??
Petr's solution is shorter and for large problems considerably faster...
-- Bert
On Wed, Mar 14, 2012 at 2:08 AM, Tsjerk Wassenaar wrote:
> Hi Lazarus,
>
> Sorry for my brevity before. I sent it from a phone.
>
> # Generate dummy data matrix
> x <- matrix(1:100,nrow=5)
>
> # Rearrange into ar
Hi Lazarus,
You should use apply, not sapply.
> a =matrix(1:12, nrow=2,ncol=6,byrow=TRUE)
> b = array(a,c(2,2,3));b
> colMeans(b)
>
> f1=function(x) sum(x)/length(row(a.df[,1:2]))
> y3 <- apply(b, 1, f1)
>
> It only gives one matrix with only two values, I expected 3 values.
To apply the functio
Hi Lazarus,
Sorry for my brevity before. I sent it from a phone.
# Generate dummy data matrix
x <- matrix(1:100,nrow=5)
# Rearrange into array; ten groups of two columns
y <- array(x,c(5,2,10))
# Average columns for each submatrix
apply(y,c(1,3),mean)
# Average per submatrix
apply(y,3,mean)
H
On Wed, Mar 14, 2012 at 03:22:39AM -0400, Lazarus Mramba wrote:
> Dear all,
>
> I have a large matrix with about 2500 variables, and 100 rows.
>
> I would like to calculate the means of the every 10 variables starting from
> 1:2500 and saving the results as a vector or matrix.
> How can I do tha
Hi Lazarus,
Checkout arrays (?array). You can cast your matrix to an array of
submatrices, and calculate the means per block using apply.
Cheers,
Tsjerk
On Mar 14, 2012 9:25 AM, "Lazarus Mramba" wrote:
Dear all,
I have a large matrix with about 2500 variables, and 100 rows.
I would like to
Dear all,
I have a large matrix with about 2500 variables, and 100 rows.
I would like to calculate the means of the every 10 variables starting from
1:2500 and saving the results as a vector or matrix.
How can I do that?
Alternatively, How can I create 250 subset matrices in the order of
variabl
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