1d:T755,# Prediction for a scalar-on-function regression model This function is used to make prediction for a new set of functional and scalar (if any) predictors based upon a fitted scalar-on-function regression model in the output of `rob.sf.reg`. ```r predict_sf_regression(object, Xnew, Xnew.scl = NULL) ``` ## Arguments - `object`: An output object obtained from `rob.sf.reg`. - `Xnew`: A list of matrices consisting of the new observations of functional predictors. The argument `Xnew` must have the same length and the same structure as the input `X` of `rob.sf.reg`. - `Xnew.scl`: A matrix consisting of the new observations of scalar predictors. The argument `Xnew.scl` must have the same length and the same structure as the input `X.scl` of `rob.sf.reg`. ## Returns An $n_{test} \times 1$-dimensional matrix of predicted values of the scalar response variable for the given set of new functional and scalar (if any) predictors `Xnew` and `Xnew.scl`, respectively. Here, $n_{test}$, the number of rows of the matrix of predicted values, equals to the number of rows of `Xnew` and and `Xnew.scl` (if any). ## Author(s) Ufuk Beyaztas and Han Lin Shang ## Examples ```r set.seed(2022) sim.data <- generate.sf.data(n = 400, n.pred = 5, n.gp = 101, out.p = 0.1) out.indx <- sim.data$out.indx indx.test <- sample(c(1:400)[-out.indx], 120) indx.train <- c(1:400)[-indx.test] Y <- sim.data$Y X <- sim.data$X Y.train <- Y[indx.train,] Y.test <- Y[indx.test,] X.train <- X.test <- list() for(i in 1:5){ X.train[[i]] <- X[[i]][indx.train,] X.test[[i]] <- X[[i]][indx.test,] } gp <- rep(list(seq(0, 1, length.out = 101)), 5) # grid points of Xs model.tau <- rob.sf.reg(Y.train, X.train, emodel = "robust", fmodel = "tau", gp = gp) pred.tau <- predict_sf_regression(object = model.tau, Xnew = X.test) round(mean((Y.test - pred.tau)^2), 4) # 1.868 (tau method) ```

predict_sf_regression function