Displaying 11 results from an estimated 11 matches for "escoufi".
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escoufier
2008 May 13
3
R benchmarking program
...ppe Grosjean
# eMail : phgrosjean at sciviews.org
# Web : http://www.sciviews.org
# License: GPL 2 or above at your convenience (see: http://www.gnu.org)
#
# Several tests are adapted from the Splus Benchmark Test V. 2
# by Stephan Steinhaus (stst at informatik.uni-frankfurt.de)
# Reference for Escoufier's equivalents vectors (test III.5):
# Escoufier Y., 1970. Echantillonnage dans une population de variables
# aleatoires r??les. Publ. Inst. Statis. Univ. Paris 19 Fasc 4, 1-47.
...
Error in getClass(Class, where = topenv(parent.frame())) :
"geMatrix" is not a defined class
Calls:...
2002 Oct 09
0
R 1.6.0 benchmark with and without optimized ATLAS
...225,000 Fibonacci numbers calculation (vector calc)_ (sec): 0.25
Creation of a 1500x1500 Hilbert matrix (matrix calc) (sec):
0.49333333333333
Grand common divisors of 35,000 pairs (recursion)___ (sec):
0.539999999999997
Creation of a 220x220 Toeplitz matrix (loops)_______ (sec):
0.896666666666666
Escoufier's method on a 22x22 matrix (mixed)________ (sec):
0.159999999999997
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated):
0.405344927915484
Total time for all 15 tests_________________________ (sec): 14.96
Overall mea...
2001 Feb 16
1
Sub_scribe and a question
...x, 10 = Inverse of a 500x500 random matrix
Serie II: programmation
11 = 225.000 Fibonacci numbers calculation (vector calc.), 12 = Creation of
a 1500x1500 Hilbert matrix (matrix calc.), 13 = Grand common divisors of
35,000 pairs (recursion), 14 = Creation of a 220x220 Toeplitz matrix
(loops), 15 = Escoufier's method on a 22x22 matrix (mixed)
Times are in sec.
Software A B C D E F(R) G H I J K
L
Serie I.
--------
1 1.03 1.35 0.95 1.23 2.19 2.79 4.24 2.91 1.00 1.92 2.95
2.67
2 2.60 2.48 0.99 2.91 0.76 2.88 2.76 1.41 2.61...
2006 Oct 17
0
[R] performance reflections
[This is a follow up on gcc3 vs. gcc4 discussion. Background: R
benchmark tests ( http://www.sciviews.org/benchmark/index.html ) show
a dramatic difference in "Escoufier's method on a 37x37 matrix
(mixed)" test when comparing binaries for PowerPC compiled with gcc3
vs gcc4.]
On Oct 16, 2006, at 11:29 AM, Ren? J.V. Bertin wrote:
> Anyway, it has nothing to do with the G4 optimisations, as the
> generic 2.4.0 on CRAN also shows the same perfo...
2007 Nov 03
1
mantel tests
This is a general statistics question so I'm sorry if its outside the field of r help.
Anyway, I have a suite of female and male traits and I have made a matrix of correlation coefficients using rcorr(). This results in a 6 by 6 matrix like this..
[1] 0.11287990 0.20441361 0.23837442 0.04713234 0.04331637 0.01461611
[7] 0.22627981 0.11720108 0.14252307 0.19531625 0.29989953 0.09989502
2015 Nov 23
3
MKL Acceleration encouraging; need adjust package builds?
...--
3,500,000 Fibonacci numbers calculation (vector calc)(sec): 1.25633333333335
Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec): 0.894999999999982
Grand common divisors of 400,000 pairs (recursion)__ (sec): 1.714
Creation of a 500x500 Toeplitz matrix (loops)_______ (sec): 1.4013333333333
Escoufier's method on a 45x45 matrix (mixed)________ (sec): 2.041
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 1.44505946077978
Total time for all 15 tests_________________________ (sec): 88.6306666666667
Overall mean (sum of I, II and III trimmed means/3)_...
2005 Oct 06
1
Compare two distance matrices
Hi all,
I am trying to compare two distance matrices with R. I would like to
create a XY plot of these matrices and do some linear regression on
it. But, I am a bit new to R, so i have a few questions (I searched in
the documentation with no success).
The first problem is loading a distance matrix into R. This matrix is
the output of a the Phylip program Protdist and lookes like this:
5
2015 Nov 23
0
MKL Acceleration encouraging; need adjust package builds?
...--
3,500,000 Fibonacci numbers calculation (vector calc)(sec): 1.25633333333335 Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec): 0.894999999999982 Grand common divisors of 400,000 pairs (recursion)__ (sec): 1.714 Creation of a 500x500 Toeplitz matrix (loops)_______ (sec): 1.4013333333333 Escoufier's method on a 45x45 matrix (mixed)________ (sec): 2.041
--------------------------------------------
Trimmed geom. mean (2 extremes eliminated): 1.44505946077978
Total time for all 15 tests_________________________ (sec): 88.6306666666667 Overall mean (sum of I, II and III trimmed means/3)_...
2008 Jun 24
5
Measuring Goodness of a Matrix
Hi all,
Suppose I have 2 matrices A and B.
And I want to measure how good each of this matrix is.
So I intend to compare A and B with another "gold standard"
matrix X. Meaning the more similar a matrix to X the better it is.
What is the common way in R to
measure matrix similarity (ie. A vs X, and B vs X) ?
- Gundala Viswanath
Jakarta - Indonesia
2001 Apr 27
2
Benchmarking R, why sort() is so slow?
...x, 10 = Inverse of a 500x500 random matrix
Serie II: programmation
11 = 225.000 Fibonacci numbers calculation (vector calc.), 12 = Creation of
a 1500x1500 Hilbert matrix (matrix calc.), 13 = Grand common divisors of
35,000 pairs (recursion), 14 = Creation of a 220x220 Toeplitz matrix
(loops), 15 = Escoufier's method on a 22x22 matrix (mixed)
Times are in sec.
Software A B C D E F(R) G H I J K
L
Serie I.
--------
1 1.03 1.35 0.95 1.23 2.19 2.79 4.24 2.91 1.00 1.92 2.95
2.67
2 2.60 2.48 0.99 2.91 0.76 2.88 2.76 1.41 2.61...
2001 Apr 27
2
Benchmarking R, why sort() is so slow?
...x, 10 = Inverse of a 500x500 random matrix
Serie II: programmation
11 = 225.000 Fibonacci numbers calculation (vector calc.), 12 = Creation of
a 1500x1500 Hilbert matrix (matrix calc.), 13 = Grand common divisors of
35,000 pairs (recursion), 14 = Creation of a 220x220 Toeplitz matrix
(loops), 15 = Escoufier's method on a 22x22 matrix (mixed)
Times are in sec.
Software A B C D E F(R) G H I J K
L
Serie I.
--------
1 1.03 1.35 0.95 1.23 2.19 2.79 4.24 2.91 1.00 1.92 2.95
2.67
2 2.60 2.48 0.99 2.91 0.76 2.88 2.76 1.41 2.61...