If you are satisfied with the structure of cusumA and just want 999
more randome realizations of the same, then try creating an empty list
to hold the 1000 dataframes you are creating and then accumulate
sequentially to the list.
> mat <- matrix(data=rep(c(1,2,3,4,5), 16), nrow=16, ncol=5)
> # The matrix that will be sampled
> A <- matrix(data=0, nrow=16, ncol=5); cusumA <- list()
## ^new empty list^
> # The variable that will store the sampled matrix
> for(i in 1:1000) {
+ # I want to do it 1000 times
+ for(j in 1:nrow(mat)) {
+ # The number of rows to be sampled)
+ A[j,] <- sample(mat[j,])
+ # The sample itself - I want to do it 1000 times and then...
+ cusumA[[i]] <- cumsum(data.frame(A))
# index ^^^^
+ # cumulative sum it, and store the 1000 randomized matrices
+ }}
>
> str(cusumA)
List of 1000
$ :'data.frame': 16 obs. of 5 variables:
..$ X1: num [1:16] 5 9 10 14 16 21 22 25 26 30 ...
..$ X2: num [1:16] 3 4 6 7 10 11 15 17 20 22 ...
..$ X3: num [1:16] 2 4 7 12 17 20 25 30 35 40 ...
..$ X4: num [1:16] 4 7 12 15 16 20 23 24 28 31 ...
..$ X5: num [1:16] 1 6 10 12 16 18 20 24 26 27 ...
$ :'data.frame': 16 obs. of 5 variables:
..$ X1: num [1:16] 4 5 7 9 14 16 17 22 27 28 ...
..$ X2: num [1:16] 3 6 10 11 13 17 20 21 23 28 ...
..$ X3: num [1:16] 5 7 10 13 14 19 23 26 27 29 ...
..$ X4: num [1:16] 2 7 8 12 15 18 20 24 28 31 ...
..$ X5: num [1:16] 1 5 10 15 19 20 25 27 30 34 ...
snip long output (no warnings)
--
David Winsemius
On Jan 12, 2009, at 12:07 PM, rafamoral wrote:
I need to store each matrix generated in a loop.
I've been working with the CUSUM algorithm and I've been trying to
implement
it in R.
What I need to do with my dataset is to create 1000 randomized
datasets and
cumulative sum them all and store all of those randomized CUSUMed
datasets
for further analysis and creation of the simulation envelope in the
CUSUM
chart. But I can't manage to store all of them, the script I've
written only
stores the last randomized matrix in the variable I called "cusumA".
Here's the script I've written:
mat <- matrix(data=rep(c(1,2,3,4,5), 16), nrow=16, ncol=5)
# The matrix that will be sampled
A <- matrix(data=0, nrow=16, ncol=5)
# The variable that will store the sampled matrix
for(i in 1:1000) {
# I want to do it 1000 times
for(j in 1:nrow(mat)) {
# The number of rows to be sampled)
A[j,] <- sample(mat[j,])
# The sample itself - I want to do it 1000 times and then...
cusumA <- cumsum(data.frame(A))
# cumulative sum it, and store the 1000 randomized matrices
}}
I've already tried to store it in a null matrix but it will only
store the
first column of each matrix, so all I got was 1000 first columns.
The code I
used to do it was
mat <- matrix(data=rep(c(1,2,3,4,5), 16), nrow=16, ncol=5)
A <- matrix(data=0, nrow=16, ncol=5)
cusumA <- matrix()
for(i in 1:1000) {
for(j in 1:nrow(mat)) {
A[j,] <- sample(mat[j,])
cusumA[i] <- cumsum(data.frame(A))
}}
and I even got 50+ warnings...
Any help'd be really appreciated!
--
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and provide commented, minimal, self-contained, reproducible code.
