Re: [R] Best way to calculate averages of Blocks in an matrix?

2013-04-17 Thread arun 
do.call(rbind,lapply(split(sim_sub,((seq_len(nrow(sim_sub))-1)%/% 
5)+1),colMeans))
  #  V11   V12   V13   V14   V15  V16   V17   V18   V19   V20
#1 0.004 0.008 0.002 0.008 0.004 0.01 0.004 0.014 0.022 0.010
#2 0.002 0.000 0.002 0.006 0.006 0.01 0.018 0.006 0.010 0.014
#3 0.000 0.004 0.002 0.012 0.004 0.01 0.010 0.016 0.012 0.012
#4 0.000 0.008 0.002 0.014 0.012 0.01 0.014 0.020 0.010 0.018
A.K.



- Original Message -
From: Keith S Weintraub kw1...@gmail.com
To: r-help@r-project.org r-help@r-project.org
Cc: 
Sent: Wednesday, April 17, 2013 12:54 PM
Subject: [R] Best way to calculate averages of Blocks in an matrix?

Folks,
  I recently was given a simulated data set like the following subset:

sim_sub-structure(list(V11 = c(0.01, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V12 = c(0, 0, 0, 0.01, 0.03, 0, 
0, 0, 0, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0.04), V13 = c(0, 
0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 
0.01), V14 = c(0, 0.01, 0.01, 0.01, 0.01, 0, 0, 0, 0, 0.03, 0, 
0, 0.01, 0.01, 0.04, 0.01, 0.02, 0, 0.01, 0.03), V15 = c(0, 0.01, 
0, 0, 0.01, 0, 0, 0, 0.01, 0.02, 0.01, 0, 0, 0.01, 0, 0, 0, 0.01, 
0.01, 0.04), V16 = c(0, 0, 0, 0.03, 0.02, 0.01, 0, 0, 0.02, 0.02, 
0, 0.02, 0.02, 0, 0.01, 0.01, 0, 0, 0.03, 0.01), V17 = c(0, 0.01, 
0, 0.01, 0, 0, 0, 0.01, 0.05, 0.03, 0, 0.01, 0, 0.02, 0.02, 0, 
0, 0.01, 0.02, 0.04), V18 = c(0, 0.01, 0, 0.03, 0.03, 0, 0, 0, 
0.02, 0.01, 0, 0.02, 0.01, 0.02, 0.03, 0.02, 0, 0, 0.04, 0.04
), V19 = c(0, 0.01, 0.01, 0.02, 0.07, 0, 0, 0, 0.04, 0.01, 0.02, 
0, 0, 0, 0.04, 0, 0, 0, 0, 0.05), V20 = c(0, 0, 0, 0.01, 0.04, 
0.01, 0, 0, 0.02, 0.04, 0.01, 0, 0.02, 0, 0.03, 0, 0.02, 0.01, 
0.03, 0.03)), .Names = c(V11, V12, V13, V14, V15, V16, 
V17, V18, V19, V20), row.names = c(NA, 20L), class = data.frame)

 sim_sub
    V11  V12  V13  V14  V15  V16  V17  V18  V19  V20
1  0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2  0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.00
3  0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00
4  0.01 0.01 0.01 0.01 0.00 0.03 0.01 0.03 0.02 0.01
5  0.00 0.03 0.00 0.01 0.01 0.02 0.00 0.03 0.07 0.04
6  0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01
7  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
8  0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00
9  0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.02 0.04 0.02
10 0.00 0.00 0.01 0.03 0.02 0.02 0.03 0.01 0.01 0.04
11 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.01
12 0.00 0.01 0.00 0.00 0.00 0.02 0.01 0.02 0.00 0.00
13 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.01 0.00 0.02
14 0.00 0.01 0.00 0.01 0.01 0.00 0.02 0.02 0.00 0.00
15 0.00 0.00 0.01 0.04 0.00 0.01 0.02 0.03 0.04 0.03
16 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.02 0.00 0.00
17 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02
18 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.01
19 0.00 0.00 0.00 0.01 0.01 0.03 0.02 0.04 0.00 0.03
20 0.00 0.04 0.01 0.03 0.04 0.01 0.04 0.04 0.05 0.03

Every 5 rows represents one block of simulated data.

What would be the best way to average the blocks?

