The CMTFtoolbox package provides R users with two data
fusion methods that have previously been presented in the MATLAB
sphere.
cmtf_opt: Coupled Matrix and Tensor Factorization
(CMTF) (doi:10.48550/arXiv.1105.3422).acmtf_opt: Advanced Coupled Matrix and Tensor
Factorization (ACMTF) (doi:10.1186/1471-2105-15-239).acmtfr_opt: ACMTF-regression (ACMTF-R) as described in
van der Ploeg et al., 2025 (see citation below).Both of these methods were implemented using the all-at-once
optimization approaches as described in the papers above. This
implementation was achieved using the S4 Tensor object from
rTensor and the various conjugate gradient approaches from
mize. Other features of the package include:
ACMTF_modelSelection: Combined random initialization
and cross-validation approach for determining the correct number of
components in ACMTF.ACMTFR_modelSelection: Combined random initialization
and cross-validation approach for determining the correct number of
components in ACMTF-R.npred: Prediction of Y for a new sample using an
existing ACMTF-R model.Georgiou2025: An example dataset containing a tensor of
inflammatory mediator data and a matrix of tooth microbiome data in a
cohort of apical periodontitis patients (doi:10.1111/iej.13854 and doi:10.1111/iej.13912).The CMTFtoolbox package can be installed from CRAN
using:
install.packages("CMTFtoolbox")You can install the development version of CMTFtoolbox
from GitHub with:
# install.packages("devtools")
devtools::install_github("GRvanderPloeg/CMTFtoolbox")Please use the following citation when using this package:
library(CMTFtoolbox)
set.seed(123)
numComponents = 3
I = 108
J = 100
K = 10
L = 100
A = array(rnorm(I*numComponents), c(I, numComponents)) # shared subject mode
B = array(rnorm(J*numComponents), c(J, numComponents)) # distinct feature mode of X1
C = array(rnorm(K*numComponents), c(K, numComponents)) # distinct condition mode of X1
D = array(rnorm(L*numComponents), c(L, numComponents)) # distinct feature mode of X2
Y = matrix(A[,1])
lambdas = array(c(1, 1, 1, 0, 0, 1), c(2,3))
df1 = array(0L, c(I, J, K))
df2 = array(0L, c(I, L))
for(i in 1:numComponents){
df1 = df1 + lambdas[1,i] * reinflateTensor(A[,i], B[,i], C[,i])
df2 = df2 + lambdas[2,i] * reinflateMatrix(A[,i], D[,i])
}
datasets = list(df1, df2)
modes = list(c(1,2,3), c(1,4))
Z = setupCMTFdata(datasets, modes, normalize=TRUE)
cmtf_model = cmtf_opt(Z, 3)
acmtf_model = acmtf_opt(Z, 3)
acmtfr_model = acmtfr_opt(Z, Y, 3)