Title:	Create (Advanced) Coupled Matrix and Tensor Factorization Models
Version:	1.0.1
Description:	Creation and selection of (Advanced) Coupled Matrix and Tensor Factorization (ACMTF) and ACMTF-Regression (ACMTF-R) models. Selection of the optimal number of components can be done using 'ACMTF_modelSelection()' and 'ACMTFR_modelSelection()'. The CMTF and ACMTF methods were originally described by Acar et al., 2011 <doi:10.48550/arXiv.1105.3422> and Acar et al., 2014 <doi:10.1186/1471-2105-15-239>, respectively.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	clue, doParallel, dplyr, foreach, ggplot2, magrittr, methods, mize, multiway, parallel, pracma, rTensor, stats, tidyr
Suggests:	knitr, ggpubr, NPLStoolbox, plotrix, parafac4microbiome, rmarkdown, testthat (≥ 3.0.0), scales
Config/testthat/edition:	3
VignetteBuilder:	knitr
Depends:	R (≥ 2.10)
LazyData:	true
URL:	https://grvanderploeg.com/CMTFtoolbox/, https://github.com/GRvanderPloeg/CMTFtoolbox
BugReports:	https://github.com/GRvanderPloeg/CMTFtoolbox/issues
NeedsCompilation:	no
Packaged:	2025-08-19 08:11:02 UTC; roelv
Author:	Geert Roelof van der Ploeg [aut, cre], Johan Westerhuis [ctb], Anna Heintz-Buschart [ctb], Age Smilde [ctb], University of Amsterdam [cph, fnd]
Maintainer:	Geert Roelof van der Ploeg <g.r.ploeg@uva.nl>
Repository:	CRAN
Date/Publication:	2025-08-22 18:20:17 UTC

Model selection for ACMTFR

Description

Model selection for ACMTFR

Usage

ACMTFR_modelSelection(
  datasets,
  modes,
  Y,
  sharedMode = 1,
  maxNumComponents = 5,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  normalize = TRUE,
  normY = 1,
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 10000,
  abs_tol = 1e-10,
  rel_tol = 1e-10,
  grad_tol = 1e-10,
  nstart = 5,
  numCores = 1,
  cvFolds = 2
)

Arguments

Value

List object containing plots of all metrics and dataframes containing the data used to create them.

Examples

set.seed(123)

I = 10
J = 5
K = 3
df = array(rnorm(I*J*K), c(I,J,K))
df2 = array(rnorm(I*J*K), c(I,J,K))
datasets = list(df, df2)
modes = list(c(1,2,3), c(1,4,5))
Y = as.matrix(rnorm(I))

# A very small procedure is run to limit computational requirements
result = ACMTFR_modelSelection(datasets,
                              modes,
                              Y,
                              pi=1.0,
                              maxNumComponents=2,
                              nstart=2,
                              cvFolds=2,
                              rel_tol=0.5,
                              abs_tol=0.5)

result$plots$overview

Model selection for ACMTF

Description

Model selection for ACMTF

Usage

ACMTF_modelSelection(
  datasets,
  modes,
  maxNumComponents = 3,
  sharedMode = 1,
  alpha = 1,
  beta = rep(0.001, length(datasets)),
  epsilon = 1e-08,
  nstart = 10,
  cvFolds = 10,
  numCores = 1,
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 1e+05,
  rel_tol = 1e-08,
  abs_tol = 1e-08,
  grad_tol = 1e-08,
  plots = TRUE
)

Arguments

Value

List object containing variation explained, FMS, and degeneracy score metrics. If plots=TRUE, plots will be attached to the list.

Examples

set.seed(123)

I = 10
J = 5
K = 3
df = array(rnorm(I*J*K), c(I,J,K))
df2 = array(rnorm(I*J*K), c(I,J,K))
datasets = list(df, df2)
modes = list(c(1,2,3), c(1,4,5))

# A very small procedure is run to limit computational requirements
# Plots are not made to reduce CRAN runtime.
result = ACMTF_modelSelection(datasets,
                              modes,
                              maxNumComponents=1,
                              nstart=2,
                              cvFolds=2,
                              rel_tol=1e-1,
                              abs_tol=1e-1,
                              max_iter=2,
                              plots=FALSE)

Compute Factor Match Score for two models.

Description

Compute Factor Match Score for two models.

