Type: Package
Title: Two-Table ExPosition
Version: 2.9.0
Date: 2025-03-30
Description: An extension of ExPosition for two table analyses, specifically, discriminant analyses.
License: GPL-2
Encoding: UTF-8
Depends: prettyGraphs (≥ 2.2.0), ExPosition (≥ 2.11.0)
Packaged: 2025-04-12 18:08:29 UTC; Derek
BugReports: https://github.com/derekbeaton/ExPosition1/issues
RoxygenNote: 7.3.2
NeedsCompilation: no
Author: Derek Beaton [aut, cre], Jenny Rieck [aut], Cherise R. Chin Fatt [aut], Ju-Chi Yu [ctb], Luke Moraglia [ctb], Herve Abdi [aut]
Maintainer: Derek Beaton <exposition.software@gmail.com>
Repository: CRAN
Date/Publication: 2025-04-15 04:20:02 UTC

TExPosition: Two-table analyses via ExPosition.

Description

TExPosition is two-table ExPosition and includes discriminant methods of the singular value decomposition (SVD). The core of TExPosition is ExPosition and the svd.

Author(s)

Questions, comments, compliments, and complaints go to Derek Beaton exposition.software@gmail.com.

The following people are authors or contributors to TExPosition code, data, or examples:
Derek Beaton, Jenny Rieck, Cherise Chin-Fatt, Francesca Filbey, Ju-Chi Yu, Luke Moraglia, and Hervé Abdi.

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.
Abdi, H. and Williams, L.J. (2010). Correspondence analysis. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 267-278.
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.
Abdi, H. & Williams, L.J. (2010). Barycentric discriminant analysis (BADIA). In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 64-75.
Abdi, H. (2007). Discriminant correspondence analysis. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 270-275. Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. NeuroImage, 56(2), 455 – 475.
McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage, 23, S250–S263.

See Also

tepBADA, tepPLS, tepDICA, tepPLSCA

R-squared computations

Description

A function to compute R-squared for BADA and DICA

Usage

R2(group.masses, di, ind.masses = NULL, dii)

Arguments

group.masses

a masses matrix for the groups

di

a set of squared distances of the groups

ind.masses

a masses matrix for the individuals

dii

a set of squared distances for the individuals

Value

R2

An R-squared

Author(s)

Jenny Rieck, Derek Beaton

calculateLVConstraints

Description

Calculates constraints for plotting latent variables.

Usage

calculateLVConstraints(results, x_axis = 1, y_axis = 2, constraints = NULL)

Arguments

results

results (with $lx and $ly) from TExPosition (i.e., $TExPosition.Data)

x_axis

which component should be on the x axis?

y_axis

which component should be on the y axis?

constraints

if available, axis constraints for the plots (determines end points of the plots).

Value

Returns a list with the following items:

$constraints

axis constraints for the plots (determines end points of the plots).

Author(s)

Derek Beaton

fastEucCalc

Description

Fast Euclidean distance calculations.

Usage

fastEucCalc(x, c)

Arguments

x

a set of points.

c

a set of centers.

Details

This function is especially useful for discriminant analyses. The distance from each point in x to each point in c is computed and returned as a nrow(x) x nrow(c) matrix.

Value

a distance matrix

Euclidean distances of each point to each center are returned.

Author(s)

Hervé Abdi, Derek Beaton

fii2fi: individuals to centers

Description

All computations between individual factor scores (fii) and group factor scores (fi).

Usage

fii2fi(DESIGN, fii, fi)

Arguments

DESIGN

a dummy-coded design matrix

fii

a set of factor scores for individuals (rows)

fi

a set of factor scores for rows

Value

A list of values containing:

distances

Euclidean distances of all rows to each category center

assignments

an assignment matrix (similar to DESIGN) where each individual is assigned to the closest category center

confusion

a confusion matrix of how many items are assigned (and mis-assigned) to each category

Author(s)

Hervé Abdi, Derek Beaton

Print assignment results

Description

Print assignment results.

Usage

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

Arguments

x

an list that contains items to make into the tepAssign class.

...

inherited/passed arguments for S3 print method(s).

