cpfa: Classification with Parallel Factor Analysis

In cpfa: Classification with Parallel Factor Analysis

Classification with Parallel Factor Analysis

Description

Fits Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model to a three-way or four-way data array. Also fits a principal component analysis model (PCA) to a two-way matrix. Allows for different constraint options on multiple tensor modes. For PCA, allows for either an unrotated or varimax rotated solution. Uses component weights from a single mode of the selected component model as predictors to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Predicts class labels and calculates multiple performance measures for binary or multiclass classification across multiple replications with different train-test splits. Provides descriptive statistics to pool output across replications.

Usage

cpfa(x, y, model = c("parafac", "parafac2", "pca"), nfac = 1, nrep = 5, 
     ratio = 0.8, nfolds = 10, 
     method = c("PLR", "SVM", "RF", "NN", "RDA", "GBM"), 
     family = c("binomial", "multinomial"), parameters = list(), 
     type.out = c("measures", "descriptives"), foldid = NULL, prior = NULL, 
     cmode = NULL, seeds = NULL, plot.out = FALSE, plot.measures = NULL, 
     parallel = FALSE, cl = NULL, verbose = TRUE, compscale = TRUE, 
     pcarot = c("unrotated", "varimax"), ...)

Arguments

x	A three-way or four-way data array. For Parafac2, can be a list where each element is a matrix or three-way array. Array or list must contain only real numbers. See note below. For PCA, can be a two-way matrix, a three-way array, or a four-way array. If a three-way or four-way array for PCA, array is unfolded across all modes except the classification mode. Note that if three-way or four-way array for PCA, classification mode must be the last mode.
y	A vector containing at least two unique class labels. Should be a factor that contains two or more levels. For binary case, ensure the order of factor levels (left to right) is such that negative class is first and positive class is second. Levels should be sequential integers starting from 0, 1, ..., etc.
model	Character designating the component model to use, including: model = "parafac" to fit the Parafac model, model = "parafac2" to fit the Parafac2 model, or model = "pca" to fit the PCA model.
nfac	Vector containing integers that specify the number of components for each component model to fit. Default is nfac = 1.
nrep	Number of replications to repeat the procedure. Default is nrep = 5.
ratio	Split ratio for dividing data into train and test sets. Default is ratio = 0.8.
nfolds	Numeric value specifying number of folds for k-fold cross-validation. Must be 2 or greater. Default is nfolds = 10.
method	Character vector indicating classification methods to use. Possible methods include penalized logistic regression (PLR); support vector machine (SVM); random forest (RF); feed-forward neural network (NN); regularized discriminant analysis (RDA); and gradient boosting machine (GBM). If none are selected, default is to use all methods with method = c("PLR", "SVM", "RF", "NN", "RDA", "GBM").
family	Character value specifying binary classification (family = "binomial") or multiclass classification (family = "multinomial"). If not provided, number of levels of input y is used, where two levels is binary, and where three or more levels is multiclass.
parameters	List containing arguments related to classification methods. When specified, must contain one or more of the following: alpha Values for penalized logistic regression alpha parameter; default is alpha = seq(0, 1, length = 6). Must be numeric and contain only real numbers between 0 and 1, inclusive. lambda Optional user-supplied lambda sequence for cv.glmnet for penalized logistic regression. Default is NULL. cost Values for support vector machine cost parameter; default is cost = c(1, 2, 4, 8, 16, 32, 64). Must be numeric and contain only real numbers greater than 0. gamma Values for support vector machine gamma parameter; default is gamma = c(0, 0.01, 0.1, 1, 10, 100, 1000). Must be numeric and greater than or equal to 0. ntree Values for random forest number of trees parameter; default is ntree = c(100, 200, 400, 600, 800, 1600, 3200). Must be numeric and contain only integers greater than or equal to 1. nodesize Values for random forest node size parameter; default is nodesize = c(1, 2, 4, 8, 16, 32, 64). Must be numeric and contain only integers greater than or equal to 1. size Values for neural network size parameter; default is size = c(1, 2, 4, 8, 16, 32, 64). Must be numeric and contain only integers greater than or equal to 0. decay Values for neural network decay parameter; default is decay = c(0.001, 0.01, 0.1, 1, 2, 4, 8, 16). Must be numeric and contain only real numbers. rda.alpha Values for regularized discriminant analysis alpha parameter; default is rda.alpha = seq(0, 0.999, length = 6). Must be numeric and contain only real numbers between 0 (inclusive) and 1 (exclusive). delta Values for regularized discriminant analysis delta parameter; default is delta = c(0, 0.1, 1, 2, 3, 4). Must be numeric and contain only real numbers greater than or equal to 0. eta Values for gradient boosting machine eta parameter; default is eta = c(0.1, 0.3, 0.5, 0.7, 0.9). Must be numeric and contain only real numbers greater than 0 and less than 1. max.depth Values for gradient boosting machine max.depth parameter; default is max.depth = c(1, 2, 3, 4). Must be numeric and contain only integers greater than or equal to 1. subsample Values for gradient boosting machine subsample parameter; default is subsample = c(0.6, 0.7, 0.8, 0.9). Must be numeric and contain only real numbers greater than 0 and less than or equal to 1. nrounds Values for gradient boosting machine nrounds parameter; default is nrounds = c(100, 200, 300, 500). Must be numeric and contain only integers greater than or equal to 1.
type.out	Type of output desired: type.out = "measures" returns an array containing classification performance measures for all replications while type.out = "descriptives" returns the list of descriptive statistics calculated across all replications for each performance measure. Both options also provide the estimated training weights and classification weights. Defaults to type.out = "descriptives".
foldid	List containing, for each replication, an integer vector. Should have length equal to 'nrep'. Each list element contains fold IDs for k-fold cross-validation. If not provided, fold IDs are generated randomly for the number of folds nfolds.
prior	Prior probabilities of class membership. If specified, the probabilities should be in the order of the factor levels of input y. If unspecified, the observed class proportions for input y are used. Based on prior, inverse probability weights are calculated to account for class imbalance. Note that RF and RDA ignore prior and use uniform priors to handle imbalance.
cmode	Integer value of 1, 2, or 3 (or 4 if x is a four-way array) specifying the mode whose component weights will be predictors for classification. Defaults to the last mode of the input array (i.e., defaults to 3 for a three-way array, and to 4 for a four-way array). If model = "parafac2", the last mode will be used. If model = "pca", and if input x is a matrix, cmode can be 1 or 2; if not provided, defaults to 1. However, if model = "pca", and if input x is a 3-way or 4-way array, cmode must be the last mode when supplied.
seeds	Random seeds to be associated with each replication. Default is seeds = 1:nrep.
plot.out	Logical indicating whether to output one or more box plots of classification performance measures that are plotted across classification methods and number of components.
plot.measures	Character vector containing values that specify for plotting one or more of 11 possible classification performance measures. Only relevant when plot.out = TRUE. Should contain one or more of the following labels: c("err", "acc", "tpr", "fpr", "tnr", "fnr", "ppv", "npv", "fdr", "fom", "fs"). A box plot will be created for each measure that is specified, summarizing output across replications. Note that additional information about each label is available in the Details section of the help file for function cpm. If NULL, defaults to classification accuracy.
parallel	Logical indicating if parallel computing should be implemented. If TRUE, the package parallel is used for parallel computing. For all classification methods except penalized logistic regression, the doParallel package is used as a wrapper. Defaults to FALSE, which implements sequential computing.
cl	Cluster for parallel computing, which is used when parallel = TRUE. Note that if parallel = TRUE and cl = NULL, then the cluster is defined as makeCluster(detectCores()).
verbose	If TRUE, progress is printed.
compscale	Logical indicating whether to scale each column of the estimated classification component weights matrix (i.e., the features/predictors). If TRUE, each column is scaled to have mean zero and unit variance. If FALSE, no scaling is performed.
pcarot	Character indicating whether to apply a varimax rotation or leave PCA loadings unrotated when model is set to pca. Ignored when model is not pca. Defaults to "unrotated" if not specified.
...	Additional arguments to be passed to function parafac for fitting a Parafac model or function parafac2 for fitting a Parafac2 model. Example: can impose different constraints on different modes of the input array using the argument const. See help file for function parafac or for function parafac2 for additional details.

