completecases
Complete cases are observations in a dataset for which all variables of interest are observed (i.e., have no missing values). In statistical practice, analyses often proceed with complete-case analysis, also known as listwise deletion, where any record with missing data is excluded from the analysis. The term is commonly encountered in discussions of missing data strategies and in the R function complete.cases, which identifies complete records.
In R, complete.cases returns a logical vector indicating which rows are complete. If applied to a data
Advantages of complete-case analysis include simplicity and use of only observed data, avoiding imputation biases and
Alternatives to complete-case analysis include multiple imputation and maximum likelihood methods (such as FIML), which use