Evaluation measures when complete data is available

Calculates measures of comparison based on distances between two configurations in two dimensions.

Usage

evalMeas(missbp, compdat = NULL)

Arguments

missbp: An object of class missbp obtained from preceding function missmi().
compdat: Complete data matrix representing the input data of missmi()

Value

The missbp object is appended with the following objects:

eval: Returns a data table with five evaluation measures: Procrustes Statistic (PS), Similarity Proportion (SP), Response Profile Recovery (RPR), Absolute Mean Bias (AMB), Root Mean Squared Bias (RMSB)
GPApred: A dataframe representing predicted categorical responses from the GPAbin biplot.
compPred: A dataframe representing predicted categorical responses from the complete MCA biplot.
compZs: Sample coordinates for the MCA biplot of the complete data.
compCLPs: CLPs for the MCA biplot of the complete data.
complvls: Names of the CLPs for the MCA biplot of the complete data.

See also missmi, impute, DRT and GPAbin.

For more detail, refer to Nienkemper-Swanepoel, J., le Roux, N. J., & Gardner-Lubbe, S. (2021). GPAbin: unifying visualizations of multiple imputations for missing values. Communications in Statistics - Simulation and Computation, 52(6), 2666–2685. https://doi.org/10.1080/03610918.2021.1914089.

Examples

# \donttest{
data(compdat)
data(implist)
missbp <- missmi(implist) |> DRT() |> GPAbin() |> evalMeas(compdat=compdat)# }