Title: Unifying Multiple Biplot Visualisations into a Single Display
Version: 1.1.1
Description: Aligning multiple visualisations by utilising generalised orthogonal Procrustes analysis (GPA) before combining coordinates into a single biplot display as described in Nienkemper-Swanepoel, le Roux and Lubbe (2023)<doi:10.1080/03610918.2021.1914089>. This is mainly suitable to combine visualisations constructed from multiple imputations, however, it can be generalised to combine variations of visualisations from the same datasets (i.e. resamples).
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (≥ 4.1.0)
Imports: ca, jomo, mi, mice, missMDA, mitools, stringr
Suggests: testthat, knitr
Config/Needs/website: rmarkdown
BugReports: https://github.com/jnienk/GPAbin/issues
URL: https://jnienk.github.io/GPAbin/
NeedsCompilation: no
Packaged: 2025-10-02 14:50:48 UTC; nienkemperj
Author: Johané Nienkemper-Swanepoel ORCID iD [aut, cre, cph]
Maintainer: Johané Nienkemper-Swanepoel <nienkemperj@sun.ac.za>
Repository: CRAN
Date/Publication: 2025-10-02 16:50:08 UTC

Category level prediction

Description

Predicts category levels from an MCA based biplot using the distances between coordinates

Usage

CLPpred(CLPs = CLPs, Zs = Zs, p = p, n = n, lvls = lvls, datIN = datIN)

Arguments

CLPs

Category level point coordinates

Zs

Sample coordinates

p

Number of variables

n

Number of samples

lvls

Names of category levels

datIN

Input data from which CLPs and Zs are obtained

Value

predCL

Final predicted categorical data set

Dimension reduction

Description

Multiple correspondence analysis is performed on the multiple imputed datasets

Usage

DRT(missbp, method = c("MCA"))

Arguments

missbp

An object of class missbp obtained from preceding function missmi()

method

Select a dimension reduction technique. In the current version MCA is available.

Value

The missbp object is appended with the following objects:

Z

List of sample coordinates

CLP

List of category level point coordinates

lvls

List of category level names

m

Number of multiple imputations

See also missmi and impute.

Examples

data(implist)
missbp <- missmi(implist) |> DRT()

Generalised Orthogonal Procrustes Analysis

Description

This function contains the OPA function to compare two configurations and the GPA function for multiple configuration comparisons

Usage

GPA(Xk, G.target = NULL, iter = 500, eps = 0.001)

Arguments

Xk

list containing the testee configurations which is updated on #each iteration

G.target

Target configuration. If not specified the centroid configuration will be used as the target

iter

Number of iterations allowed before convergence

eps

Threshold value for convergence of the alogrithm

Value

Xk.F

List containing the updated testee configurations

sk.F

Vector containing the final scaling factors

Qk.F

List containing the final rotation matrices

Gmat

Final target configuration

sum.sq

Final minimised sum of squared distance

Function to unify coordinates of multiple configurations

Description

Combines multiple configurations from dimension reduction solutions applied to multiple imputed data sets

Usage

GPAbin(missbp, G.target = NULL)

Arguments

missbp

An object of class missmi obtained from preceding function missmi()

G.target

Target configuration. If not specified the centroid configuration will be used as the target.

Value

The missbp object is appended with the following objects:

Z.GPA.list

List containing the sample coordinates for each MI after GPA

CLP.GPA.list

List containing the CLPs for each MI after GPA

G.target

Target configuration

Z.GPAbin

Sample coordinates for the GPAbin biplot

CLP.GPAbin

CLPs for the GPAbin biplot

See also missmi, impute and DRT.

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

data(implist)
missbp <- missmi(implist) |> DRT() |> GPAbin()

Orthogonal Procrustes Analysis

Description

This function performs Orthogonal Procrustes Analysis on centred data

Usage

OPA(missbp, compdat, centring = TRUE, dim = "2D")

Arguments

missbp

An object of class missmi obtained from preceding function missmi()

compdat

Complete data set, only available for simulated data examples.

centring

Logical argument to apply centering, default is TRUE.

dim

Number of dimensions to use in final solutions (⁠2D⁠ or All available dimensions.)

