performance of apply

Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at> writes:

To demonstrate it, I did some benchmarking for different methods of
computing the row sums of an nx10 matrix with n =3D 2000, ..., 10000.

The first method (M1) I used is the normal apply command:
	y <- apply(x,1,sum)
The second method (M2) uses a for-loop for the computations, where the
memory for the resulting vector has been allocated before. That is, for
n=3D2000:
	z <- numeric(2000); for (i in 1:2000) z[i] <- sum(x[i,])
The third method (M3) also uses a for-loop, but the resulting vector
is built recursively, i.e.
	z1 <- NULL; for (i in 1:2000) z1 <- c(z1,sum(x[i,]))

All computations have been made on a Pentium II 233MHz, 256MB, R
started as R -v 50. The following table shows the minimum, mean, and
maximum CPU-time in seconds as measured by system.time over 10 runs
for every computation for different values of n.

n			M1			M2		M3
2000	 4.03  4.16   4.34	0.27 0.40 0.47	0.51 0.63 0.71
4000	12.65 13.40  14.68	0.73 0.81 0.94	1.78 1.86 1.98
6000	26.51 28.14  29.50	1.19 1.22 1.38	3.79 3.80 3.80
8000	52.06 63.43  67.61	1.46 1.63 1.69	6.38 6.41 6.58
10000	84.06 98.17 118.94	1.93 2.01 2.13  9.78 9.79 9.81

That is, the computation of the sums of the rows of a 10000x10 matrix
with apply takes about 100sec on average, where a simple for-loop does
the same job in about 2sec.