My way was to reshape sim_sub, average over the columns and then reshape back 
like so:

 matrix(colSums(matrix(t(sim_sub), byrow = TRUE, ncol = 50)), byrow = TRUE, 
 ncol = 10)/4
       [,1]   [,2]   [,3]   [,4]   [,5]  [,6]   [,7]   [,8]   [,9]  [,10]
[1,] 0.0050 0. 0. 0.0025 0.0025 0.005 0. 0.0050 0.0050 0.0050
[2,] 0. 0.0025 0. 0.0075 0.0025 0.005 0.0050 0.0075 0.0025 0.0050
[3,] 0. 0. 0. 0.0050 0.0025 0.005 0.0050 0.0025 0.0025 0.0075
[4,] 0.0025 0.0050 0.0025 0.0075 0.0075 0.020 0.0250 0.0275 0.0150 0.0150
[5,] 0. 0.0175 0.0075 0.0275 0.0175 0.015 0.0225 0.0275 0.0425 0.0350


How bad is t(sim_sub) in the above?

Thanks for your time,
KW

--

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.


__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Re: [R] Best way to calculate averages of Blocks in an matrix?

2013-04-17 Thread arun 
Also,
do.call(rbind,lapply(split(sim_sub,rep(1:(1+nrow(sim_sub)/5),each=5)[seq_len(nrow(sim_sub))]),colMeans))
#    V11   V12   V13   V14   V15  V16   V17   V18   V19   V20
#1 0.004 0.008 0.002 0.008 0.004 0.01 0.004 0.014 0.022 0.010
#2 0.002 0.000 0.002 0.006 0.006 0.01 0.018 0.006 0.010 0.014
#3 0.000 0.004 0.002 0.012 0.004 0.01 0.010 0.016 0.012 0.012
#4 0.000 0.008 0.002 0.014 0.012 0.01 0.014 0.020 0.010 0.018
A.K.




- Original Message -
From: arun smartpink...@yahoo.com
To: Keith S Weintraub kw1...@gmail.com
Cc: R help r-help@r-project.org
Sent: Wednesday, April 17, 2013 1:04 PM
Subject: Re: [R] Best way to calculate averages of Blocks in an matrix?

do.call(rbind,lapply(split(sim_sub,((seq_len(nrow(sim_sub))-1)%/% 
5)+1),colMeans))
  #  V11   V12   V13   V14   V15  V16   V17   V18   V19   V20
#1 0.004 0.008 0.002 0.008 0.004 0.01 0.004 0.014 0.022 0.010
#2 0.002 0.000 0.002 0.006 0.006 0.01 0.018 0.006 0.010 0.014
#3 0.000 0.004 0.002 0.012 0.004 0.01 0.010 0.016 0.012 0.012
#4 0.000 0.008 0.002 0.014 0.012 0.01 0.014 0.020 0.010 0.018
A.K.



- Original Message -
From: Keith S Weintraub kw1...@gmail.com
To: r-help@r-project.org r-help@r-project.org
Cc: 
Sent: Wednesday, April 17, 2013 12:54 PM
Subject: [R] Best way to calculate averages of Blocks in an matrix?

Folks,
  I recently was given a simulated data set like the following subset:

sim_sub-structure(list(V11 = c(0.01, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V12 = c(0, 0, 0, 0.01, 0.03, 0, 
0, 0, 0, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0.04), V13 = c(0, 
0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 
0.01), V14 = c(0, 0.01, 0.01, 0.01, 0.01, 0, 0, 0, 0, 0.03, 0, 
0, 0.01, 0.01, 0.04, 0.01, 0.02, 0, 0.01, 0.03), V15 = c(0, 0.01, 
0, 0, 0.01, 0, 0, 0, 0.01, 0.02, 0.01, 0, 0, 0.01, 0, 0, 0, 0.01, 
0.01, 0.04), V16 = c(0, 0, 0, 0.03, 0.02, 0.01, 0, 0, 0.02, 0.02, 
0, 0.02, 0.02, 0, 0.01, 0.01, 0, 0, 0.03, 0.01), V17 = c(0, 0.01, 
0, 0.01, 0, 0, 0, 0.01, 0.05, 0.03, 0, 0.01, 0, 0.02, 0.02, 0, 
0, 0.01, 0.02, 0.04), V18 = c(0, 0.01, 0, 0.03, 0.03, 0, 0, 0, 
0.02, 0.01, 0, 0.02, 0.01, 0.02, 0.03, 0.02, 0, 0, 0.04, 0.04
), V19 = c(0, 0.01, 0.01, 0.02, 0.07, 0, 0, 0, 0.04, 0.01, 0.02, 
0, 0, 0, 0.04, 0, 0, 0, 0, 0.05), V20 = c(0, 0, 0, 0.01, 0.04, 
0.01, 0, 0, 0.02, 0.04, 0.01, 0, 0.02, 0, 0.03, 0, 0.02, 0.01, 
0.03, 0.03)), .Names = c(V11, V12, V13, V14, V15, V16, 
V17, V18, V19, V20), row.names = c(NA, 20L), class = data.frame)

