# WinVector/RcppDynProg

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 #' xlin_fits_R #' #' Calculate out of sample linear fit predictions. #' #' @param x NumericVector, x-coords of values to group (length>=2). #' @param y NumericVector, values to group in order. #' @param w NumericVector, weights (positive). #' @return vector of predictions. #' #' @keywords internal #' #' @examples #' #' xlin_fits_V(c(1, 2, 3, 4), c(1, 2, 2, 1), c(1, 1, 1, 1)) #' #' @export #' xlin_fits_V <- function(x, y, w) { n = length(y) # build fitting data regularization = 1.0e-5 xx_0_0 = numeric(n) + sum(w*1) xx_1_0 = numeric(n) + sum(w*x) xx_0_1 = numeric(n) + sum(w*x) xx_1_1 = numeric(n) + sum(w*x*x) xy_0 = numeric(n) + sum(w*y) xy_1 = numeric(n) + sum(w*x*y) xx_1_0 = xx_1_0 + regularization xx_0_1 = xx_0_1 + regularization # pull out k'th observation xxk_0_0 = xx_0_0 - w*1 xxk_1_0 = xx_1_0 - w*x xxk_0_1 = xx_0_1 - w*x xxk_1_1 = xx_1_1 - w*x*x xyk_0 = xy_0 - w*y xyk_1 = xy_1 - w*x*y # solve linear system det = xxk_0_0*xxk_1_1 - xxk_0_1*xxk_1_0 c0 = (xxk_1_1*xyk_0 - xxk_0_1*xyk_1)/det c1 = (-xxk_1_0*xyk_0 + xxk_0_0*xyk_1)/det # form estimate y_est = c0 + c1*x return(y_est) }