Correlation Aggregates

The correlation aggregates determine the relationship between elements.

The URI for the data science functions is <http://cambridgesemantics.com/anzograph/statistics#>. For readability, the syntax for each function below includes the prefix stats:, defined as PREFIX stats: <http://cambridgesemantics.com/anzograph/statistics#>.

Matthews Correlation Coefficient (MCC)

The Matthews correlation coefficient aggregate returns a coefficient value between observed and predicted binary classifications.

Syntax

stats:mcc(x, y)
Parameter Type Description
x boolean First variable column data.
y boolean Second variable column data.

Returns

Type Description
double Coefficient value that shows the extent to which observed and predicted binary classifications are related.

Pearson Correlation Coefficient (PCC)

The Pearson correlation coefficient aggregate determines the extent to which two variables are linearly related: positive, negative, or no relationship.

Syntax

stats:pcc(x, y)
Parameter Type Description
x boolean First variable column data.
y boolean Second variable column data.

Returns

Type Description
double Coefficient that shows the extent to which two variables are linearly related.

Spearman's Correlation Coefficient (SCC)

The Spearman's rank correlation coefficient aggregate determines how well the relationship between two variables can be described using a monotonic function.

Syntax

stats:scc(rank_X, rank_Y)
Parameter Type Description
rank_X double First set of ranked data.
rank_Y double Second set of ranked data.

Returns

Type Description
double Coefficient between ranked datasets.