collapse | R Documentation |

Collapse array

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

`x` |
A numeric multi-mode tensor (array), without |

`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 |

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

```
# Set ncores = 2 to comply to CRAN policy. Please don't run this line
ravetools_threads(n_threads = 2L)
# Example 1
x = matrix(1:16, 4)
# Keep the first dimension and calculate sums along the rest
collapse(x, keep = 1)
rowMeans(x) # Should yield the same result
# 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)
ravetools_threads(n_threads = -1)
# 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
})
```

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