# Prediction from fitted CLR model(s) Takes a fitted `clr` object produced by `clr()` and produces predictions given a new set of functions or the original values used for the model fit. ```r ## S3 method for class 'clr' predict(object, newX = NULL, newclust = NULL, newXmean = NULL, simplify = FALSE, ...) ``` ## Arguments - `object`: A fitted `clr` object produced by `clr()`. - `newX`: An object of class `clrdata` or a matrix with one function a row. If this is not provided then predictions corresponding to the original data are returned. If `newX` is provided then it should contain the same type of functions as the original ones (same dimension, same clusters eventually, ...). - `newclust`: A new list of indices to obtain (approximately) homogeneous dependence structure inside each cluster of functions. - `newXmean`: To complete when done - `simplify`: If TRUE, one matrix of predicted functions is returned instead of a list of matrices (one matrix by cluster). In the final matrix, rows are sorted by increasing row numbers. - `...`: Further arguments are ignored. ## Returns predicted functions ## Examples ```r library(clr) data(gb_load) clr_load <- clrdata(x = gb_load$ENGLAND_WALES_DEMAND, order_by = gb_load$TIMESTAMP, support_grid = 1:48) # data cleaning: replace zeros with NA clr_load[rowSums((clr_load == 0) * 1) > 0, ] <- NA Y <- clr_load[2:nrow(clr_load), ] X <- clr_load[1:(nrow(clr_load) - 1), ] begin_pred <- which(substr(rownames(Y), 1, 4) == '2016')[1] Y_train <- Y[1:(begin_pred - 1), ] X_train <- X[1:(begin_pred - 1), ] Y_test <- Y[begin_pred:nrow(Y), ] X_test <- X[begin_pred:nrow(X), ] ## Example without any cluster model <- clr(Y = Y_train, X = X_train) pred_on_train <- predict(model) head(pred_on_train[[1]]) pred_on_test <- predict(model, newX = X_test) head(pred_on_test[[1]]) ## Example with clusters model <- clr(Y = Y_train, X = X_train, clust = clust_train) pred_on_train <- predict(model) str(pred_on_train) head(pred_on_train[[1]]) pred_on_test <- predict(model, newX = X_test, newclust = clust_test, simplify = TRUE) str(pred_on_test) head(pred_on_test) # With dates as row names rownames(pred_on_test) <- rownames(Y_test)[as.numeric(rownames(pred_on_test))] ```

predict.clr function