# MutualInfo - chart function MutualInfo calculates the mutual information (MI) between two fields or between aggregated values in Aggr(). MutualInfo returns the aggregated mutual information for two datasets. This allows key driver analysis between a field and a potential driver. Mutual information measures 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 and can be formatted as a percentile value. MutualInfo is defined by either selections or by a set expression. MutualInfo allows 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 does not necessarily indicate causality between fields sharing mutual information. Two fields may share mutual information, but may not be equal drivers for each other. For example, when comparing ice cream sales and outdoor temperature, MutualInfo will show mutual information between the two. It will not indicate if it is outdoor temperature driving ice cream sales, which is likely, or if it is ice cream sales that drives outdoor temperature, which is unlikely. 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 fields or selections may vary slightly. This is due to each MutualInfo call operating 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] field1, field2 , datatype [, breakdownbyvalue [, samplesize ]]) Return data type: numeric Arguments: Arguments | Argument | Description | | --- | --- | | field1, field2 | 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.

By using TOTAL [<fld {.fld}>], where the TOTAL qualifier is followed by a list of one or more field names as a subset of the chart dimension variables, you create a subset of the total possible values.

[Defining the aggregation scope](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/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 application and run it. To see the result, add the fields listed in the results column to a sheet in your application. 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 '|');` Related learning: * [How do I evaluate relationships using Mutual information?](https://help.qlik.com/lnk/How_evaluate_relationships_using_Mutual_information) ## Learn more * [How do I evaluate relationships using Mutual information?](https://help.qlik.com/lnk/How_evaluate_relationships_using_Mutual_information)