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2009 May 14
1
automated polynomial regression
...et with polynomial regression. 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 >...