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I would like to fit my data using spline(y~x) but all of the examples that I can find use a spline with smoothing, e.g. lm(y~ns(x), df=_).
I want to use spline() specifically because I am using this to do the analysis represented by the plot that I am making.
Is there a simple way to use spline() in ggplot?
I have considered the hackish approach of fitting a line using
geom_smooth(aes(x=(spline(y~x)$x, y=spline(y~x)$y))
but I would prefer not to have to resort to this.
Thanks!
748
Splines provide a way to smoothly interpolate between fixed points, called knots. Polynomial regression is computed between knots. In other words, splines are series of polynomial segments strung together, joining at knots. In case of spline regression we divide datasets into bins.
Key R function: geom_smooth() for adding smoothed conditional means / regression line. Key arguments: color , size and linetype : Change the line color, size and type. fill : Change the fill color of the confidence region.
A spline is a continuous function which coincides with a polynomial on every subinterval of the whole interval on which is defined. In other words, splines are functions which are piecewise polynomial. The coefficients of the polynomial differs from interval to interval, but the order of the polynomial is the same.
is this what you want?
n <- 10
d <- data.frame(x = 1:n, y = rnorm(n))
ggplot(d,aes(x,y)) + geom_point() +
geom_line(data=data.frame(spline(d, n=n*10)))
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