 sim_sub
    V11  V12  V13  V14  V15  V16  V17  V18  V19  V20
1  0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2  0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.00
3  0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00
4  0.01 0.01 0.01 0.01 0.00 0.03 0.01 0.03 0.02 0.01
5  0.00 0.03 0.00 0.01 0.01 0.02 0.00 0.03 0.07 0.04
6  0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01
7  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
8  0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00
9  0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.02 0.04 0.02
10 0.00 0.00 0.01 0.03 0.02 0.02 0.03 0.01 0.01 0.04
11 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.01
12 0.00 0.01 0.00 0.00 0.00 0.02 0.01 0.02 0.00 0.00
13 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.01 0.00 0.02
14 0.00 0.01 0.00 0.01 0.01 0.00 0.02 0.02 0.00 0.00
15 0.00 0.00 0.01 0.04 0.00 0.01 0.02 0.03 0.04 0.03
16 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.02 0.00 0.00
17 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02
18 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.01
19 0.00 0.00 0.00 0.01 0.01 0.03 0.02 0.04 0.00 0.03
20 0.00 0.04 0.01 0.03 0.04 0.01 0.04 0.04 0.05 0.03

Every 5 rows represents one block of simulated data.

What would be the best way to average the blocks?

My way was to reshape sim_sub, average over the columns and then reshape back 
like so:

 matrix(colSums(matrix(t(sim_sub), byrow = TRUE, ncol = 50)), byrow = TRUE, 
 ncol = 10)/4
       [,1]   [,2]   [,3]   [,4]   [,5]  [,6]   [,7]   [,8]   [,9]  [,10]
[1,] 0.0050 0. 0. 0.0025 0.0025 0.005 0. 0.0050 0.0050 0.0050
[2,] 0. 0.0025 0. 0.0075 0.0025 0.005 0.0050 0.0075 0.0025 0.0050
[3,] 0. 0. 0. 0.0050 0.0025 0.005 0.0050 0.0025 0.0025 0.0075
[4,] 0.0025 0.0050 0.0025 0.0075 0.0075 0.020 0.0250 0.0275 0.0150 0.0150
[5,] 0. 0.0175 0.0075 0.0275 0.0175 0.015 0.0225 0.0275 0.0425 0.0350


How bad is t(sim_sub) in the above?

Thanks for your time,
KW

--

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Re: [R] Best way to calculate averages of Blocks in an matrix?

2013-04-17 Thread Rui Barradas 

Hello,

Try the following.


blocks - rep(1:(1 + nrow(sim_sub) %/% 5), each = 5)[seq_len(nrow(sim_sub))]
aggregate(sim_sub, list(blocks), FUN = mean)


Hope this helps,

Rui Barradas

Em 17-04-2013 18:04, arun escreveu:

do.call(rbind,lapply(split(sim_sub,((seq_len(nrow(sim_sub))-1)%/% 
5)+1),colMeans))
   #  V11   V12   V13   V14   V15  V16   V17   V18   V19   V20
#1 0.004 0.008 0.002 0.008 0.004 0.01 0.004 0.014 0.022 0.010
#2 0.002 0.000 0.002 0.006 0.006 0.01 0.018 0.006 0.010 0.014
#3 0.000 0.004 0.002 0.012 0.004 0.01 0.010 0.016 0.012 0.012
#4 0.000 0.008 0.002 0.014 0.012 0.01 0.014 0.020 0.010 0.018
A.K.



