Collapse array

## Usage

collapse(x, keep, ...)

# S3 method for array
collapse(
x,
keep,
average = TRUE,
transform = c("asis", "10log10", "square", "sqrt"),
...
)

## Arguments

x

A numeric multi-mode tensor (array), without NA

keep

Which dimension to keep

...

passed to other methods

average

collapse to sum or mean

transform

transform on the data before applying collapsing; choices are 'asis' (no change), '10log10' (used to calculate decibel), 'square' (sum-squared), 'sqrt' (square-root and collapse)

## Value

a collapsed array with values to be mean or summation along collapsing dimensions

## Examples


# Set ncores = 2 to comply to CRAN policy. Please don't run this line

# Example 1
x = matrix(1:16, 4)

# Keep the first dimension and calculate sums along the rest
collapse(x, keep = 1)
#> [1]  7  8  9 10
rowMeans(x)  # Should yield the same result
#> [1]  7  8  9 10

# Example 2
x = array(1:120, dim = c(2,3,4,5))
result = collapse(x, keep = c(3,2))
compare = apply(x, c(3,2), mean)
sum(abs(result - compare)) # The same, yield 0 or very small number (1e-10)
#> [1] 5.684342e-14

if(interactive()){

# Example 3 (performance)

# Small data, no big difference
x = array(rnorm(240), dim = c(4,5,6,2))
microbenchmark::microbenchmark(
result = collapse(x, keep = c(3,2)),
compare = apply(x, c(3,2), mean),
times = 1L, check = function(v){
max(abs(range(do.call('-', v)))) < 1e-10
}
)

# large data big difference
x = array(rnorm(prod(300,200,105)), c(300,200,105,1))
microbenchmark::microbenchmark(
result = collapse(x, keep = c(3,2)),
compare = apply(x, c(3,2), mean),
times = 1L , check = function(v){
max(abs(range(do.call('-', v)))) < 1e-10
})

}