Programmer Guide/Command Reference/EVAL/rpolyreg: Difference between revisions
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=== | Linear, multivariant and polynominal regression. | ||
---- | |||
;Usage 1: <code>rpolyreg(<var>x<sub>vector</sub></var>, <var>y<sub>vector</sub></var> {, <var>m</var>})</code>: | |||
:;<var>x</var>: ''x'' data vector | |||
:;<var>y</var>: y data vector: <code>''y''[i] = f(''x''[i] {, ''x''[i]^2, .., ''x''[i]^''m''})</code> | |||
:;<var>m</var>: the regression (polynom) order; m>0 (default=1) | |||
;Result 1: Approximate the function ''y'' using polynominal regression. The result is the vector ''r'' with the ''m''+1 coefficients of the regression polynom. | |||
::<code>''y''<sub>REG</sub>[i] = ''r''[0] + ''r''[1]*''x''[i] {+ ''r''[2]*''x''[i]^2 + .. + ''r''[''m'']*''x''[i]^''m''}</code> | |||
---- | |||
;Usage 2: <code>rpolyreg(<var>x<sub>matrix</sub></var>, <var>y<sub>vector</sub></var> {, <var>m</var>})</code>: | |||
:;<var>x</var>: ''x'' data matrix, the regression order is the number of independent variables <code>''m''=ncol(''x'')</code> | |||
:;<var>y</var>: ''y'' data vector: <code>''y''[i] = f(''x''[i,*])</code> | |||
;Result 2: Approximate the function ''y'' using multivariant linear regression. The result is the vector ''r'' with the ''m''+1 regression coefficients. | |||
::<code>''y''<sub>REG</sub>[i] = ''r''[0] + ''r''[1]*''x''[i,0] + .. + ''r''[''m'']*''x''[i,''m''-1]</code> | |||
---- | |||
;See also: [[../rpoly|rpoly]], [[../interp|interp]], [[../rleqs|rleqs, cleqs]], [[../svd|svd]] | |||
[[../#Functions|<function list>]] | |||
Latest revision as of 11:38, 21 April 2011
Linear, multivariant and polynominal regression.
- Usage 1
rpolyreg(xvector, yvector {, m}):- x
- x data vector
- y
- y data vector:
y[i] = f(x[i] {, x[i]^2, .., x[i]^m}) - m
- the regression (polynom) order; m>0 (default=1)
- Result 1
- Approximate the function y using polynominal regression. The result is the vector r with the m+1 coefficients of the regression polynom.
yREG[i] = r[0] + r[1]*x[i] {+ r[2]*x[i]^2 + .. + r[m]*x[i]^m}
- Usage 2
rpolyreg(xmatrix, yvector {, m}):- x
- x data matrix, the regression order is the number of independent variables
m=ncol(x) - y
- y data vector:
y[i] = f(x[i,*])
- Result 2
- Approximate the function y using multivariant linear regression. The result is the vector r with the m+1 regression coefficients.
yREG[i] = r[0] + r[1]*x[i,0] + .. + r[m]*x[i,m-1]
- See also
- rpoly, interp, rleqs, cleqs, svd
