MOESM1 of Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study

Autor: Kokla, Marietta, Virtanen, Jyrki, Kolehmainen, Marjukka, Paananen, Jussi, Hanhineva, Kati
Rok vydání: 2019
DOI: 10.6084/m9.figshare.9969011.v1
Popis: Additional file 1. Contains descriptive statistics measures such as boxplots and correlation plots for the simulated sub-datasets, two summary tables that illustrate the average NRMSEs for all four proportion of missing values for every imputation method and for every type of missingness and one plot illustrating the computational times for every imputation method in every proposition of missing values.
Databáze: OpenAIRE