# MutualInfo - chart function MutualInfo calculates the mutual information (MI) between two fields or between aggregated values in Aggr(). MutualInfo enables different kinds of MI analysis: * Pair-wise MI: Calculate the MI between a driver field and a target field. * Driver breakdown by value: The MI is calculated between individual field values in the driver and target fields. * Feature selection: Use MutualInfo in a grid chart to create a matrix where all fields are compared to each other based on MI. MutualInfo returns the aggregated mutual information for two data sets. Mutual information is a measure of the relationship between the datasets and is aggregated for (x,y) pair values iterated over the chart dimensions. Mutual information is measured between 0 and 1. MutualInfo is defined by the selection or the set expression. When calculating mutual information, associations affect the correspondence between and the frequency of values from fields that are from different tables. Returned values for the same target and driver may vary slightly. This is due to each MutualInfo call operates on a randomly selected sample and the inherent randomness of the MutualInfo algorithm. MutualInfo can be applied to the Aggr() function. Syntax: MutualInfo({SetExpression}] [DISTINCT] [TOTAL] target, driver , datatype [, breakdownbyvalue [, samplesize ]]) Return data type: numeric Arguments: Arguments | Argument | Description | | --- | --- | | target, driver | The expressions or fields containing the two sample sets for which the mutual information to be measured. | | datatype | The data types contained in the target and driver,

1 or 'dd' for discrete:discrete

2 or 'cc' for continuous:continuous

3 or 'cd' for continuous:discrete

4 or 'dc' for discrete:continuous

Data types are not case sensitive. | | breakdownbyvalue | A static value corresponding to a value in the driver. If supplied, the calculation will calculate the MI contribution for that value. You can use ValueList() or ValueLoop(). If Null() is added, the calculation will calculate the overall MI for all values in the driver.

Breaking down by value requires the driver contain discrete data. | | samplesize | The number of values to sample from the target and driver. Sampling is random. MutualInfo requires a minimum sample size of 80. By default, MutualInfo only samples up to 10,000 data-pairs as MutualInfo can be resource intensive. You can specify greater numbers of data-pairs in the sample size. If MutualInfo times out, reduce the sample size. | | SetExpression | By default, the aggregation function
will aggregate over the set of possible records defined by the selection.
An alternative set of records can be defined by a set analysis
expression. | | DISTINCT | If the word
DISTINCT occurs before the function
arguments, duplicates resulting from the evaluation of the function arguments are disregarded. | | TOTAL | If the word
TOTAL occurs before the function
arguments, the calculation is made over all possible values given
the current selections, and not just those that pertain to the current dimensional value, that is, it disregards the chart dimensions. The TOTAL qualifier may be followed by a list of one or more field names within
angle brackets <fld>. These field names should be a subset of the chart dimension
variables.

