I need to create an accumulation index across columns in a matrix

1
votes

I need to create an accumulation index across columns in my data. I set up the problem as follows

#accumulation function
mat1 <- matrix(nrow=16, ncol =4)
mat1[1,] <- c(1,1,1,1)
mat1[2:16,] <- 1+rnorm(60,0,0.1)

   [,1]      [,2]      [,3]      [,4]
 [1,] 1.0000000 1.0000000 1.0000000 1.0000000
 [2,] 0.9120755 0.9345682 0.8533162 0.8737582
 [3,] 0.7838427 0.9691806 0.8216284 0.9863669
 [4,] 0.9095204 1.1906031 1.0253083 1.0700338
 [5,] 1.0202524 0.9974672 1.1348315 1.1115018
 [6,] 0.9456184 1.1250529 1.0348011 0.9323336
 [7,] 1.0053195 0.9917475 1.0178855 1.0880626
 [8,] 0.9550709 0.9107060 0.8876688 0.9060996
 [9,] 1.0728177 1.0559643 0.9161789 0.9711522
[10,] 0.9579642 1.0082560 0.9833227 0.9306639
[11,] 1.0044883 1.1323498 1.0388025 0.8926033
[12,] 0.8777846 0.9940302 0.8314166 0.8479962
[13,] 1.1042297 0.9767410 0.9355374 0.8859680
[14,] 1.1245737 0.8291948 1.0491585 0.9887672
[15,] 0.9687700 0.9915095 0.8962534 1.0220163
[16,] 0.9432597 1.0310273 0.9288159 1.0838243

The desired output takes the product of entries in each column, up to each row number.

therefore:

mat2 <- matrix(nrow=16, ncol=4)
mat2[1,] <- c(1,1,1,1)
mat2[2,] <- mat1[1,]*mat1[2,]
mat2[3,] <-mat1[1,]*mat1[2,]*mat1[3,]
mat2[4,] <-mat1[1,]*mat1[2,]*mat1[3,]*mat1[4,]

and so on and so forth up to row 16. The idea is to accumulate (take the product) of all entries in mat1 up to a particular row number. So row1 of mat2 = row 1 of mat 1. row 2 of mat 2, is equal to row1 mat1 *row2 mat1. row3 of mat2 is equal to row1 of mat1 *row2 of mat1, *row3 of mat1. This process continues up to row 16.

I need to write a function able to do this calculation for matrices in a list all of the same size.

2
rmatrixaccumulate

2 Answers

1
votes

Basically what you need is cumulative product over each column which can be applied using cumprod function in base R

apply(mat1, 2, cumprod)

#        [,1]   [,2]   [,3]   [,4]
# [1,] 1.0000 1.0000 1.0000 1.0000
# [2,] 0.8793 0.9890 1.1102 0.9031
# [3,] 0.9037 0.9384 1.0574 0.8031
# [4,] 1.0017 0.8529 0.9824 0.7026
# [5,] 0.7667 0.7815 0.9332 0.6658
# [6,] 0.7996 0.9703 0.7811 0.6327
# [7,] 0.8401 0.9833 0.6899 0.5184
# [8,] 0.7918 0.9351 0.5395 0.4883
# [9,] 0.7485 0.8939 0.4672 0.4341
#[10,] 0.7063 0.9350 0.4534 0.3901
#[11,] 0.6434 0.8701 0.4323 0.3837
#[12,] 0.6127 0.7441 0.4950 0.4053
#[13,] 0.5515 0.7869 0.4421 0.4721
#[14,] 0.5087 0.7063 0.4043 0.4356
#[15,] 0.5120 0.7052 0.3929 0.5056
#[16,] 0.5611 0.6392 0.3538 0.4470

data

set.seed(1234)
mat1 <- matrix(nrow=16, ncol =4)
mat1[1,] <- c(1,1,1,1)
mat1[2:16,] <- 1+rnorm(60,0,0.1)
1
votes

We can make use of rowCumprods from matrixStats which would be efficient

library(matrixStats)
rowCumprods(mat1)
#       [,1]      [,2]      [,3]      [,4]
# [1,] 1.0000000 1.0000000 1.0000000 1.0000000
# [2,] 0.8792934 0.8695961 0.9654515 0.8719461
# [3,] 1.0277429 0.9752243 0.9288433 0.8259908
# [4,] 1.1084441 1.0074432 0.9359711 0.8187889
# [5,] 0.7654302 0.7013506 0.6661948 0.6312977
# [6,] 1.0429125 1.2948629 1.0839177 1.0300632
# [7,] 1.0506056 1.0646930 0.9403774 0.7705423
# [8,] 0.9425260 0.8962776 0.7008855 0.6600887
# [9,] 0.9453368 0.9036902 0.7825060 0.6957347
#[10,] 0.9435548 0.9869196 0.9578751 0.8606545
#[11,] 0.9109962 0.8477986 0.8082998 0.7951804
#[12,] 0.9522807 0.8143710 0.9324137 0.9849138
#[13,] 0.9001614 0.9518986 0.8501747 0.9902680
#[14,] 0.9223746 0.8279552 0.7571348 0.6985816
#[15,] 1.0064459 1.0049223 0.9767219 1.1335746
#[16,] 1.0959494 0.9933742 0.8945990 0.7910216

data

set.seed(1234)
mat1 <- matrix(nrow=16, ncol =4)
mat1[1,] <- c(1,1,1,1)
mat1[2:16,] <- 1+rnorm(60,0,0.1)