nanvar - Variance, ignoring NaN values
Syntax
y = nanvar(X)
y
= nanvar(X,1)
y = nanvar(X,w)
y = nanvar(X,w,dim)
Description
y = nanvar(X) is the variance var of X, computed
after removing NaN values.
For vectors x, nanvar(x) is
the sample variance of the remaining elements, once NaN values
are removed. For matrices X, nanvar(X) is
a row vector of column sample variances, once NaN values
are removed. For multidimensional arrays X, nanvar operates
along the first nonsingleton dimension.
nancov removes the mean from each variable
(column for matrix X) before calculating Y.
If n is the number of remaining observations after
removing observations with NaN values, nanvar normalizes y by
either n – 1 or n , depending
on whether n > 1 or n = 1,
respectively. To specify normalization by n, use y
= nanvar(X,1).
y = nanvar(X,w) computes the variance using
the weight vector w. The length of w must
equal the length of the dimension over which nanvar operates,
and its elements must be nonnegative. Elements of X corresponding
to NaN values of w are ignored.
y = nanvar(X,w,dim) takes the variance
along the dimension dim of X.
Set w to [] to use the default
normalization by n – 1.
Examples
Find column standard deviations for data with missing values:
X = magic(3);
X([1 6:9]) = repmat(NaN,1,5)
X =
NaN 1 NaN
3 5 NaN
4 NaN NaN
y = nanvar(X)
y =
0.5000 8.0000 NaNSee Also
NaN, var, nanstd, nanmean
 | nansum | | nbincdf |  |
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