[Defining the aggregation scope](https://help.qlik.com/en-US/qlikview/September2026/Subsystems/Client/Content/QV_QlikView/ChartFunctions/define-aggregation-scope.htm) | Limitations: Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair being disregarded. Examples and results: Add the example script to your app and run it. Then add the fields listed in the results column to a sheet in your app to see the result. Function examples | Example | Result | | --- | --- | | `mutualinfo(Age, Salary, 1)` | For a table including the dimension `Employee name` and the measure `mutualinfo(Age, Salary, 1)`, the result is 0.99820986. The result is only displayed for the totals cell. | | `mutualinfo(TOTAL Age, Salary, 1, null(), 81)` | If you create a filter pane with the dimension Gender, and make selections from it, you see the result 0.99805677 when Female is selected and 0.99847373 if Male is selected. This is because the selection excludes all results that do not belong to the other value of Gender. | | `mutualinfo(TOTAL Age, Gender, 1, ValueLoop(25,35))` | 0.68196996. Selecting any value from Gender will change this to 0. | | `mutualinfo({1} TOTAL Age, Salary, 1, null())` | 0.99820986. This is independent of selections. The set expression {1} disregards all selections and dimensions. | Data used in examples: `Salary:` `LOAD * inline [` `"Employee name"|Age|Gender|Salary` `Aiden Charles|20|Male|25000` `Ann Lindquist|69|Female|58000` `Anna Johansen|37|Female|36000` `Anna Karlsson|42|Female|23000` `Antonio Garcia|20|Male|61000` `Benjamin Smith|42|Male|27000` `Bill Yang|49|Male|50000` `Binh Protzmann|69|Male|21000` `Bob Park|51|Male|54000` `Brenda Davies|25|Male|32000` `Celine Gagnon|48|Female|38000` `Cezar Sandu|50|Male|46000` `Charles Ingvar Jönsson|27|Male|58000` `Charlotte Edberg|45|Female|56000` `Cindy Lynn|69|Female|28000` `Clark Wayne|63|Male|31000` `Daroush Ferrara|31|Male|29000` `David Cooper|37|Male|64000` `David Leg|58|Male|57000` `Eunice Goldblum|31|Female|32000` `Freddy Halvorsen|25|Male|26000` `Gauri Indu|36|Female|46000` `George van Zaant|59|Male|47000` `Glenn Brown|58|Male|40000` `Harry Jones|38|Male|40000` `Helen Brolin|52|Female|66000` `Hiroshi Ito|24|Male|42000` `Ian Underwood|40|Male|45000` `Ingrid Hendrix|63|Female|27000` `Ira Baumel|39|Female|39000` `Jackie Kingsley|23|Female|28000` `Jennica Williams|36|Female|48000` `Jerry Tessel|31|Male|57000` `Jim Bond|50|Male|58000` `Joan Callins|60|Female|65000` `Joan Cleaves|25|Female|61000` `Joe Cheng|61|Male|41000` `John Doe|36|Male|59000` `John Lemon|43|Male|21000` `Karen Helmkey|54|Female|25000` `Karl Berger|38|Male|68000` `Karl Straubaum|30|Male|40000` `Kaya Alpan|32|Female|60000` `Kenneth Finley|21|Male|25000` `Leif Shine|63|Male|70000` `Lennart Skoglund|63|Male|24000` `Leona Korhonen|46|Female|50000` `Lina André|50|Female|65000` `Louis Presley|29|Male|36000` `Luke Langston|50|Male|63000` `Marcus Salvatori|31|Male|46000` `Marie Simon|57|Female|23000` `Mario Rossi|39|Male|62000` `Markus Danzig|26|Male|48000` `Michael Carlen|21|Male|45000` `Michelle Tyson|44|Female|69000` `Mike Ashkenaz|45|Male|68000` `Miro Ito|40|Male|39000` `Nina Mihn|62|Female|57000` `Olivia Nguyen|35|Female|51000` `Olivier Simenon|44|Male|31000` `Östen Ärlig|68|Male|57000` `Pamala Garcia|69|Female|29000` `Paolo Romano|34|Male|45000` `Pat Taylor|67|Female|69000` `Paul Dupont|34|Male|38000` `Peter Smith|56|Male|53000` `Pierre Clouseau|21|Male|37000` `Preben Jørgensen|35|Male|38000` `Rey Jones|65|Female|20000` `Ricardo Gucci|55|Male|65000` `Richard Ranieri|30|Male|64000` `Rob Carsson|46|Male|54000` `Rolf Wesenlund|25|Male|51000` `Ronaldo Costa|64|Male|39000` `Sabrina Richards|57|Female|40000` `Sato Hiromu|35|Male|21000` `Sehoon Daw|57|Male|24000` `Stefan Lind|67|Male|35000` `Steve Cioazzi|58|Male|23000` `Sunil Gupta|45|Male|40000` `Sven Svensson|45|Male|55000` `Tom Lindwall|46|Male|24000` `Tomas Nilsson|27|Male|22000` `Trinity Rizzo|52|Female|48000` `Vanessa Lambert|54|Female|27000` `] (delimiter is '|');`