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.
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.
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
|
Pre 4.0 |
Introduced |
A_CORRELATE , C_CORRELATE , M_CORRELATE , P_CORRELATE, R_CORRELATE