scale function within a for-loop?

apply(scale(as.matrix(kalcin[,3:7])), na.rm=T,2,sd)

apply(scale(as.matrix(kalcin[,3:7])), na.rm=T,2,mean)

I have a data matrix containing around 180 variables and more than 470
observations for each and no missing values. 

Within a for-loop, the first step of calculations is to standardize
each column, such that the mean of each column is zero and the sd is
one. The for-loop starts with a subset of the initial matrix and
includes all columns but only a third of the rows. The loop "works
itself through" the whole matrix and adds everytimes one row, so in
the last loop, the whole data matrix is used. The standardization
within the loop is done using the "scale" function. 

Now, my problem is that with all the 180 variables, either the
for-loop or the scale function does not work properly, as the
resulting matrix after the standardization does not have the same
dimensions anymore as it had before. The matrix is no longer a 180*470
matrix, but a 180*130 matrix. If, however, I include only 130
variables instead of 180, the result is correct, the dimensions are
right and each column indeed has mean zero and sd one. 

Can anyone please tell me, why this problem appears? Would there be a
way that gives the same result without using a for-loop?

Thanks





 [[alternative HTML version deleted]]