Correlation Pairs To Matrix

Input Ports

Input Correlation Pairs: The input set of correlations as a list of pairs. Each pair should quantify the correlation between a single row and a single column for all unique row-column combinations for the Output Correlation Matrix. The Input Correlation Pairs should include the following columns:

1. From Distribution (string): The name of the first Customer Distribution for a row/column within the Output Correlation Matrix
2. To Distribution (string): The name of the second Customer Distribution for a column/row within the Output Correlation Matrix
3. Correlation (double): The degree of correlation between the first Customer Distribution and the Second Customer Distribution. If multiple correlations are provided for A:B or B:A then the lower correlations found in the input table will be used. This allows the user to append new rows to the bottom of the Input Correlation Pairs table and be confident that the new values will override the old values.

Output Ports

Output Correlation Matrix: The output set of correlations that define the relationship between Customer Distributions. The Correlation Matrix will be symmetrical such that the number of data rows match the number of columns. Each row Distribution Name will be unique and correspond to a column of the same name. The Output Correlation Matrix will contain these columns:

1. Distribution: The row name of the first Customer Distribution within the Output Correlation Matrix.
2. Correlated Distributions: The column name of the second Customer Distribution within the Output Correlation Matrix, along with the degree of correlation to the row Customer Distribution. Output correlations will be symmetrical and range-limited to -1.0 and +1.0.

Output Correlation Repaired Matrix: The repaired output set of correlations that define the relationship between Customer Distributions. Repairing is required when the correlations are unrealistic. For example, if A is highly correlated to B (for example, A:B = +0.99) and if A is highly correlated with C (for example, A:C = +0.99) then B must be highly correlated with C (that is, B:C >> 0.0). More precisely, the Correlation Matrix must have all positive definite Eigenvalues. Note that it is not necessary for downstream nodes that generate Customer Distributions (such as the Matrix Distributions node or the Feature Generation node) to use this Correlation Repaired Matrix as these downstream nodes will always first self-repair the Input Correlation Matrix. The Output Correlation Repaired Matrix will contain the same columns as the Output Correlation Matrix:

1. Distribution: The row name of the first Customer Distribution within the Output Correlation Repaired Matrix.
2. Correlated Distributions: The column name of the second Customer Distribution within the Output Correlation Matrix, along with the repaired degree of correlation to the row Customer Distribution. Output correlations will be symmetrical and range-limited to -1.0 and +1.0.

Output Correlation Error Matrix: The difference between the Output Correlation Matrix and the Output Correlation Repaired Matrix. This is a convenience output to show how the Correlation Matrix needs to be repaired before Customer Distributions can be generated. The Output Correlation Error Matrix will contain the same columns as the Output Correlation Matrix:

1. Distribution: The row name of the first Customer Distribution within the Output Correlation Error Matrix.
2. Correlated Distributions: The column name of the second Customer Distribution within the Output Correlation Matrix, along with the difference between the output correlation and the repaired correlation.

Popular Predecessors

• Price Sensitivity21 %
• Linear Correlation13 %
• Low Variance Filter11 %
• Correlation Matrix To Pairs11 %
• Table Row to Variable9 %
• Show all 19 recommendations

Popular Successors

• Matrix Distributions29 %
• Convert Table to Matrix14 %
• Correlation Concatenation14 %
• Correlation Matrix To Pairs14 %
• Color Manager14 %
• Excel Sheet Appender (XLS)14 %