Imputation methods for mixed datasets in bioarchaeology.

ABSTRACT: Missing data is a prevalent problem in bioarchaeological research and imputation could provide a promising solution. This work simulated missingness on a control dataset (481 samples × 41 variables) in order to explore imputation methods for mixed data (qualitative and quantitative data). The tested methods included Random Forest (RF), PCA/MCA, factorial analysis for mixed data (FAMD), hotdeck, predictive mean matching (PMM), random samples from observed values (RSOV), and a multi-method (MM) approach for the three missingness mechanisms (MCAR, MAR, and MNAR) at levels of 5%, 10%, 20%, 30%, and 40% missingness. This study also compared single imputation with an adapted multiple imputation method derived from the R package "mice". The results showed that the adapted multiple imputation tech

SUBMITTER: Ryan-Despraz J 

PROVIDER: S-EPMC11496361 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature