Mean

Computes the arithmetic mean along the specified dimensions. You can also use mean().

Usage

nv_mean(operand, dims = NULL, drop = TRUE, nan_rm = FALSE)

# S3 method for class 'AnvlArray'
mean(x, trim = 0, na.rm = FALSE, ..., dims = NULL, drop = TRUE)

Arguments

operand

(arrayish)
Operand.

dims

(integer() | NULL)
Dimensions to reduce. If NULL (default), reduces over all dimensions, returning a scalar.

drop

(logical(1))
Whether to drop reduced dimensions.

nan_rm

(logical(1))
How to handle NaN values in floating-point inputs. If FALSE (default), NaN propagates. If TRUE, NaN values are skipped.

x

(arrayish)
Same as operand; this is the name used by the base R S3 generic.

trim

Currently not supported.

na.rm

Forwarded to nv_mean()'s nan_rm argument.

...

No additional arguments.

Value

arrayish
Has the same data type as the input. When drop = TRUE, the reduced dimensions are removed. When drop = FALSE, the reduced dimensions are set to 1.

See also

nv_reduce_sum()

Examples

x <- nv_matrix(1:6, nrow = 2)
nv_mean(x)            # all dims -> scalar
#> AnvlArray
#>  3.5000
#> [ CPUf32?{} ] 
nv_mean(x, dims = 1L)
#> AnvlArray
#>  1.5000
#>  3.5000
#>  5.5000
#> [ CPUf32?{3} ] 
nv_mean(nv_array(c(1, NaN, 3)))
#> AnvlArray
#>  nan
#> [ CPUf32{} ] 
nv_mean(nv_array(c(1, NaN, 3)), nan_rm = TRUE)
#> AnvlArray
#>  2
#> [ CPUf32{} ]