stdev
standard deviation (row or column-wise) of vector/matrix entries
Syntax
y = stdev(x)
y = stdev(x, '*')
y = stdev(x, 'r'|1)
y = stdev(x, 'c'|2)
y = stdev(x, orien, m)
Argumentsx, yreal vector, matrix or hypermatrixyreal scalar, vector or matrixorien"*" (default), "r" or 1,
"c" or 2, or 0<integer<=ndims(x): direction along
which calculations are performed.
mreal scalar, vector or hypermatrix, the a priori meanDescription
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.
If m is a scalar, then it is expanded to match the size of
mean(x) along the n-th dimension.
stdev() can be overloaded.
Examples assessed from A before computing stdev
stdev(A, '*', 7.5) / 3 // using the theoretical built-in mean
// With an hypermatrix:
A = grand(3, 5, 30, "nor", 4.1, 1.5);
stdev(A) / 1.5
sd = stdev(A, 3, 4.1) / 1.5
mean(sd)
]]>See also
nanstdev
stdevf
sum
median
mean
History5.5.0
Can now compute the mean squared deviation using the a priori mean defined by m6.0.0
stdev(x, orien>ndims(x)) no longer returns zeros(x) but yields an error.
6.0.1
stdev() is now overloadable.