- Original Message -
From: Keith S Weintraub kw1...@gmail.com
To: r-help@r-project.org r-help@r-project.org
Cc:
Sent: Wednesday, April 17, 2013 12:54 PM
Subject: [R] Best way to calculate averages of Blocks in an matrix?

Folks,
   I recently was given a simulated data set like the following subset:

sim_sub-structure(list(V11 = c(0.01, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V12 = c(0, 0, 0, 0.01, 0.03, 0,
0, 0, 0, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0.04), V13 = c(0,
0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0.01), V14 = c(0, 0.01, 0.01, 0.01, 0.01, 0, 0, 0, 0, 0.03, 0,
0, 0.01, 0.01, 0.04, 0.01, 0.02, 0, 0.01, 0.03), V15 = c(0, 0.01,
0, 0, 0.01, 0, 0, 0, 0.01, 0.02, 0.01, 0, 0, 0.01, 0, 0, 0, 0.01,
0.01, 0.04), V16 = c(0, 0, 0, 0.03, 0.02, 0.01, 0, 0, 0.02, 0.02,
0, 0.02, 0.02, 0, 0.01, 0.01, 0, 0, 0.03, 0.01), V17 = c(0, 0.01,
0, 0.01, 0, 0, 0, 0.01, 0.05, 0.03, 0, 0.01, 0, 0.02, 0.02, 0,
0, 0.01, 0.02, 0.04), V18 = c(0, 0.01, 0, 0.03, 0.03, 0, 0, 0,
0.02, 0.01, 0, 0.02, 0.01, 0.02, 0.03, 0.02, 0, 0, 0.04, 0.04
), V19 = c(0, 0.01, 0.01, 0.02, 0.07, 0, 0, 0, 0.04, 0.01, 0.02,
0, 0, 0, 0.04, 0, 0, 0, 0, 0.05), V20 = c(0, 0, 0, 0.01, 0.04,
0.01, 0, 0, 0.02, 0.04, 0.01, 0, 0.02, 0, 0.03, 0, 0.02, 0.01,
0.03, 0.03)), .Names = c(V11, V12, V13, V14, V15, V16,
V17, V18, V19, V20), row.names = c(NA, 20L), class = data.frame)


sim_sub

 V11  V12  V13  V14  V15  V16  V17  V18  V19  V20
1  0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
2  0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.00
3  0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00
4  0.01 0.01 0.01 0.01 0.00 0.03 0.01 0.03 0.02 0.01
5  0.00 0.03 0.00 0.01 0.01 0.02 0.00 0.03 0.07 0.04
6  0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01
7  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
8  0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00
9  0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.02 0.04 0.02
10 0.00 0.00 0.01 0.03 0.02 0.02 0.03 0.01 0.01 0.04
11 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.01
12 0.00 0.01 0.00 0.00 0.00 0.02 0.01 0.02 0.00 0.00
13 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.01 0.00 0.02
14 0.00 0.01 0.00 0.01 0.01 0.00 0.02 0.02 0.00 0.00
15 0.00 0.00 0.01 0.04 0.00 0.01 0.02 0.03 0.04 0.03
16 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.02 0.00 0.00
17 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02
18 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.01
19 0.00 0.00 0.00 0.01 0.01 0.03 0.02 0.04 0.00 0.03
20 0.00 0.04 0.01 0.03 0.04 0.01 0.04 0.04 0.05 0.03

Every 5 rows represents one block of simulated data.

What would be the best way to average the blocks?

My way was to reshape sim_sub, average over the columns and then reshape back 
like so:


matrix(colSums(matrix(t(sim_sub), byrow = TRUE, ncol = 50)), byrow = TRUE, ncol 
= 10)/4

[,1]   [,2]   [,3]   [,4]   [,5]  [,6]   [,7]   [,8]   [,9]  [,10]
[1,] 0.0050 0. 0. 0.0025 0.0025 0.005 0. 0.0050 0.0050 0.0050
[2,] 0. 0.0025 0. 0.0075 0.0025 0.005 0.0050 0.0075 0.0025 0.0050
[3,] 0. 0. 0. 0.0050 0.0025 0.005 0.0050 0.0025 0.0025 0.0075
[4,] 0.0025 0.0050 0.0025 0.0075 0.0075 0.020 0.0250 0.0275 0.0150 0.0150
[5,] 0. 0.0175 0.0075 0.0275 0.0175 0.015 0.0225 0.0275 0.0425 0.0350


How bad is t(sim_sub) in the above?

