R10177/estimate_rmse_crossval.R
e0c09ada6d27master

estimate_rmse_crossval.R
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			estimate_rmse_crossval <- function(preprocessed, control_group, treatment_group,
			N_list, N_metadata,
			folds, f) {
			## Description: computes the RMSE in the context of a parallelizable cross-validation scheme

			#-Paras: -preprocessed: data produced by preprocess_E_N_for_activities()
			#-control_group: vector containing the names of the control samples (column names in preprocessed$E_centered)
			#-treatment_group: vector containing the sames of the treatment samples (column names in preprocessed$E_centered)
			#-N_list: list of the different N matrices. Rows must already correspond to those of preprocessed$E_centered
			#-N_metadata: dataframe with a number of rows equal to the number of elements in N_list
			#-folds: vector of the same length as the number of observations in E_centered, with integers from 1:nFolds, each defining a fold
			#-f: fold index for which to estimate the RMSE

			idx_training = folds==f
			idx_test = !idx_training
			# initializing for temporary results
			rmse = N_metadata
			rmse$fold = as.integer(0)
			rmse$rmse = 0
			rmse['fold'] = f
			for(i in 1:nrow(N_metadata)) {
			rmse[[i, 'rmse']] = estimateRMSE(preprocessed$E_centered, N=N_list[[i]], treatment_group = treatment_group, control_group=control_group, idx_training = idx_training, idx_test = idx_test)
			}
			return(rmse)
			}