Frame correlation_matrix

---

correlation_matrix(self, data_column_names, matrix_name=None)

Calculate correlation matrix for two or more columns.

Parameters:

data_column_names : list

The names of the columns from which to compute the matrix.

matrix_name : unicode (default=None)

The name for the returned matrix Frame.

Returns:

: Frame

A Frame with the matrix of the correlation values for the columns.

This method applies only to columns containing numerical data.

Examples

Consider Frame my_frame, which contains the data

>>> my_frame.inspect()
 [#]  idnum  x1   x2   x3   x4
===============================
[0]      0  1.0  4.0  0.0  -1.0
[1]      1  2.0  3.0  0.0  -1.0
[2]      2  3.0  2.0  1.0  -1.0
[3]      3  4.0  1.0  2.0  -1.0
[4]      4  5.0  0.0  2.0  -1.0

my_frame.correlation_matrix computes the common correlation coefficient (Pearson’s) on each pair of columns in the user-provided list. In this example, the idnum and most of the columns have trivial correlations: -1, 0, or +1. Column x3 provides a contrasting coefficient of 3 / sqrt(3) = 0.948683298051

>>> corr_matrix = my_frame.correlation_matrix(my_frame.column_names)
[===Job Progress===]

The resulting table (specifying all columns) is:

>>> corr_matrix.inspect()
[#]  idnum           x1              x2               x3               x4
==========================================================================
[0]             1.0             1.0             -1.0   0.948683298051  0.0
[1]             1.0             1.0             -1.0   0.948683298051  0.0
[2]            -1.0            -1.0              1.0  -0.948683298051  0.0
[3]  0.948683298051  0.948683298051  -0.948683298051              1.0  0.0
[4]             0.0             0.0              0.0              0.0  1.0