Thanks for your time,
KW

--

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.


__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.



__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Re: [R] Best way to calculate averages of Blocks in an matrix?

2013-04-17 Thread David Winsemius 

On Apr 17, 2013, at 9:54 AM, Keith S Weintraub wrote:

 Folks,
  I recently was given a simulated data set like the following subset:
 
 sim_sub-structure(list(V11 = c(0.01, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V12 = c(0, 0, 0, 0.01, 0.03, 0, 
 0, 0, 0, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0.04), V13 = c(0, 
 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 
 0.01), V14 = c(0, 0.01, 0.01, 0.01, 0.01, 0, 0, 0, 0, 0.03, 0, 
 0, 0.01, 0.01, 0.04, 0.01, 0.02, 0, 0.01, 0.03), V15 = c(0, 0.01, 
 0, 0, 0.01, 0, 0, 0, 0.01, 0.02, 0.01, 0, 0, 0.01, 0, 0, 0, 0.01, 
 0.01, 0.04), V16 = c(0, 0, 0, 0.03, 0.02, 0.01, 0, 0, 0.02, 0.02, 
 0, 0.02, 0.02, 0, 0.01, 0.01, 0, 0, 0.03, 0.01), V17 = c(0, 0.01, 
 0, 0.01, 0, 0, 0, 0.01, 0.05, 0.03, 0, 0.01, 0, 0.02, 0.02, 0, 
 0, 0.01, 0.02, 0.04), V18 = c(0, 0.01, 0, 0.03, 0.03, 0, 0, 0, 
 0.02, 0.01, 0, 0.02, 0.01, 0.02, 0.03, 0.02, 0, 0, 0.04, 0.04
 ), V19 = c(0, 0.01, 0.01, 0.02, 0.07, 0, 0, 0, 0.04, 0.01, 0.02, 
 0, 0, 0, 0.04, 0, 0, 0, 0, 0.05), V20 = c(0, 0, 0, 0.01, 0.04, 
 0.01, 0, 0, 0.02, 0.04, 0.01, 0, 0.02, 0, 0.03, 0, 0.02, 0.01, 
 0.03, 0.03)), .Names = c(V11, V12, V13, V14, V15, V16, 
 V17, V18, V19, V20), row.names = c(NA, 20L), class = data.frame)
 
 sim_sub
V11  V12  V13  V14  V15  V16  V17  V18  V19  V20
 1  0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
 2  0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.00
 3  0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00
 4  0.01 0.01 0.01 0.01 0.00 0.03 0.01 0.03 0.02 0.01
 5  0.00 0.03 0.00 0.01 0.01 0.02 0.00 0.03 0.07 0.04
 6  0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01
 7  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
 8  0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00
 9  0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.02 0.04 0.02
 10 0.00 0.00 0.01 0.03 0.02 0.02 0.03 0.01 0.01 0.04
 11 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.01
 12 0.00 0.01 0.00 0.00 0.00 0.02 0.01 0.02 0.00 0.00
 13 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.01 0.00 0.02
 14 0.00 0.01 0.00 0.01 0.01 0.00 0.02 0.02 0.00 0.00
 15 0.00 0.00 0.01 0.04 0.00 0.01 0.02 0.03 0.04 0.03
 16 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.02 0.00 0.00
 17 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02
 18 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.01
 19 0.00 0.00 0.00 0.01 0.01 0.03 0.02 0.04 0.00 0.03
 20 0.00 0.04 0.01 0.03 0.04 0.01 0.04 0.04 0.05 0.03
 
 Every 5 rows represents one block of simulated data.
 
 What would be the best way to average the blocks?

