另一个矩阵，该矩阵的元素是矩阵列表的按元素表示的平均值，并考虑和排除NaN？假设我们有一个矩阵列表：)

> A
     [,1] [,2] [,3]
[1,]   1    4    7
[2,]   2    5    8
[3,]   3    6    9

> B
     [,1] [,2] [,3]
[1,]   2    5    8
[2,]  NaN  NaN  NaN
[3,]   4    7   10

> C
     [,1] [,2] [,3]
[1,]   3    3    6
[2,]   2    3    7
[3,]  NaN  NaN  NaN

> my.list <- list(A, B, C)

我想要的输出是：

     [,1] [,2] [,3]
[1,]   2    4    7
[2,]   2    4   7.5
[3,]  3.5  6.5  9.5

如您所见，我希望计算列表的元素均值，但不包括列表中矩阵的NaN值。例如，输出的元素1,1是(1 +2+ 3) /3= 2，但是输出的元素3,1是(3 + 4) /2= 3.5，因为我们排除了矩阵B中那个位置的NaN。知道如何计算吗？

Answer 1

转换为array，然后使用apply循环遍历并获取mean

 apply(simplify2array(my.list), c(1, 2), FUN = mean, na.rm = TRUE)

-ouptut

      [,1] [,2] [,3]
[1,]  2.0  4.0  7.0
[2,]  2.0  4.0  7.5
[3,]  3.5  6.5  9.5

或者可以循环遍历一个维度并使用rowMeans

apply(simplify2array(my.list), 2, rowMeans, na.rm = TRUE)
     [,1] [,2] [,3]
[1,]  2.0  4.0  7.0
[2,]  2.0  4.0  7.5
[3,]  3.5  6.5  9.5

或者另一种选择是

library(dplyr)
library(purrr)
library(data.table)
 map_dfr(my.list, as.data.frame, .id = 'grp') %>% 
   group_by(grp = rowid(grp)) %>% 
   summarise(across(everything(), mean, na.rm = TRUE), 
       .groups = 'drop') %>%
   select(-grp)
# A tibble: 3 x 3
     V1    V2    V3
  <dbl> <dbl> <dbl>
1   2     4     7  
2   2     4     7.5
3   3.5   6.5   9.5

数据

my.list <- list(structure(1:9, .Dim = c(3L, 3L)), structure(c(2, NaN, 4, 
5, NaN, 7, 8, NaN, 10), .Dim = c(3L, 3L)), structure(c(3, 2, 
NaN, 3, 3, NaN, 6, 7, NaN), .Dim = c(3L, 3L)))

Answer 2

  # input prepare
  A= matrix(1:9,nrow=3,ncol=3)
  B = matrix(2:10,nrow=3,ncol=3)
  B[2,] = NaN
  C =matrix(c(3,2,NaN,3,3,NaN,6,7,NaN),nrow=3,ncol=3)

  # flatten every matrix as a column of rowMeans big matrix
  # so rowMeans do element-wise process
  mat.as.vec.list = lapply(list(A,B,C), as.vector)
  bind.by.col= do.call(cbind, mat.as.vec.list)

  
  mean.by.row = rowMeans(bind.by.col, na.rm = T)

  #convert back to matrix
  dim(mean.by.row) = dim(A)
  mean.by.row

     [,1] [,2] [,3]
[1,]  2.0  4.0  7.0
[2,]  2.0  4.0  7.5
[3,]  3.5  6.5  9.5