Usage

FMS_cv(Fac1, Fac2, sharedMode = 1)

Arguments

Value

Scalar of FMS value

Examples

set.seed(123)

I = 10
J = 5
K = 3
df = array(rnorm(I*J*K), c(I,J,K))
datasets = list(df, df)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes)

model1 = acmtf_opt(Z, 1)

Fac1 = model1$Fac[1:3]
Fac2 = Fac1 # identical models for the purposes of demonstration
result = FMS_cv(Fac1, Fac2) # [1] 1

Compute Factor Match Score for two models.

Description

Compute Factor Match Score for two models.

Usage

FMS_random(Fac1, Fac2)

Arguments

Value

Scalar of FMS value

Examples

set.seed(123)

I = 10
J = 5
K = 3
df = array(rnorm(I*J*K), c(I,J,K))
datasets = list(df, df)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes)

model1 = acmtf_opt(Z, 1)

Fac1 = model1$Fac[1:3]
Fac2 = Fac1 # identical models for the purposes of demonstration
result = FMS_random(Fac1, Fac2) # [1] 1

Georgiou2025 Apical Periodontitis data

Description

The Georgiou longitudinal dataset as a three-dimensional array and a matrix, with subjects in mode 1, features in mode 2, and time in mode3.

Usage

Georgiou2025

Format

Georgiou2025

A list object with two elements

Inflammatory_mediators

Longitudinally measured inflammatory mediator data.

Tooth_microbiome

Single-timepoint tooth microbiome data of the extracted tooth

Source

TBD

Function value and gradient calculation for ACMTF

Description

Function value and gradient calculation for ACMTF

Usage

acmtf_fg(x, Z, alpha = 1, beta = rep(0.001, length(Z$object)), epsilon = 1e-08)

Arguments

Value

A list containing the function ("fn") and the gradient ("gr").

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeACMTF(Z, 2, output="vect")
outcome = acmtf_fg(init, Z)
f = outcome$fn
g = outcome$gr

Calculate function value of ACMTF

Description

Calculate function value of ACMTF

Usage

acmtf_fun(
  x,
  Z,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  manual = FALSE
)

Arguments

Value

Scalar of the loss function value (when manual=FALSE), otherwise a list containing all loss terms

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeACMTF(Z, 2, output="vect")
f = acmtf_fun(init, Z)

Calculate gradient of ACMTF model.

Description

Calculate gradient of ACMTF model.

Usage

acmtf_gradient(
  x,
  Z,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08
)

Arguments

Value

Vectorized gradient of the ACMTF model.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeACMTF(Z, 2, output="vect")
g = acmtf_gradient(init, Z)

Advanced coupled matrix and tensor factorizations

Description

Advanced coupled matrix and tensor factorizations

Usage

acmtf_opt(
  Z,
  numComponents,
  initialization = "random",
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 10000,
  abs_tol = 1e-10,
  rel_tol = 1e-10,
  grad_tol = 1e-10,
  nstart = 1,
  numCores = 1,
  sortComponents = TRUE,
  allOutput = FALSE
)

Arguments

Value

List object, similar to mize::mize() output. Includes a Fac object of the model, which is a list of components per mode. Also includes an init object giving the initialized input vectors.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

# specific setting to reduce runtime for CRAN
model = acmtf_opt(Z, 1, rel_tol=1e-5, abs_tol=1e-5)

Calculate function value of ACMTF

Description

Calculate function value of ACMTF

Usage

acmtfr_fg(
  x,
  Z,
  Y,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  mu = 1e-06
)

Arguments

Value

Scalar of the loss function value (when manual=FALSE), otherwise a list containing all loss terms.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)
Y = A[,1]

init = initializeACMTF(Z, 2, output="vect")
outcome = acmtfr_fg(init, Z, Y)
f = outcome$fn
g = outcome$gr

Calculate function value of ACMTF

Description

Calculate function value of ACMTF

Usage

acmtfr_fun(
  x,
  Z,
  Y,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  mu = 1e-06,
  manual = FALSE
)

Arguments

Value

Scalar of the loss function value (when manual=FALSE), otherwise a list containing all loss terms.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)
Y = A[,1]

init = initializeACMTF(Z, 2, output="vect")
f = acmtfr_fun(init, Z, Y)

Calculate gradient of ACMTF model.

Description

Calculate gradient of ACMTF model.

