This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementLinear CorrelationCalculates for each pair of selected columns a correlation coefficient, i.e. a measure of the correlation of the two variables.
Which correlation measure is applied depends on the types of the
underlying variables:
numeric <-> numeric:
Pearson's product-moment coefficient.
Missing values in a column are ignored in such a way that for the
computation of the correlation between two columns only complete
records are taken into account. For instance, if there are three
columns A, B and C and a row contains a missing value in column A
but not in B and C, then the row will be ignored for computing the
correlation between (A, B) and (A, C). It will not be ignored for
the correlation between (B, C). This corresponds to the function
cor(<data.frame>, use="pairwise.complete.obs")
in the R statistics package.
The value of this measure ranges from -1 (strong negative
correlation) to 1 (strong positive correlation). A value of 0
represents no linear correlation (the columns might still be
highly dependent on each other, though).
nominal <-> nominal:
Pearson's chi square test on the contingency table.
This value is then normalized to a range [0,1] using
Cramer's V, whereby 0 represents no correlation and 1
a strong correlation. Missing values in nominal columns are
treated such as they were a self-contained possible value.
If one of the two columns contains more possible values than
specified in the dialog (default 50), the correlation will not
be computed.
Correlation measures for other pairs of columns are not
available, they are represented by missing values in the output
table and crosses in the accompanying view.
Options
Manual Selection
Wildcard/Regex Selection
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Views
- Correlation Matrix
- Squared table view showing the pair-wise correlation values of all columns. The color range varies from dark red (strong negative correlation), over white (no correlation) to dark blue (strong positive correlation). If a correlation value for a pair of column is not available, the corresponding cell contains a missing value (shown as cross in the color view).
Workflows
01_AnalyticsKNIME Hub- 01_AnalyticsKNIME Hub
- 01_Correlation_MatrixKNIME Hub
- 01_Data_PreparationKNIME Hub
- 01_Forest_Fire_PredictionKNIME Hub
- Show all 100 workflows
Links
- Booking time vs. cancellation correlationby armingrudd on 2019-05-12forum.knime.com/p/60388
- Feature selectionby mlauber71 on 2019-09-06forum.knime.com/p/69527
Developers
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