I was curious to compare all the methods, so ran a quick microbenchmark
. Clearly, the simplest 0.5 * (S + t(S))
is the fastest.
The specific function Matrix::forceSymmetric()
is sometimes slightly faster, but it returns an object of a different class (dsyMatrix
instead of matrix
), and converting back to matrix
takes a lot of time (although one might argue that it is a good idea to keep the output as dsyMatrix
for further gains in computation).
S <-matrix(1:50^2,50)
pick_lower <- function(M) M[lower.tri(M)] = t(M)[lower.tri(M)]
microbenchmark::microbenchmark(micEcon=miscTools::symMatrix(S[upper.tri(S, TRUE)], nrow=nrow(S), byrow=TRUE),
Matri_raw =Matrix::forceSymmetric(S),
Matri_conv =as.matrix(Matrix::forceSymmetric(S)),
pick_lower = pick_lower(S),
base =0.5 * (S + t(S)),
times=100)
#> Unit: microseconds
#> expr min lq mean median uq max neval cld
#> micEcon 62.133 74.7515 136.49538 104.2430 115.6950 3581.001 100 a
#> Matri_raw 14.766 17.9130 24.15157 24.5060 26.6050 63.939 100 a
#> Matri_conv 46.767 59.8165 5621.96140 66.3785 73.5380 555393.346 100 a
#> pick_lower 27.907 30.7930 235.65058 48.9760 53.0425 12484.779 100 a
#> base 10.771 12.4535 16.97627 17.1190 18.3175 47.623 100 a
Created on 2021-02-08 by the reprex package (v1.0.0)