The CORRELATE function computes the linear Pearson correlation coefficient of two vectors or the correlation matrix of an m x n array. Alternatively, this function computes the covariance of two vectors or the covariance matrix of an m x n array.
This routine is written in the IDL language. Its source code can be found in the file correlate.pro in the lib subdirectory of the IDL distribution.
Define the data vectors.
X = [65,63,67,64,68,62,70,66,68,67,69,71]
Y = [68,66,68,65,69,66,68,65,71,67,68,70]
Compute the linear Pearson correlation coefficient of x and y. The result should be 0.702652:
PRINT, CORRELATE(X, Y)
IDL prints:
0.702652
Compute the covariance of x and y. The result should be 3.66667.
PRINT, CORRELATE(X, Y, /COVARIANCE)
IDL prints:
3.66667
Define an array with x and y as its columns.
A = TRANSPOSE([[X],[Y]])
Compute the correlation matrix.
PRINT, CORRELATE(A)
IDL prints:
1.00000 0.702652
0.702652 1.00000
Result = CORRELATE( X [, Y] [, /COVARIANCE] [, /DOUBLE] )
If vectors of unequal lengths are specified, the longer vector is truncated to the length of the shorter vector and a single correlation coefficient is returned. If an m x n array is specified, the result will be an m x m array of linear Pearson correlation coefficients, with the element i,j corresponding to correlation of the ith and jth columns of the input array.
A vector or an m x n array. X can be integer, single-, or double-precision floating-point.
An integer, single-, or double-precision floating-point vector. If X is an m x n array, Y should not be supplied.
Set this keyword to compute the sample covariance rather than the correlation coefficient.
Set this keyword to force the computation to be done in double-precision arithmetic.
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Pre 4.0 |
Introduced |
J. Neter, W. Wasserman, G.A. Whitmore, Applied Statistics (Third Edition), Allyn and Bacon (ISBN 0-205-10328-6).
A_CORRELATE, C_CORRELATE, M_CORRELATE, P_CORRELATE, R_CORRELATE