Value

ProcStat

Procrustes Statistic

compZ

Sample coordinates representing the complete data set

compCLP

Category level point coordinates representing the complete data set

complvls

Category levels

compdat

Complete data set, only available for simulated data examples

Biplot function

Description

Creates a multiple correspondence analysis (MCA) biplot

Usage

biplFig(
  missbp,
  Z.col = "#61223b",
  CLP.col = "#b79962",
  Z.pch = 19,
  CLP.pch = 15,
  Z.cex = 1.5,
  CLP.cex = 1.7,
  title = ""
)

Arguments

missbp

An object of class missbp obtained from preceding function missmi()

Z.col

Colour of sample coordinates

CLP.col

Colour of category level point coordinates

Z.pch

Plotting character of sample coordinates

CLP.pch

Plotting character of category level point coordinates

Z.cex

Size of plotting character for sample points

CLP.cex

Size of plotting character for category level point points

title

Title of the plot

Value

Examples

data(implist)
missbp <- missmi(implist)|> DRT() |> GPAbin() |> biplFig()

Complete data example

Description

Simulated data example

Format

A data frame with 1000 rows and 5 columns.

Details

V1

Variable 1

V1

Variable 2

V1

Variable 3

V1

Variable 4

V1

Variable 5

Source

Simulated data from a uniform distribution that is categorised into levels.

Evaluation measures when complete data is available

Description

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


data(compdat)
data(implist)
missbp <- missmi(implist) |> DRT() |> GPAbin() |> evalMeas(compdat=compdat)

List of multiple imputed data sets

Description

Five multiple imputations of missdat

Format

List containing five multiple imputations of missdat. Each list item a data frame with 1000 rows and 5 columns.

Details

V1

Variable 1

V1

Variable 2

V1

Variable 3

V1

Variable 4

V1

Variable 5

Source

simulated example data imputed with mice::mice(missdat, m=5, method="polyreg", maxit=10, remove.collinear=FALSE, printFlag = FALSE)

Multiple imputation

Description

Choose between four available multiple imputation strategies in R.

Usage

impute(missbp, imp.method = c("MIMCA", "jomo", "DPMPM", "mice"), m = 5)

Arguments

missbp

An object of class missmi obtained from preceding function missmi().

imp.method

Select one of four imputation methods: MIMCA, jomo, DPMPM, mice

m

Number of multiple imputations

Value

The missbp object is appended with the following object:

dataimp

List of imputed data

See also MIMCA, jomo1cat and mi and mice.

Examples


data(missdat)
missbp <- missmi(missdat) |> impute(imp.method="DPMPM", m=5)

Missing data example

Description

compdat containing approximately 35% simulated missing values according to a missing at random (MAR) missing data mechanism

Format

A data frame with 1000 rows and 5 columns.

Details

V1

Variable 1

V1

Variable 2

V1

Variable 3

V1

Variable 4

V1

Variable 5

Source

Simulated data from a uniform distribution that is categorised into levels.

First step before constructing unified biplots

Description

This function produces a list of elements to be used when producing a GPAbin biplot.

Usage

missmi(data)

Arguments

data

input data frame or list

Value

X

The processed data

m

Number of multiple imputations applied

n

The number of samples

p

The number of variables

miss_pct

Percentage of missing values

Examples

data(missdat)
missbp <- missmi(missdat)
data(implist)
missbp <- missmi(implist)

Generic print function for objects of class missmi

Description

This function is used to print output when the missmi biplot object is created.

Usage

## S3 method for class 'missmi'
print(x, ...)

Arguments

x

an object of class missmi.

...

additional arguments.

Value

This function will not produce a return value, it is called for side effects.

Examples

data(missdat)
missbp <- missmi(missdat)
data(implist)
missbp <- missmi(implist)
print(missbp)