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Ajuda do Scilab >> Estatística > Mean Central Tendency > mean

# mean

mean of all values, or means along a given dimension

### Syntax

y = mean(x) y = mean(x, orientation)

### Arguments

- x
- Vector, matrix, or hypermatrix of real or complex numbers. Sparse matrices accepted.
- orientation
- direction = index of the dimension along which the mean is computed.
"r" is equivalent to 1. "c" is equivalent to 2. "m" is equivalent
to
`find(size(x)>1,1)`

. - y
- dense scalar if
`orientation`

is not used or if`x`

is a scalar. Otherwise, array such that`size(y,orientation)`

is 1 (sparse-encoded if`x`

is so).

### Description

**y = mean(x)** returns the mean of all
entries. If at least one entry is NaN, NaN is returned.
This scalar result is always dense-encoded.

**y = mean(x,1)** or `y=mean(x,"r")`

computes the means accross rows. `y`

is a row if
`x`

is a matrix.

**y = mean(x,2)** or `y=mean(x,"c")`

computes the means accross columns. `y`

is a column if
`x`

is a matrix.

**y = mean(x, n)** with `3 ≤ n ≤ ndims(x)`

computes the means along the n^{th} dimension of `x`

.

**y = mean(x,'m')** is the mean along the
first non singleton dimension of x (for compatibility with Matlab).

`mean([])` and `mean(sparse([]))` return NaN.
For any `orientation` not "m",
`mean([], orientation)` returns `[]` , and
`mean(sparse([]), orientation)` returns `sparse([])` . |

`mean()` can be overloaded. |

### Examples

With a matrix:

A = [0,1,1,0,1;1,0,0,1,1;0,0,1,0,0;0,0,1,0,0] mean(A) mean(A, 'r') mean(A, 'c')

--> A = [0,1,1,0,1;1,0,0,1,1;0,0,1,0,0;0,0,1,0,0] A = 0. 1. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. --> mean(A) ans = 0.4 --> mean(A, 'r') ans = 0.25 0.25 0.75 0.25 0.5 --> mean(A, 'c') ans = 0.6 0.6 0.2 0.2

With an hypermatrix:

A = [1,0,0,1,0,0,1,1,1,1,1,1,0,1,0,1,1,0,1,1,1,1,1,0,1,0,1,0,1,1,1,0,1,0,0,1,0,1,1,0]; A = matrix(A, [4,5,2]) mean(A) mean(A, 'r') mean(A, 'c') mean(A, 3) A = matrix(1:12, [1,1,2,3,2]); // in this case mean(A,'m') is equivalent to mean(A,3), the first non singleton dimension of A mean(A, 'm')

--> A = matrix(A, [4,5,2]) A = (:,:,1) 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. (:,:,2) 1. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. --> mean(A) ans = 0.625 --> mean(A, 'r') ans = (:,:,1) 0.5 0.5 1. 0.5 0.75 (:,:,2) 0.75 0.5 0.75 0.5 0.5 --> mean(A, 'c') ans = (:,:,1) 0.6 0.4 0.6 1. (:,:,2) 0.8 0.6 0.8 0.2 --> mean(A, 3) ans = 1. 0.5 1. 0.5 0.5 0.5 0. 1. 0.5 0.5 0.5 1. 1. 0. 1. 0.5 0.5 0.5 1. 0.5

### See also

### History

Versão | Descrição |

6.0.1 | mean() is now overloadable. |

6.1.1 | Extension to sparse matrices. |

## Comments

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