escovc

Computes sample cross-covariances at lag 0 only.

Prototype

	function escovc (
		x  : numeric,  
		y  : numeric   
	)

	return_val  :  numeric

An array of any numeric type or size. The rightmost dimension is usually time.

An array of any numeric type or size. The rightmost dimension is usually time. The size of the rightmost dimension must be the same as x.

A scalar value if x and y are one-dimensional. The same size as x if either x or y are multi-dimensional. Double if x is double, float otherwise.

Computes sample cross-covariances at lag 0 only. If a lagged covariance is required, use esccv. Missing values are allowed.

Algorithm:

     cov = SUM [(X(t)-Xave)*(Y(t)-Yave)]/(NT-1)

The dimension sizes(s) of c are a function of the dimension sizes of the x and y arrays. The following illustrates dimensioning:

        x(N), y(N)          a scalar
        x(N), y(K,M,N)      c(K,M)
      x(I,N), y(K,M,N)      c(I,K,M)
    x(J,I,N), y(L,K,M,N)    c(J,I,L,K,M)

special case when dimensions of all x and y are identical:

    x(J,I,N), y(J,I,N)      c(J,I)

Example 1

The following will calculate the cross-covariance for a two one-dimensional arrays x(N) and y(N).

     ccv = escovc(x,y)   ; ccv is a scalar

Example 2

The following will calculate the cross-covariance for one two-dimensional array y(lat,lon,time) and one one-dimensional array x(time).

     ccv = escovc(x,y)    ; ccv(lat,lon)

Example 3

Consider x(neval,time) and y(lat,lon,time)

     ccv = escovc(x,y)    ; ccv(neval,lat,lon)