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See the recommended documentation of this function

Scilab Help >> Statistics > Data with Missing Values > nanmean

# nanmean

mean (ignoring Nan's)

### Syntax

```m=nanmean(val)
m=nanmean(val,'r') (or m=nanmean(val,1))
m=nanmean(val,'c') (or m=nanmean(val,2))```

### Arguments

val

real or complex vector or matrix

### Description

This function returns in scalar `m` the mean of the values (ignoring the NANs) of a vector or matrix `val`.

For a vector or matrix `val` , `m=nanmean(val)` or `m=nanmean(val,'*')` returns in scalar `m` the mean of all the entries (ignoring the NANs) of `val`.

`m=nanmean(val,'r')` (or, equivalently, `m=nanmean(val,1)`) returns in each entry of the row vector `m` of type 1xsize(val,'c') the mean of each column of `val` (ignoring the NANs).

`m=nanmeanf(val,'c')` (or, equivalently, `m=nanmean(val,2)`) returns in each entry of the column vector `m` of type size(val,'c')x1 the mean of each row of `val` (ignoring the NANs).

In Labostat, NAN values stand for missing values in tables.

### Examples

```x=[0.2113249 %nan 0.6653811;0.7560439 0.3303271 0.6283918]
m=nanmean(x)
m=nanmean(x,1)
m=nanmean(x,2)```

### Bibliography

Wonacott, T.H. & Wonacott, R.J.; Introductory Statistics, fifth edition, J.Wiley & Sons, 1990.