Evaluation measures when complete data is available

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

Usage

evaluation(missbp, compdat = NULL)

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.tab

Returns a table with five evaluation measures: 1-Procrustes Statistic (1-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.

Interpretation GPAbin preserves the distances of the original biplot when large values (close to 1) are obtained for 1-PS, SP and RPR and small values for AMB and RMSB relative to other bias measures.

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.

See also

missmi, impute, DRT, GPAbin

Examples

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