This answers the posed question:

   tapply( data.matrix(sim_sub),  rep( rep(1:4, each=5), each=10) ,mean)
 1  2  3  4 
0.0030 0.0070 0.0106 0.0144 


Your code following suggests that you do not want the average values within 
blocks but within blocks AND ALSO within columns (although how you get 5 rows 
of 5 blocks from a 20 row input object is unclear to me)

 data.frame( lapply(sim_sub, function(col) tapply(col, rep(1:4, each=5), mean) 
  ) )
V11   V12   V13   V14   V15  V16   V17   V18   V19   V20
1 0.004 0.008 0.002 0.008 0.004 0.01 0.004 0.014 0.022 0.010
2 0.002 0.000 0.002 0.006 0.006 0.01 0.018 0.006 0.010 0.014
3 0.000 0.004 0.002 0.012 0.004 0.01 0.010 0.016 0.012 0.012
4 0.000 0.008 0.002 0.014 0.012 0.01 0.014 0.020 0.010 0.018

From your code I am guessing a typo of 5 for 4?

 
 My way was to reshape sim_sub, average over the columns and then reshape back 
 like so:
 
 matrix(colSums(matrix(t(sim_sub), byrow = TRUE, ncol = 50)), byrow = TRUE, 
 ncol = 10)/4
   [,1]   [,2]   [,3]   [,4]   [,5]  [,6]   [,7]   [,8]   [,9]  [,10]
 [1,] 0.0050 0. 0. 0.0025 0.0025 0.005 0. 0.0050 0.0050 0.0050
 [2,] 0. 0.0025 0. 0.0075 0.0025 0.005 0.0050 0.0075 0.0025 0.0050
 [3,] 0. 0. 0. 0.0050 0.0025 0.005 0.0050 0.0025 0.0025 0.0075
 [4,] 0.0025 0.0050 0.0025 0.0075 0.0075 0.020 0.0250 0.0275 0.0150 0.0150
 [5,] 0. 0.0175 0.0075 0.0275 0.0175 0.015 0.0225 0.0275 0.0425 0.0350
 
 
 How bad is t(sim_sub) in the above?

The whole matrix( matrix( t(.), ... )) approach seems kind of tortured, but to 
your question, t() is a fairly efficient function.

-- 

David Winsemius
Alameda, CA, USA

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Re: [R] Best way to calculate averages of Blocks in an matrix?

2013-04-17 Thread arun 



 tapply(t(data.matrix(sim_sub)),rep( rep(1:4, each=5), each=10),mean) 
   #  1  2  3  4 
#0.0086 0.0074 0.0082 0.0108 

unlist(lapply(split(sim_sub,((seq_len(nrow(sim_sub))-1)%/%5)+1),function(x) 
mean(unlist(x
#    1  2  3  4 
#0.0086 0.0074 0.0082 0.0108 
A.K.

- Original Message -
From: David Winsemius dwinsem...@comcast.net
To: Keith S Weintraub kw1...@gmail.com
Cc: r-help@r-project.org r-help@r-project.org
Sent: Wednesday, April 17, 2013 4:05 PM
Subject: Re: [R] Best way to calculate averages of Blocks in an matrix?


On Apr 17, 2013, at 9:54 AM, Keith S Weintraub wrote:

 Folks,
  I recently was given a simulated data set like the following subset:
 
 sim_sub-structure(list(V11 = c(0.01, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V12 = c(0, 0, 0, 0.01, 0.03, 0, 
 0, 0, 0, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0.04), V13 = c(0, 
 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 
 0.01), V14 = c(0, 0.01, 0.01, 0.01, 0.01, 0, 0, 0, 0, 0.03, 0, 
 0, 0.01, 0.01, 0.04, 0.01, 0.02, 0, 0.01, 0.03), V15 = c(0, 0.01, 
 0, 0, 0.01, 0, 0, 0, 0.01, 0.02, 0.01, 0, 0, 0.01, 0, 0, 0, 0.01, 
 0.01, 0.04), V16 = c(0, 0, 0, 0.03, 0.02, 0.01, 0, 0, 0.02, 0.02, 
 0, 0.02, 0.02, 0, 0.01, 0.01, 0, 0, 0.03, 0.01), V17 = c(0, 0.01, 
 0, 0.01, 0, 0, 0, 0.01, 0.05, 0.03, 0, 0.01, 0, 0.02, 0.02, 0, 
 0, 0.01, 0.02, 0.04), V18 = c(0, 0.01, 0, 0.03, 0.03, 0, 0, 0, 
 0.02, 0.01, 0, 0.02, 0.01, 0.02, 0.03, 0.02, 0, 0, 0.04, 0.04
 ), V19 = c(0, 0.01, 0.01, 0.02, 0.07, 0, 0, 0, 0.04, 0.01, 0.02, 
 0, 0, 0, 0.04, 0, 0, 0, 0, 0.05), V20 = c(0, 0, 0, 0.01, 0.04, 
 0.01, 0, 0, 0.02, 0.04, 0.01, 0, 0.02, 0, 0.03, 0, 0.02, 0.01, 
 0.03, 0.03)), .Names = c(V11, V12, V13, V14, V15, V16, 
 V17, V18, V19, V20), row.names = c(NA, 20L), class = data.frame)
 
