Piecewise Polynomial Fit
Piecewise linear or cubic fitting.
ppfit(x, y, xi, method = c("linear", "cubic"))x, y |
x-, y-coordinates of given points. |
xi |
x-coordinates of the choosen support nodes. |
method |
interpolation method, can be ‘constant’, ‘linear’, or ‘cubic’ (i.e., ‘spline’). |
ppfit fits a piece-wise polynomial to the input independent and
dependent variables,x and y, respectively. A weighted linear
least squares solution is provided. The weighting vector w must be
of the same size as the input variables.
Returns a pp (i.e., piecewise polynomial) structure.
Following an idea of Copyright (c) 2012 Ben Abbott, Martin Helm for Octave.
x <- 0:39
y <- c( 8.8500, 32.0775, 74.7375, 107.6775, 132.0975, 156.6675,
169.0650, 187.5375, 202.2575, 198.0750, 225.9600, 204.3550,
233.8125, 204.5925, 232.3625, 204.7550, 220.1925, 199.5875,
197.3025, 175.3050, 218.6325, 163.0775, 170.6625, 148.2850,
154.5950, 135.4050, 138.8600, 125.6750, 118.8450, 99.2675,
129.1675, 91.1925, 89.7000, 76.8825, 83.6625, 74.1950,
73.9125, 55.8750, 59.8675, 48.1900)
xi <- linspace(0, 39, 8)
pplin <- ppfit(x, y, xi) # method = "linear"
ppcub <- ppfit(x, y, xi, method = "cubic")
## Not run:
plot(x, y, type = "b", main = "Piecewise polynomial approximation")
xs <- linspace(0, 39, 100)
yslin <- ppval(pplin, xs)
yscub <- ppval(ppcub, xs)
lines(xs, yscub, col="red",lwd = 2)
lines(xs, yslin, col="blue")
grid()
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.