| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112 | [[search-aggregations-matrix-stats-aggregation]]=== Matrix StatsThe `matrix_stats` aggregation is a numeric aggregation that computes the following statistics over a set of document fields:[horizontal]`count`:: Number of per field samples included in the calculation.`mean`:: The average value for each field.`variance`:: Per field Measurement for how spread out the samples are from the mean.`skewness`:: Per field measurement quantifying the asymmetric distribution around the mean.`kurtosis`:: Per field measurement quantifying the shape of the distribution.`covariance`:: A matrix that quantitatively describes how changes in one field are associated with another.`correlation`:: The covariance matrix scaled to a range of -1 to 1, inclusive. Describes the relationship between field            distributions.The following example demonstrates the use of matrix stats to describe the relationship between income and poverty.[source,js]--------------------------------------------------{    "aggs": {        "matrixstats": {            "matrix_stats": {                "fields": ["poverty", "income"]            }        }    }}--------------------------------------------------The aggregation type is `matrix_stats` and the `fields` setting defines the set of fields (as an array) for computingthe statistics. The above request returns the following response:[source,js]--------------------------------------------------{    ...    "aggregations": {        "matrixstats": {            "fields": [{                "name": "income",                "count": 50,                "mean": 51985.1,                "variance": 7.383377037755103E7,                "skewness": 0.5595114003506483,                "kurtosis": 2.5692365287787124,                "covariance": {                    "income": 7.383377037755103E7,                    "poverty": -21093.65836734694                },                "correlation": {                    "income": 1.0,                    "poverty": -0.8352655256272504                }            }, {                "name": "poverty",                "count": 50,                "mean": 12.732000000000001,                "variance": 8.637730612244896,                "skewness": 0.4516049811903419,                "kurtosis": 2.8615929677997767,                "covariance": {                    "income": -21093.65836734694,                    "poverty": 8.637730612244896                },                "correlation": {                    "income": -0.8352655256272504,                    "poverty": 1.0                }            }]        }    }}--------------------------------------------------==== Multi Value FieldsThe `matrix_stats` aggregation treats each document field as an independent sample. The `mode` parameter controls whatarray value the aggregation will use for array or multi-valued fields. This parameter can take one of the following:[horizontal]`avg`:: (default) Use the average of all values.`min`:: Pick the lowest value.`max`:: Pick the highest value.`sum`:: Use the sum of all values.`median`:: Use the median of all values.==== Missing ValuesThe `missing` parameter defines how documents that are missing a value should be treated.By default they will be ignored but it is also possible to treat them as if they had a value.This is done by adding a set of fieldname : value mappings to specify default values per field.[source,js]--------------------------------------------------{    "aggs": {        "matrixstats": {            "matrix_stats": {                "fields": ["poverty", "income"],                "missing": {"income" : 50000} <1>            }        }    }}--------------------------------------------------<1> Documents without a value in the `income` field will have the default value `50000`.==== ScriptThis aggregation family does not yet support scripting.
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