# Predict for new data Return predicted values for newX based on training results of stacking. ```r stacking_predict(newX, stacking_train_result) ``` ## Arguments - `newX`: An N x P matrix of explanatory variables of new data where N is the number of samples and P is the number of explanatory variables. Note that the order of explanatory variables should be the same as those for training. Column names of newX are ignored. - `stacking_train_result`: A list output by stacking_train. When train_basemodel and train_metamodel are directly used, a list combining each output should be created and given as stacking_train_result. See examples for this operation. ## Details Prediction processes of this package are as follows. First, newX is given to all base models. As a result, each base learner returns Nfold predicted values where Nfold is an argument of stacking_train. Then the predicted values are averaged for each learner. Giving these averaged values as the explanatory variables of the meta model, final predicted values are output. ## Returns - **result**: Vector of predicted values. When TrainEachFold of stacking_train or train_metamodel is TRUE (i.e., stacking_train_result$meta$TrainEachFold is TRUE), the values are the averages of the values predicted from the meta models trained for each cross-validation fold. In the case of classification, the probabilities of each category are returned. ## Author(s) Taichi Nukui, Akio Onogi ## Examples ```r #Create a toy example ##Number of training samples N1 <- 100 ##Number of explanatory variables P <- 200 ##Create X of training data X1 <- matrix(rnorm(N1 * P), nrow = N1, ncol = P) colnames(X1) <- 1:P#column names are required by caret ##Assume that the first 10 variables have effects on Y ##Then add noise with rnorm Y1 <- rowSums(X1[, 1:10]) + rnorm(N1) ##Test data N2 <- 100 X2 <- matrix(rnorm(N2 * P), nrow = N2, ncol = P) colnames(X2) <- 1:P#Ignored (not required) Y2 <- rowSums(X2[, 1:10]) #Specify base learners Method <- list(glmnet = data.frame(alpha = c(0.5, 0.8), lambda = c(0.1, 1)), pls = data.frame(ncomp = 5)) #=>This specifies 5 base learners. ##1. glmnet with alpha = 0.5 and lambda = 0.1 ##2. glmnet with alpha = 0.5 and lambda = 1 ##3. glmnet with alpha = 0.8 and lambda = 0.1 ##4. glmnet with alpha = 0.8 and lambda = 1 ##5. pls with ncomp = 5 #The followings are the training and prediction processes #If glmnet and pls are not installed, please install them in advance. #Please remove #s before execution #Training #stacking_train_result <- stacking_train(X = X1, # Y = Y1, # Nfold = 5, # Method = Method, # Metamodel = "lm", # core = 2) #Prediction #result <- stacking_predict(newX = X2, stacking_train_result) #plot(Y2, result) #Training using train_basemodel and train_metamodel #base <- train_basemodel(X = X1, Y = Y1, Nfold = 5, Method = Method, core = 3) #meta <- train_metamodel(base, which_to_use = 1:5, Metamodel = "lm") #stacking_train_result <- list(base = base, meta = meta) #=>this list should have elements named as base and meta #Prediction #result <- stacking_predict(newX = X2, stacking_train_result) #plot(Y2, result) ```

stacking_predict function