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See the recommended documentation of this function
stdev
standard deviation (row orcolumn-wise) of vector/matrix entries
Calling Sequence
y = stdev(x) y = stdev(x, '*') y = stdev(x, 'r') y = stdev(x, 'c') y = stdev(x, orien, m)
Arguments
- x, y
real vector, matrix or hypermatrix
- y
real scalar, vector or matrix
- orien
string scalar or positive integer, can be
"*"
,"r"
(or1
) or"c"
(or2
)- m
real scalar, vector or hypermatrix, the a priori mean
Description
stdev computes the "sample" standard deviation, that
is, it is normalized by N-1, where N is the sequence length.
If m
is present, then stdev
computes the
mean squared deviation (normalized by N) using the a priori mean defined by m
.
For a vector or a matrix x
, y=stdev(x)
returns in the
scalar y
the standard deviation of all the entries of x
.
y=stdev(x,'r')
(or, equivalently,
y=stdev(x,1)
) is the rowwise standard deviation. It returns in each
entry of the row vector y
the standard deviation of each column of x
.
y=stdev(x,'c')
(or, equivalently, y=stdev(x,2)
) is the columnwise stdev. It returns in each
entry of the column vector y
the standard deviation of each row of x
.
By extension, y=stdev(x,n)
with n
a positive integer returns the deviation
along the n
-th dimension, and if n>ndims(x)
, then stdev(x,n)
returns zeros(x)
.
Examples
A = [1 2 10; 7 7.1 7.01]; stdev(A) stdev(A, 'r') stdev(A, 'c') stdev(A, 'c', mean(A,'c')) stdev(A, 'c', 1)
See Also
History
Version | Description |
5.5.0 | Can now compute the mean squared deviation using the a priori mean defined by m |
Report an issue | ||
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