Multiple imputation to fill in missing data in soil physico-hydrical properties database (Revista Ciência Agronômica, 2020)
Missing values in databases is a common issue and almost inevitable. Multiple imputation (MI) is an efficientstatistical method for estimating missing values in an incomplete dataset. To test this approach for a soil database, wehypothesized that the imputation of missing data provides a statistically more accurate database than the complete case analysis(CCA). The overall goal of our study was to evaluate the efficiency of the MI using the MICE (Multivariate Imputation byChained Equations) algorithm to fill in missing data in a database of soil physico-hydrical properties, and to show that it is morefeasible to perform the imputation than the CCA. Preliminary analyses were performed to check the suitability of the proposedalgorithm. Imputation of the missing data of each variable was adjusted using linear regression models. The variables withmissing data comprise the model as the dependent variable and the other variables, which were correlated with the same, enteras covariates. The analysis was performed by comparing the values of the estimates, their standard errors and 95% confidenceintervals. The pattern missing was multivariate and arbitrary and, organic matter was the variable with the largest amount ofmissing data. The significance of the covariates varied depending on the variable to be estimated. The results showed that theMICE presented better performance than CCA, since, although the statistical comparison of the two methods was similar,multiple imputation maintains the size of the database and preserves the general distribution.