rms

Root mean square value.

📝Syntax

Y = rms(X)
Y = rms(X, DIM)
Y = rms(X, VECDIM)
Y = rms(X, "all")
Y = rms(..., TYPE)
Y = rms(..., NANFLAG)

📥Input Arguments

Parameter	Description
X	input data: single, double, logical or integer.
DIM	dimension to operate along.
VECDIM	vector of dimensions to operate along.
"all"	operate on every element of X.
TYPE	class of the result: "default", "double" or "native".
NANFLAG	"includenan" or "omitnan". "includemissing" and "omitmissing" are also accepted.

📤Output Arguments

Parameter	Description
Y	root mean square values.

📄Description

rms computes sqrt(mean(abs(X) .^ 2)) along the selected dimension:

$$\mathrm{RMS}(X) = \sqrt{ \frac{1}{N} \sum_{n=1}^{N} |x_n|^2 }$$

where N is the number of elements along that dimension.

If X is a vector, Y is a scalar.
If X is a matrix, Y is a row vector holding the value of each column.
If X is a multidimensional array, Y is computed along the first dimension whose size is not 1, unless a dimension is given.

Class of the result: the square and the mean always run in double, so an integer input never saturates. "native" returns the class of the input, "double" returns double, and "default" returns double for an integer input and the class of the input otherwise. A logical input is not an integer class and returns double.

Missing values: NaN values are included by default. Use "omitnan" or "omitmissing" to leave them out.

💡Examples

root mean square of a vector

t = 0:0.001:1-0.001;
x = cos(2*pi*100*t);
y = rms(x)
% y = 0.7071

one value per column

x = [4 -5 1; 2 3 5; -9 1 7];
y = rms(x)
% y = [5.8023 3.4157 5.0000]

one value per row

x = [6 4 23 -3; 9 -10 4 11; 2 8 -5 1];
y = rms(x, 2)
% y = [12.1450; 8.9163; 4.8477]

leaving missing values out

x = [1.77 -0.005 nan -2.95; nan 0.34 nan 0.19];
y = rms(x, "omitnan")
% y = [1.7700 0.2404 nan 2.0903]

integer input with a native result

M = uint8([10:30:70; 20:30:80; 30:30:90]);
R = rms(M, 'native')
% R = uint8([22 51 80])
D = rms(M)
% D = [21.6025 50.6623 80.4156]

🔗See Also

peak2peak max min

🕔Version History

Version	Description
1.16.0	initial version

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