Author(s)

Derek Beaton, Cherise Chin-Fatt

Print tepBADA results

Description

Print tepBADA results.

Usage

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

Arguments

x

an list that contains items to make into the tepBADA class.

...

inherited/passed arguments for S3 print method(s).

Author(s)

Derek Beaton, Cherise Chin-Fatt

Print tepDICA results

Description

Print tepDICA results.

Usage

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

Arguments

x

an list that contains items to make into the tepDICA class.

...

inherited/passed arguments for S3 print method(s).

Author(s)

Derek Beaton, Cherise Chin-Fatt

Print tepGraphs results

Description

Print tepGraphs results.

Usage

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

Arguments

x

an list that contains items to make into the tepGraphs class.

...

inherited/passed arguments for S3 print method(s).

Author(s)

Derek Beaton, Cherise Chin-Fatt

Print tepPLS results

Description

Print tepPLS results.

Usage

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

Arguments

x

an list that contains items to make into the tepPLS class.

...

inherited/passed arguments for S3 print method(s).

Author(s)

Derek Beaton, Cherise Chin-Fatt

Print tepPLSCA results

Description

Print tepPLSCA results.

Usage

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

Arguments

x

an list that contains items to make into the tepPLSCA class.

...

inherited/passed arguments for S3 print method(s).

Author(s)

Derek Beaton, Cherise Chin-Fatt

Print TExPosition results

Description

Print TExPosition results.

Usage

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

Arguments

x

an list that contains items to make into the texpoOutput class.

...

inherited/passed arguments for S3 print method(s).

Author(s)

Derek Beaton, Cherise Chin-Fatt

Barycentric Discriminant Analysis

Description

Barycentric Discriminant Analysis (BADA) via TExPosition.

Usage

tepBADA(
  DATA,
  scale = TRUE,
  center = TRUE,
  DESIGN = NULL,
  make_design_nominal = TRUE,
  graphs = TRUE,
  k = 0
)

Arguments

DATA

original data to perform a BADA on.

scale

a boolean, vector, or string. See expo.scale for details.

center

a boolean, vector, or string. See expo.scale for details.

DESIGN

a design matrix to indicate if rows belong to groups. Required for BADA.

make_design_nominal

a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.

graphs

a boolean. If TRUE (default), graphs and plots are provided (via tepGraphs)

k

number of components to return.

Details

Note: BADA is a special case of PLS (tepPLS) wherein DATA1 are data and DATA2 are a group-coded disjunctive matrix. This is also called mean-centered PLS (Krishnan et al., 2011).

Value

See corePCA for details on what is returned. In addition to the values returned:

fii

factor scores computed for supplemental observations

dii

squared distances for supplemental observations

rii

cosines for supplemental observations

assign

a list of assignment data. See fii2fi and R2

lx

latent variables from DATA1 computed for observations

ly

latent variables from DATA2 computed for observations

Author(s)

Derek Beaton

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.
Abdi, H. and Williams, L.J. (2010). Correspondence analysis. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 267-278.
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.
Abdi, H. & Williams, L.J. (2010). Barycentric discriminant analysis (BADIA). In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 64-75.
Abdi, H., Williams, L.J., Beaton, D., Posamentier, M., Harris, T.S., Krishnan, A., & Devous, M.D. (in press, 2012). Analysis of regional cerebral blood flow data to discriminate among Alzheimer's disease, fronto-temporal dementia, and elderly controls: A multi-block barycentric discriminant analysis (MUBADA) methodology. Journal of Alzheimer Disease, , -. Abdi, H., Williams, L.J., Connolly, A.C., Gobbini, M.I., Dunlop, J.P., & Haxby, J.V. (2012). Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to assign scans to categories without using spatial normalization. Computational and Mathematical Methods in Medicine, 2012, 1-15. doi:10.1155/2012/634165.
Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. NeuroImage, 56(2), 455 – 475.

See Also

corePCA, epPCA, epMDS

Examples


data(bada.wine)
bada.res <- tepBADA(bada.wine$data,scale=FALSE,DESIGN=bada.wine$design,make_design_nominal=FALSE)

Discriminant Correspondence Analysis

Description

Discriminant Correspondence Analysis (DICA) via TExPosition.

Usage

tepDICA(
  DATA,
  make_data_nominal = FALSE,
  DESIGN = NULL,
  make_design_nominal = TRUE,
  symmetric = TRUE,
  graphs = TRUE,
  k = 0
)

Arguments

DATA

original data to perform a DICA on. Data can be contingency (like CA) or categorical (like MCA).

make_data_nominal

a boolean. If TRUE (default), DATA is recoded as a dummy-coded matrix. If FALSE, DATA is a dummy-coded matrix.

DESIGN

a design matrix to indicate if rows belong to groups. Required for DICA.

make_design_nominal

a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.

symmetric

a boolean. If TRUE (default) symmetric factor scores for rows.

graphs

a boolean. If TRUE (default), graphs and plots are provided (via tepGraphs)

k

number of components to return.

Details

If you use Hellinger distance, it is best to set symmetric to FALSE.

Note: DICA is a special case of PLS-CA (tepPLSCA) wherein DATA1 are data and DATA2 are a group-coded disjunctive matrix.

Value

See epCA (and also coreCA) for details on what is returned. In addition to the values returned:

fii

factor scores computed for supplemental observations

dii

squared distances for supplemental observations

rii

cosines for supplemental observations

assign

a list of assignment data. See fii2fi and R2

lx

latent variables from DATA1 computed for observations

ly

latent variables from DATA2 computed for observations

Author(s)

Derek Beaton, Hervé Abdi

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.
Abdi, H. and Williams, L.J. (2010). Correspondence analysis. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 267-278.
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.
Abdi, H. (2007). Discriminant correspondence analysis. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 270-275.
Pinkham, A.E., Sasson, N.J., Beaton, D., Abdi, H., Kohler, C.G., Penn, D.L. (in press, 2012). Qualitatively distinct factors contribute to elevated rates of paranoia in autism and schizophrenia. Journal of Abnormal Psychology, 121, -.
Williams, L.J., Abdi, H., French, R., & Orange, J.B. (2010). A tutorial on Multi-Block Discriminant Correspondence Analysis (MUDICA): A new method for analyzing discourse data from clinical populations. Journal of Speech Language and Hearing Research, 53, 1372-1393.
Williams, L.J., Dunlop, J.P., & Abdi, H. (2012). Effect of age on the variability in the production of text-based global inferences. PLoS One, 7(5): e36161. doi:10.1371/ journal.pone.0036161 (pp.1-9)

See Also

coreCA, epCA, epMCA

Examples


data(dica.wine)
dica.res <- tepDICA(dica.wine$data,DESIGN=dica.wine$design,make_design_nominal=FALSE)

tepGraphs: TExPosition plotting function

Description

TExPosition plotting function which is an interface to prettyGraphs.

Usage

tepGraphs(
  res,
  x_axis = 1,
  y_axis = 2,
  tepPlotInfo = NULL,
  DESIGN = NULL,
  fi.col = NULL,
  fi.pch = NULL,
  fii.col = NULL,
  fii.pch = NULL,
  fj.col = NULL,
  fj.pch = NULL,
  col.offset = NULL,
  constraints = NULL,
  lv.constraints = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  lvPlots = TRUE,
  lvAgainst = TRUE,
  contributionPlots = TRUE,
  correlationPlotter = TRUE,
  showHulls = 1,
  graphs = TRUE
)

Arguments

res

results from TExPosition

x_axis

which component should be on the x axis?

y_axis

which component should be on the y axis?

tepPlotInfo

A list ($Plotting.Data) from tepGraphs or TExPosition.