Usage

acmtfr_gradient(
  x,
  Z,
  Y,
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  mu = 1e-06
)

Arguments

Value

Vectorized gradient of the ACMTF regression model.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)
Y = A[,1]

init = initializeACMTF(Z, 2, output="vect")
g = acmtfr_gradient(init, Z, Y)

Advanced coupled matrix and tensor factorizations

Description

Advanced coupled matrix and tensor factorizations

Usage

acmtfr_opt(
  Z,
  Y,
  numComponents,
  initialization = "random",
  alpha = 1,
  beta = rep(0.001, length(Z$object)),
  epsilon = 1e-08,
  pi = 0.5,
  mu = 1e-06,
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 10000,
  abs_tol = 1e-10,
  rel_tol = 1e-10,
  grad_tol = 1e-10,
  nstart = 1,
  numCores = 1,
  sortComponents = TRUE,
  allOutput = FALSE
)

Arguments

Value

List object, similar to mize::mize() output. Includes a Fac object of the model, which is a list of components per mode. Also includes an init object giving the initialized input vectors.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
Y = A[,1]
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

# specific setting to reduce runtime for CRAN
model = acmtfr_opt(Z, Y, 2, rel_tol=1e-5, abs_tol=1e-5)

Function value and gradient calculation for CMTF

Description

Function value and gradient calculation for CMTF

Usage

cmtf_fg(x, Z)

Arguments

Value

A list containing the function ("fn") and the gradient ("gr").

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeCMTF(Z, 2, output="vect")
outcome = cmtf_fg(init, Z)
f = outcome$fn
g = outcome$gr

Calculate function value of CMTF

Description

Calculate function value of CMTF

Usage

cmtf_fun(x, Z, manual = FALSE)

Arguments

Value

Function value of the CMTF loss value if manual=FALSE, otherwise a vector of the loss values per term.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeCMTF(Z, 2, output="vect")
f = cmtf_fun(init, Z)

Calculate gradient of CMTF model.

Description

Calculate gradient of CMTF model.

Usage

cmtf_gradient(x, Z)

Arguments

Value

Vectorized gradient of the CMTF model.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeCMTF(Z, 2, output="vect")
g = cmtf_gradient(init, Z)

Coupled matrix and tensor factorizations

Description

Coupled matrix and tensor factorizations

Usage

cmtf_opt(
  Z,
  numComponents,
  initialization = "random",
  method = "CG",
  cg_update = "HS",
  line_search = "MT",
  max_iter = 10000,
  max_fn = 10000,
  abs_tol = 1e-08,
  rel_tol = 1e-08,
  grad_tol = 1e-08,
  nstart = 1,
  numCores = 1,
  sortComponents = TRUE,
  allOutput = FALSE
)

Arguments

Value

List object, similar to mize::mize() output. Includes a Fac object of the model, which is a list of components per mode. Also includes an init object giving the initialized input vectors.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

model = cmtf_opt(Z, 1, rel_tol=1e-4) # quick convergence for example only

Compute Factor Match Score for two models.

Description

Compute Factor Match Score for two models.

Usage

computeFMS(Fac1, Fac2, modes)

Arguments

Value

Vector of FMS scores, one per dataset.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100, 2))
E = array(rnorm(10*2), c(10, 2))

Fac1 = list(A,B,C,D,E)
Fac2 = Fac1 # identical models for the purposes of demonstration
modes = list(c(1,2,3), c(1,4,5))
FMS_result = computeFMS(Fac1, Fac2, modes) # FMS_result = c(1,1)

degenScore

Description

Computes the maximum absolute off-diagonal Tucker congruence coefficient between subject-mode components in an ACMTF model. This metric serves as a diagnostic tool to detect potential degeneracy in the subject-mode loadings.

Usage

degenScore(A)

Arguments

Details

A high degenScore (e.g., > 0.85) indicates that two or more components in the subject mode are highly similar, suggesting a possible degeneracy or lack of uniqueness. A low value (e.g., < 0.3) indicates well-separated components.

Value

A numeric scalar representing the maximum absolute off-diagonal Tucker congruence between components.

Examples

# Example: Compute degenScore for a random loading matrix
A <- matrix(rnorm(100), nrow = 10, ncol = 10)
degenScore(A)

Vectorize Fac object

Description

Vectorize Fac object

Usage

fac_to_vect(Fac)

Arguments

Value

Vectorized Fac object

Examples

set.seed(123)
A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))
Fac = list(A, B, C, D, E)
v = fac_to_vect(Fac)

Initialize input vectors for the ACMTF algorithm

Description

Initialize input vectors for the ACMTF algorithm

Usage

initializeACMTF(
  Z,
  numComponents,
  initialization = "random",
  output = "Fac",
  Y = NULL
)

Arguments

Value

List or vector of initialized input vectors per mode.

