# Prediction Function for Hierarchical Spatial Finlay-Wilkinson Model This function considers spatial adjustments. ```r HSFWM_pred(Y, VAR, ENV, COOR, VAR2, ENV2, COOR2, save_int = FALSE, kin_info = FALSE, A = NULL, inits = NULL, hyper_para = NULL, M_iter = 5000, burn_in = 3000, thin = 5, seed = NULL) ``` ## Arguments - `Y`: A length-N1 numerical response vector from training set - `VAR`: A length-N1 factor/character vector indicating the genotype information of Y - `ENV`: A length-N1 factor/character vector indicating the field information of Y - `COOR`: A N1 by 2 numerical matrix indicating the spatial locations of Y - `VAR2`: A length-N2 factor/character vector indicating the genotype information of testing set - `ENV2`: A length-N2 factor/character vector indicating the field information of of testing set - `COOR2`: A N2 by 2 numerical matrix indicating the spatial locations of testing set - `save_int`: A logical parameter controling whether to save prediction credible intervals - `kin_info`: A logical parameter controling if to use kinship matrix - `A`: kinship matrix, give value only if kin_info = TRUE - `inits`: initial values, default is given - `hyper_para`: hyper-parameter values, default is given - `M_iter`: Total iteration number - `burn_in`: Burn in number - `thin`: Thinning value - `seed`: Random seed value ## Returns Mean prediction values and/or prediction intervals ## Examples ```r library(spFW) # load and split data data(spFW_example_data) idx_pred <- sample(125, 25) Y0 <- spFW_example_data$yield VAR0 <- spFW_example_data$geno ENV0 <- spFW_example_data$loc COOR0 <- spFW_example_data[,c(4,5)] Y1 <- Y0[-idx_pred] Y2 <- Y0[idx_pred] VAR1 <- VAR0[-idx_pred] VAR2 <- VAR0[idx_pred] ENV1 <- ENV0[-idx_pred] ENV2 <- ENV0[idx_pred] COOR1 <- COOR0[-idx_pred,] COOR2 <- COOR0[idx_pred,] order_y <- order(Y2) # run model pred1 <- HSFWM_pred(Y1, VAR1, ENV1, COOR1, VAR2, ENV2, COOR2, save_int = TRUE, M_iter = 1000, burn_in = 500, thin = 5) # visualize prediction results plot(1:25, pred1$PY[order_y], ylim = c(50, 250), pch = 15, col = "red", xlab = "Plant ID for Prediction", ylab = "Yield", main = "95% Prediction Intervals with Predicted Mean (Red) Versus True Yield (Blue)") points(1:25, Y2[order_y], col = "blue") for (i in 1:25){ lines(x = c(i,i), y = c(pred1$PY_CI[,order_y][1,i], pred1$PY_CI[,order_y][4,i])) } ```

HSFWM_pred function