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m_y_2
2009 May 14
1
automated polynomial regression
...ession.
The data might look like follows:
> true_y <- c(1:50)*.8
> # the real values
> m_y <- c((1:21)*1.1, 21.1, 22.2, 23.3 ,c(25:50)*.9)/0.3-5.2
> # the measured data
> x <- c(1:50)
> # and the x-axes
>
> # Now I do the following:
>
> m_y_2 <- m_y^2
> m_y_3 <- m_y^3
> mylm <- lm(true_y ~ m_y + m_y_2 + m_y_3) ; mylm
Call:
lm(formula = true_y ~ m_y + m_y_2 + m_y_3)
Coefficients:
(Intercept) m_y m_y_2 m_y_3
1.646e+00 1.252e-01 2.340e-03 -9.638e-06
Now I can get the real result with
> calibration <- 1.646...