v = nanvar (x) returns the variance of x, after removing NaN values. If x is a vector, a scalar value is returned. If x is a matrix, a row vector of column variances is returned. If x is a multidimensional array, nanvar operates along the first nonsingleton dimension. If a dimension contains fewer than two non-NaN values, the variance is returned as 0 for a single value and as NaN when all values are NaN.

v = nanvar (x, w) specifies the normalization. When w is 0 (default), the variance is normalized by $N-1$, where $N$ is the number of non-NaN observations. When w is 1, it is normalized by $N$. w may also be a vector of nonnegative weights whose length matches the operating dimension, in which case the weighted variance normalized by the sum of the weights is returned.

v = nanvar (x, w, 'all') returns the variance of all elements of x, after removing NaN values. Use an empty value, w = [], to pass the default normalization.

v = nanvar (x, w, dim) operates along the dimension dim of x.

v = nanvar (x, w, vecdim) returns the variance over the dimensions specified in the vector vecdim. A weight vector is not supported together with 'all' or vecdim. Any dimension in vecdim greater than ndims (x) is ignored.

See also: var, nanstd, nanmean, nansum

Source Code: nanvar