# Documentation

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# nanmin

Minimum ignoring `NaN`s

## Syntax

```m = nanmin(X)
[m,ndx] = nanmin(X)
m = nanmin(X,Y)
[m,ndx] = nanmin(X,[],DIM)
```

## Arguments

 `X` Financial times series object. `Y` Financial times series object or scalar. `DIM` Dimension along which the operation is conducted.

## Description

`nanmin` for financial times series objects is based on the Statistics and Machine Learning Toolbox™ function `nanmin`. See `nanmin`.

`m = nanmin(X)` returns the minimum of a financial time series object `X` with `NaN`s treated as missing. `m` is the smallest non-`NaN` element in `X`.

`[m,ndx] = nanmin(X)` returns the indices of the minimum values in `X`. If the values along the first nonsingleton dimension contain multiple elements, the index of the first one is returned.

`m = nanmin(X,Y)` returns an array the same size as `X` and `Y` with the smallest elements taken from `X` or `Y`. Only `Y` can be a scalar double.

`[m,ndx] = nanmin(X, [], DIM)` operates along the dimension `DIM`.

## Examples

To compute `nanmin` for the following dates:

```dates = {'01-Jan-2007';'02-Jan-2007';'03-Jan-2007'}; f = fints(dates, magic(3)); f.series1(1) = nan; f.series2(3) = nan; f.series3(2) = nan; [nmin, minidx] = nanmin(f)```
```nmin = 3 1 2 minidx = 2 1 3```