DESIGN

A design matrix to apply colors (by pallete selection) to row items

fi.col

A matrix of colors for the group items. If NULL, colors will be selected.

fi.pch

A matrix of pch values for the group items. If NULL, pch values are all 21.

fii.col

A matrix of colors for the row items (observations). If NULL, colors will be selected.

fii.pch

A matrix of pch values for the row items (observations). If NULL, pch values are all 21.

fj.col

A matrix of colors for the column items. If NULL, colors will be selected.

fj.pch

A matrix of pch values for the column items. If NULL, pch values are all 21.

col.offset

A numeric offset value. Is passed to createColorVectorsByDesign.

constraints

Plot constraints as returned from prettyPlot. If NULL, constraints are selected.

lv.constraints

Plot constraints for latent variables. If NULL, constraints are selected.

xlab

x axis label

ylab

y axis label

main

main label for the graph window

lvPlots

a boolean. If TRUE, latent variables (X, Y) are plotted. If FALSE, latent variables are not plotted.

lvAgainst

a boolean. If TRUE, latent variables (X, Y) are plotted against each other. If FALSE, latent variables are plotted like factor scores.

contributionPlots

a boolean. If TRUE (default), contribution bar plots will be created.

correlationPlotter

a boolean. If TRUE (default), a correlation circle plot will be created. Applies to PCA family of methods (CA is excluded for now).

showHulls

a value between 0 and 1 to make a peeled hull at that percentage. All values outside of 0-1 will not plot any hulls.

graphs

a boolean. If TRUE, graphs are created. If FALSE, only data associated to plotting (e.g., constraints, colors) are returned.

Details

tepGraphs is an interface between TExPosition and prettyGraphs.

Value

The following items are bundled inside of $Plotting.Data:

$fii.col

the colors that are associated to the individuals (row items; $fii).

$fii.pch

the pch values associated to the individuals (row items; $fii).

$fi.col

the colors that are associated to the groups ($fi).

$fi.pch

the pch values associated to the groups ($fi).

$fj.col

the colors that are associated to the column items ($fj).

$fj.pch

the pch values associated to the column items ($fj).

$constraints

axis constraints for the plots (determines end points of the plots).

Author(s)

Derek Beaton

See Also

prettyGraphs

Examples


	#this is for TExPosition's iris data
	data(ep.iris)
	bada.iris <- tepBADA(ep.iris$data,DESIGN=ep.iris$design,
	  make_design_nominal=FALSE,graphs=FALSE)
	#there are only 2 components, not 3.		
	bada.iris.plotting.data <- tepGraphs(bada.iris,x_axis=1,y_axis=2)

Partial Least Squares

Description

Partial Least Squares (PLS) via TExPosition.

Usage

tepPLS(
  DATA1,
  DATA2,
  center1 = TRUE,
  scale1 = "SS1",
  center2 = TRUE,
  scale2 = "SS1",
  DESIGN = NULL,
  make_design_nominal = TRUE,
  graphs = TRUE,
  k = 0
)

Arguments

DATA1

Data matrix 1 (X)

DATA2

Data matrix 2 (Y)

center1

a boolean, vector, or string to center DATA1. See expo.scale for details.

scale1

a boolean, vector, or string to scale DATA1. See expo.scale for details.

center2

a boolean, vector, or string to center DATA2. See expo.scale for details.

scale2

a boolean, vector, or string to scale DATA2. See expo.scale for details.

DESIGN

a design matrix to indicate if rows belong to groups.

make_design_nominal

a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.

graphs

a boolean. If TRUE (default), graphs and plots are provided (via tepGraphs)

k

number of components to return.

Details

This implementation of Partial Least Squares is a symmetric analysis. It was first described by Tucker (1958), again by Bookstein (1994), and has gained notoriety in Neuroimaging from McIntosh et al., (1996).

Value

See corePCA for details on what is returned. In addition to the values returned:

lx

latent variables from DATA1 computed for observations

ly

latent variables from DATA2 computed for observations

data1.norm

center and scale information for DATA1

data1.norm

center and scale information for DATA2

Author(s)

Derek Beaton

References

Tucker, L. R. (1958). An inter-battery method of factor analysis. Psychometrika, 23(2), 111–136.
Bookstein, F., (1994). Partial least squares: a dose–response model for measurement in the behavioral and brain sciences. Psycoloquy 5 (23)
McIntosh, A. R., Bookstein, F. L., Haxby, J. V., & Grady, C. L. (1996). Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares. NeuroImage, 3(3), 143–157.

Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. NeuroImage, 56(2), 455 – 475.
McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage, 23, S250–S263.

See Also

corePCA, epPCA, tepBADA, tepPLSCA

Examples


data(beer.tasting.notes)
data1<-beer.tasting.notes$data[,1:8]
data2<-beer.tasting.notes$data[,9:16]
pls.res <- tepPLS(data1,data2)

Partial Least Squares-Correspondence Analysis

Description

Partial Least Squares-Correspondence Analysis (PLSCA) via TExPosition.

Usage

tepPLSCA(
  DATA1,
  DATA2,
  make_data1_nominal = FALSE,
  make_data2_nominal = FALSE,
  DESIGN = NULL,
  make_design_nominal = TRUE,
  symmetric = TRUE,
  graphs = TRUE,
  k = 0
)

Arguments

DATA1

Data matrix 1 (X), must be categorical (like MCA) or in disjunctive code see make_data1_nominal.

DATA2

Data matrix 2 (Y), must be categorical (like MCA) or in disjunctive code see make_data2_nominal.

make_data1_nominal

a boolean. If TRUE (default), DATA1 is recoded as a dummy-coded matrix. If FALSE, DATA1 is a dummy-coded matrix.

make_data2_nominal

a boolean. If TRUE (default), DATA2 is recoded as a dummy-coded matrix. If FALSE, DATA2 is a dummy-coded matrix.

DESIGN

a design matrix to indicate if rows belong to groups.

make_design_nominal

a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.

symmetric

a boolean. If TRUE (default) symmetric factor scores for rows.

graphs

a boolean. If TRUE (default), graphs and plots are provided (via tepGraphs)

k

number of components to return.

Details

This implementation of Partial Least Squares is for two categorical data sets (Beaton et al., 2013), and based on the PLS method proposed by Tucker (1958) and again by Bookstein (1994).

Value

See epCA (and also coreCA) for details on what is returned. In addition to the values returned:

W1

Weights for columns of DATA1, replaces M from coreCA.

W2

Weights for columns of DATA2, replaces W from coreCA.

lx

latent variables from DATA1 computed for observations

ly

latent variables from DATA2 computed for observations

Author(s)

Derek Beaton, Hervé Abdi

References

Tucker, L. R. (1958). An inter-battery method of factor analysis. Psychometrika, 23(2), 111–136.
Bookstein, F., (1994). Partial least squares: a dose–response model for measurement in the behavioral and brain sciences. Psycoloquy 5 (23)
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.
Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. NeuroImage, 56(2), 455 – 475.
Beaton, D., Filbey, F., & Abdi H. (in press, 2013). Integrating partial least squares correlation and correspondence analysis for nominal data. In Abdi, H., Chin, W., Esposito Vinzi, V., Russolillo, G., & Trinchera, L. (Eds.), New Perspectives in Partial Least Squares and Related Methods. New York: Springer Verlag.

See Also

coreCA, epCA, epMCA, tepDICA

Examples


	data(snps.druguse)
	plsca.res <- tepPLSCA(snps.druguse$DATA1,snps.druguse$DATA2,
			make_data1_nominal=TRUE,make_data2_nominal=TRUE)

texpoDesignCheck

Description

TExPosition's DESIGN matrix check function. Calls into ExPosition's designCheck.

Usage

texpoDesignCheck(
  DATA = NULL,
  DESIGN = NULL,
  make_design_nominal = TRUE,
  force_bary = FALSE
)

Arguments

DATA

original data that should be matched to a design matrix

DESIGN

a column vector with levels for observations or a dummy-coded matrix

make_design_nominal

a boolean. Will make DESIGN nominal if TRUE (default).

force_bary

a boolean. If TRUE, it forces the check for barycentric methods (tepDICA, tepBADA). If FALSE, designCheck is performed.

Details

For BADA & DICA, execution stops if:
1. DESIGN has more columns (groups) than observations, 2. DESIGN has only 1 column (group), or 3. DESIGN has at least 1 occurence where an observation is the only observation in a group (i.e., colSums(DESIGN)==1 at least once).

Value

DESIGN

dummy-coded design matrix

Author(s)

Derek Beaton