# mbetaTopN {#mbetatopn}

## Syntax {#syntax}

`mbetaTopN(X, Y, S, window, top, [ascending=true], [tiesMethod='oldest'])`

Please see [mTopN](../Themes/mTopN.md) for the parameters and windowing logic.

## Details {#details}

Within a sliding window of given length \(measured by the number of elements\), the function stably sorts *X* and *Y* by *S* in the order specified by *ascending*, then calculates the coefficient estimate ordinary-least-squares regressions of *Y* on *X*.

## Examples {#examples}

```
x = NULL 3 8 4 0
y = 2 3 1 7 3
s = 5 NULL 8 9 4
mbetaTopN(x, y, s, 3, 2)
// output: [ , , , -0.66, -4]

s2=2021.01.01 2021.02.03 2021.01.23 2021.04.06 2021.12.29
mbetaTopN(x, y, s2, 3, 2)
// output: [ , , , -2.5, -0.6667]

x1 = matrix(x, 4 3 6 2 3)
y1=matrix(3 7 9 3 2, y)
s1=matrix(2 3 1 7 3, s)

mbetaTopN(x, y1, s1, 3, 2)
```

|col1|col2|
|----|----|
|||
|||
|||
|2.5|-2.5|
|1.1429|-4|

```
mbetaTopN(x1, y1, s, 3, 2)
```

|col1|col2|
|----|----|
|||
||-1|
||-1|
|2.5|-1.5|
|1.1429|-1.5|

```
n = 3000
ids = 1..3000
dates = take(2021.01.01..2021.10.01,n)
prices = rand(1000,n)
vals = rand(1000,n)
t = table(ids as id,dates as date,prices as price,vals as val)
dbName = "dfs://test_mbetaTopN_2"
if(existsDatabase(dbName))dropDB(dbName)
db = database(dbName,VALUE,1..5000)
pt = db.createPartitionedTable(t,"pt",`id).append!(t)
select mbetaTopN(price, val, id, 10, 5, true) from pt where date>2021.05.01
```

Related function: [mbeta](mbeta.md)

