search for: curfit

...uot;percur", iopt = as.integer(iopt), m = as.integer(m), x = as.single(x), y = as.single(y), w = as.single(w), k = k, s = as.single(s), nest = as.integer(nest), n = as.integer(n), knots = Knots, coef = coef, fp = single(1), wrk = wrk, lwrk = lwrk, iwrk = iwrk, ier = integer(1)) 2: curfitSS(xyw, s = s, knots = knots, n = n, from = From, to = To, k = k, periodic = periodic, ...) 3: curfit.default(x, periodic = TRUE, ...) 4: curfit(x, periodic = TRUE, ...) 5: percur(x, y) Possible actions: 1: abort (with core dump, if enabled) 2: normal R exit 3: exit R without saving workspace...

Suppose I have two variables, x and y. For a fixed number of knots, I want to create a spline transformation of x such that a loss function is minimized. Presumably, this loss function would be least squares, i.e. sum (f(x)-y)^2. The spline transformations would be linear, quadratic or cubic. I know I can solve this problem using some optimization function in R, but I was wondering if anyone