Programmer Guide/Command Reference/EVAL/var: Difference between revisions

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Compute the variance of ''x''.
Compute the variance, covariance or covariance-matrix.
----
----
;Usage 1: '''<code>var(''x''<sub>vector</sub>)</code>'''
;Usage 1: '''<code>var(''x''<sub>vector</sub>)</code>'''
;Result 1: The variance ''v'' of vector ''x''.  
;Result 1: The variance ''v'' of vector ''x''.  
:<code>''v'' = sum<sub>i = 0..ncol(''x'')-1</sub> ( (x<sub>i</sub>-avr(''x''))^2 ) / (ncol(''x'')-1)</code>
:<code>''v'' = sum<sub>i = 0..ncol(''x'')-1</sub> ( (''x''<sub>i</sub>-avr(''x''))^2 ) / (ncol(''x'')-1)</code>
----
----
;Usage 2: '''<code>var(''x''<sub>vector</sub>, ''y''<sub>vector</sub>)</code>'''
;Usage 2: '''<code>var(''x''<sub>vector</sub>, ''y''<sub>vector</sub>)</code>'''
;Result 2: The covariance ''v'' of the vectors ''x'' and ''y''.  
;Result 2: The covariance ''v'' of the vectors ''x'' and ''y''.  
:<code>''v'' = sum<sub>i = 0..ncol(''x'')</sub> ( (x<sub>i</sub>-avr(''x'')) * (y<sub>i</sub>-avr(''y'')) ) / (ncol(''x'')-1)</code>
:<code>''v'' = sum<sub>i = 0..ncol(''x'')</sub> ( (''x''<sub>i</sub>-avr(''x'')) * (''y''<sub>i</sub>-avr(''y'')) ) / (ncol(''x'')-1)</code>
;Usage 3: '''<code>var(''x''<sub>matrix</sub>, ''y''<sub>vector</sub>)</code>'''
----
;Usage 3: '''<code>var(''x''<sub>matrix</sub>)</code>'''
:'''<code>var(''x''<sub>matrix</sub>, ''y''<sub>scalar</sub>)</code>'''
:'''<code>var(''x''<sub>matrix</sub>, ''y''<sub>vector</sub>)</code>'''
:'''<code>var(''x''<sub>matrix</sub>, ''y''<sub>vector</sub>)</code>'''
;Result 3: The covariance ''v'' of the vectors ''x'' and ''y''.  
;Result 3: CompuThe covariance matrix ''v'' of the column vectors of ''x''.  
:<code>''v'' = sum<sub>i = 0..ncol(''x'')</sub> ( (x<sub>i</sub>-avr(''x'')) * (y<sub>i</sub>-avr(''y'')) ) / (ncol(''x'')-1)</code>
:<code>''v<sub>i,j</sub>'' = sum<sub>k = 0..nrow(''x'')</sub> ( (x<sub>k,i</sub>-a<sub>i</sub>) * (x<sub>k,j</sub>-a<sub>j</sub>) ) / (ncol(''x'')-1) , with: i,j = 0..ncol(''x'')</code>
 
:The column averages a<sub>i=0..ncol(''x'') are computed as follows:
{|
:{|class="einrahmen"
|-
|''y'' not supplied ||... a<sub>i</sub> = avr(''x''<sub>*,i</sub>)
|Usage:
|var(xv)
|-
|Result type:
|scalar
|}
 
The covariance of the vector xv and <var>y</var>v.
 
{|
|-
|Usage:
|var(xv,<var>y</var>s)
|-
|Result type:
|scalar
|}
 
The covariance matrix for the column vector of the matrix xM. The column average a[j] is either calculated with avr(xM[*,j]) or through the argument y (a[j] = <var>y</var>S or a[j] = <var>y</var>V[j]).
 
{|
|-
|Usage:
|var(xm)
|-
|-
|  
|''y''<sub>scalar</sub> ||... a<sub>i</sub> = ''y''
|var(xm,<var>y</var>s)
|-
|-
|  
|''y''<sub>vector</sub> ||... a<sub>i</sub> = ''y''<sub>i</sub>
|var(xm,<var>y</var>v)
|-
|-
|Result type:
|matrix <nowiki>-</nowiki> (ncol(xm) x ncol(xm))
|}
|}

Revision as of 14:30, 8 April 2011

Compute the variance, covariance or covariance-matrix.


Usage 1
var(xvector)
Result 1
The variance v of vector x.
v = sumi = 0..ncol(x)-1 ( (xi-avr(x))^2 ) / (ncol(x)-1)

Usage 2
var(xvector, yvector)
Result 2
The covariance v of the vectors x and y.
v = sumi = 0..ncol(x) ( (xi-avr(x)) * (yi-avr(y)) ) / (ncol(x)-1)

Usage 3
var(xmatrix)
var(xmatrix, yscalar)
var(xmatrix, yvector)
Result 3
CompuThe covariance matrix v of the column vectors of x.
vi,j = sumk = 0..nrow(x) ( (xk,i-ai) * (xk,j-aj) ) / (ncol(x)-1) , with: i,j = 0..ncol(x)
The column averages ai=0..ncol(x) are computed as follows:
y not supplied ... ai = avr(x*,i)
yscalar ... ai = y
yvector ... ai = yi

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