Examples

A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeACMTF(Z, 2)

Initialize input vectors for the CMTF algorithm

Description

Initialize input vectors for the CMTF algorithm

Usage

initializeCMTF(
  Z,
  numComponents,
  initialization = "random",
  output = "Fac",
  Y = NULL
)

Arguments

Value

List or vector of initialized input vectors per mode.

Examples

set.seed(123)
A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

init = initializeCMTF(Z, 1)

Normalize all vectors in model output Fac object to norm 1.

Description

Normalize all vectors in model output Fac object to norm 1.

Usage

normalizeFac(Fac, modes)

Arguments

Value

List object of normalized Fac object, the extracted norms per loading vector per component, and the norms per dataset per component.

Examples

set.seed(123)
A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))
modes = list(c(1,2,3), c(1,4,5))

Fac = list(A, B, C, D, E)
output = normalizeFac(Fac, modes)

Predict Y for new data by projecting the data onto the latent space defined by an ACMTF-R model.

Description

Predict Y for new data by projecting the data onto the latent space defined by an ACMTF-R model.

Usage

npred(model, newX, Z, sharedMode = 1)

Arguments

Value

Ypred: the predicted value of Y for the new data

Examples

set.seed(123)
A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100, 2))
E = array(rnorm(10*2), c(10, 2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes)
Y = matrix(A[,1])
# Remove a sample and define
i = 1
Xtest = lapply(Z$object, function(x){x@data[i,,]})
Ytest = Y[i]
Xtrain = lapply(Z$object, function(x){x@data[-i,,]})
Ytrain = Y[-i]
Ztrain = setupCMTFdata(Xtrain, Z$modes)
model = acmtfr_opt(Ztrain,Ytrain,1,initialization="random",pi=1, nstart=1, max_iter=10)
Ypred = npred(model, Xtest, Ztrain, sharedMode=1)

Reinflate all datablocks from a model Fac object.

Description

Basically a wrapper function for reinflateTensor() and reinflateMatrix().

Usage

reinflateFac(Fac, Z, returnAsTensor = FALSE)

Arguments

Value

List of data blocks

Examples

set.seed(123)
A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

result = cmtf_opt(Z, 1, max_iter=2)
Xhats = reinflateFac(result$Fac, Z)

Create a matrix from a matrix of scores and loadings similar to a component model.

Description

Create a matrix from a matrix of scores and loadings similar to a component model.

Usage

reinflateMatrix(A, B)

Arguments

Value

M, an I x J matrix.

Examples

A = rnorm(108)
B = rnorm(100)
M = reinflateMatrix(A,B)

Create a tensor out of a set of matrices similar to a component model.

Description

Create a tensor out of a set of matrices similar to a component model.

Usage

reinflateTensor(A, B, C)

Arguments

Value

M, an I x J x K tensor.

Examples

A = rnorm(108)
B = rnorm(100)
C = rnorm(10)
M = reinflateTensor(A,B,C)

Remove two-norms column-wise from a matrix

Description

Remove two-norms column-wise from a matrix

Usage

removeTwoNormCol(df)

Arguments

Value

Matrix of loadings where the column-wise 2-norm is 1.

Examples

A = array(rnorm(108*4), c(108,4))
Anorm = removeTwoNormCol(A)

Set up datasets for (A)CMTF input

Description

Set up datasets for (A)CMTF input

Usage

setupCMTFdata(datasets, modes, normalize = TRUE)

Arguments

Value

Z, a list with "object" listing the datasets, "sizes" with their size, "norms" with their norms and "missing" stating the missing data.

Examples

set.seed(123)
A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

Convert vectorized output of (a)cmtf to a Fac list object with all loadings per mode.

Description

Convert vectorized output of (a)cmtf to a Fac list object with all loadings per mode.

Usage

vect_to_fac(vect, Z, sortComponents = FALSE)

Arguments

Value

Fac: list object with all loadings in all components per mode, ordered the same way as Z$modes.

Examples

set.seed(123)
A = array(rnorm(108*2), c(108, 2))
B = array(rnorm(100*2), c(100, 2))
C = array(rnorm(10*2), c(10, 2))
D = array(rnorm(100*2), c(100,2))
E = array(rnorm(10*2), c(10,2))

df1 = reinflateTensor(A, B, C)
df2 = reinflateTensor(A, D, E)
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4,5))
Z = setupCMTFdata(datasets, modes, normalize=FALSE)

result = cmtf_opt(Z, 2, initialization="random", max_iter = 2)
Fac = vect_to_fac(result$par, Z)