 sim_sub
    V11  V12  V13  V14  V15  V16  V17  V18  V19  V20
 1  0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
 2  0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.00
 3  0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00
 4  0.01 0.01 0.01 0.01 0.00 0.03 0.01 0.03 0.02 0.01
 5  0.00 0.03 0.00 0.01 0.01 0.02 0.00 0.03 0.07 0.04
 6  0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01
 7  0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
 8  0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00
 9  0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.02 0.04 0.02
 10 0.00 0.00 0.01 0.03 0.02 0.02 0.03 0.01 0.01 0.04
 11 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.01
 12 0.00 0.01 0.00 0.00 0.00 0.02 0.01 0.02 0.00 0.00
 13 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.01 0.00 0.02
 14 0.00 0.01 0.00 0.01 0.01 0.00 0.02 0.02 0.00 0.00
 15 0.00 0.00 0.01 0.04 0.00 0.01 0.02 0.03 0.04 0.03
 16 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.02 0.00 0.00
 17 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02
 18 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.00 0.01
 19 0.00 0.00 0.00 0.01 0.01 0.03 0.02 0.04 0.00 0.03
 20 0.00 0.04 0.01 0.03 0.04 0.01 0.04 0.04 0.05 0.03
 
 Every 5 rows represents one block of simulated data.
 
 What would be the best way to average the blocks?

This answers the posed question:

   tapply( data.matrix(sim_sub),  rep( rep(1:4, each=5), each=10) ,mean)
     1      2      3      4 
0.0030 0.0070 0.0106 0.0144 


Your code following suggests that you do not want the average values within 
blocks but within blocks AND ALSO within columns (although how you get 5 rows 
of 5 blocks from a 20 row input object is unclear to me)

 data.frame( lapply(sim_sub, function(col) tapply(col, rep(1:4, each=5), mean) 
  ) )
    V11   V12   V13   V14   V15  V16   V17   V18   V19   V20
1 0.004 0.008 0.002 0.008 0.004 0.01 0.004 0.014 0.022 0.010
2 0.002 0.000 0.002 0.006 0.006 0.01 0.018 0.006 0.010 0.014
3 0.000 0.004 0.002 0.012 0.004 0.01 0.010 0.016 0.012 0.012
4 0.000 0.008 0.002 0.014 0.012 0.01 0.014 0.020 0.010 0.018

From your code I am guessing a typo of 5 for 4?

 
 My way was to reshape sim_sub, average over the columns and then reshape back 
 like so:
 
 matrix(colSums(matrix(t(sim_sub), byrow = TRUE, ncol = 50)), byrow = TRUE, 
 ncol = 10)/4
       [,1]   [,2]   [,3]   [,4]   [,5]  [,6]   [,7]   [,8]   [,9]  [,10]
 [1,] 0.0050 0. 0. 0.0025 0.0025 0.005 0. 0.0050 0.0050 0.0050
 [2,] 0. 0.0025 0. 0.0075 0.0025 0.005 0.0050 0.0075 0.0025 0.0050
 [3,] 0. 0. 0. 0.0050 0.0025 0.005 0.0050 0.0025 0.0025 0.0075
 [4,] 0.0025 0.0050 0.0025 0.0075 0.0075 0.020 0.0250 0.0275 0.0150 0.0150
 [5,] 0. 0.0175 0.0075 0.0275 0.0175 0.015 0.0225 0.0275 0.0425 0.0350
 
 
 How bad is t(sim_sub) in the above?

The whole matrix( matrix( t(.), ... )) approach seems kind of tortured, but to 
your question, t() is a fairly efficient function.

-- 

David Winsemius
Alameda, CA, USA

__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained,