Element-Wise Mean in R

Element-wise mean in R

how about:

rowMeans(cbind(a, b), na.rm=TRUE)

or

colMeans(rbind(a, b), na.rm=TRUE)

Element-wise mean over list of matrices

You can use:

Reduce("+", my.list) / length(my.list)

According to comments, you want both mean and sd implemented on a list of matrices, and the above ways will not work smoothly for sd. Try this instead :

apply(simplify2array(my.list), 1:2, mean)
apply(simplify2array(my.list), 1:2, sd)

Element wise mean of multiple lists in R

Assuming your As are lists of vectors:

Anames <- paste0("A", 1:10)

# for completeness
for(A in Anames)
    assign(A, lapply(1:7, function(x) rnorm(1000)))

sapply(1:7, function(i)
{
    m <- sapply(Anames, function(A) get(A)[[i]])
    mean(m)
})

This avoids building a copy of all your As in memory, instead retrieving them one at a time and extracting the desired vector. But if you have enough memory to store all that data, you could probably afford to store a copy as well.

Element wise mean for a list of dataframes with NA

Here is an approach that uses data.table

The steps are (1) coerce each data.frame [element] in x to data.table, with a column (called rn) identifying the rownames. (2) on the large data.table, by rowname calculate the mean of each column (with na.rm = TRUE dealing with NA values). (3) remove the rn column

library(data.table)

results <- rbindlist(lapply(x,data.table, keep.rownames = TRUE))[,
                     lapply(.SD, mean,na.rm = TRUE),by=rn][,rn := NULL]

an alternative would be to coerce to matrix, "simplify" to a 3-dimensional array then apply a mean over the appropriate margins

    # for example

    results <- as.data.frame(apply(simplify2array(lapply(x, as.matrix)),1:2,mean, na.rm = TRUE))

Sapply function for element-wise calculation a matrix in R

We may use elementwise subtraction of column with outer

outer(data[,1], data[,1], `-`)

If it should be done on each column, loop over the columns (or do asplit with MARGIN = 2 to split by column), loop over the list and apply the outer

lapply(asplit(data, 2), function(x) outer(x, x, `-`))

What is the difference between the operator acting elementwise vs on the matrix using Numpy?

Say we've got two matrices:

a = [ p q ]
    [ r s ]

b = [ w x ]
    [ y z ]

Element-wise product means:

a * b = [ p*w  q*x ]
        [ r*y  s*z ]

Matrix product means:

a @ b = [ (p*w)+(q*y)  (p*x)+(q*z) ]
        [ (r*w)+(s*y)  (r*x)+(s*z) ]

When literature in math, machine learning etc talks about "matrix multiplication", this matrix product is what is meant. Note that a @ b is not the same as b @ a.

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