Details

Data are split into a training set and a testing set. After fitting a Parafac or Parafac2 model with the training set using package multiway (see parafac or parafac2 in multiway for details), or after fitting a PCA model using the singular value decomposition, the estimated classification mode weight matrix is passed to one or more classification methods. The methods include: penalized logistic regression (PLR); support vector machine (SVM); random forest (RF); feed-forward neural network (NN); regularized discriminant analysis (RDA); and gradient boosting machine (GBM).

Package glmnet fits models for PLR. PLR tunes penalty parameter lambda while the elastic net parameter alpha is set by the user (see the help file for function cv.glmnet in package glmnet). For SVM, package e1071 is used with a radial basis kernel. Penalty parameter cost and radial basis parameter gamma are used (see svm in package e1071). For RF, package randomForest is used and implements Breiman's random forest algorithm. The number of predictors sampled at each node split is set at the default of sqrt(R), where R is the number of Parafac or Parafac2 components. Two tuning parameters allowed are ntree, the number of trees to be grown, and nodesize, the minimum size of terminal nodes (see randomForest in package randomForest). For NN, package nnet fits a single-hidden-layer, feed-forward neural network model. Penalty parameters size (i.e., number of hidden layer units) and decay (i.e., weight decay) are used (see nnet). For RDA, package rda fits a shrunken centroids regularized discriminant analysis model. Tuning parameters include rda.alpha, the shrinkage penalty for the within-class covariance matrix, and delta, the shrinkage penalty of class centroids towards the overall dataset centroid. For GBM, package xgboost fits a gradient boosting machine model. Four tuning parameters are allowed: (1) eta, the learning rate; (2) max.depth, the maximum tree depth; (3) subsample, the fraction of samples per tree; and (4) nrounds, the number of boosting trees to build.

For all six methods, k-fold cross-validation is implemented to tune classification parameters where the number of folds is set by argument nfolds. Separately, the trained Parafac, Parafac2, or PCA model is used to predict the classification mode's component weights using the testing set data. The predicted component weights and the optimized classification method are then used to predict class labels. Finally, classification performance measures are calculated. The process is repeated over a number of replications with different random splits of the input array and of the class labels at each replication.

Value

Returns an object of class wrapcpfa that either (1) contains a three-way array with classification performance measures for each model and for each replication, or (2) contains a list with matrices with descriptive statistics for performance measures calculated across all replications. Specify type.out = "measures" to output the array of performance measures. Specify type.out = "descriptives" to output descriptive statistics across replications. In addition, for either option, the returned list object includes the following:

predweights	List of predicted classification weights for each Parafac, Parafac2, or PCA model and for each replication.
train.weights	List of lists of training weights for each Parafac, Parafac2, or PCA model and for each replication.
opt.tune	List of optimal tuning parameters for classification methods for each Parafac or Parafac2 model and for each replication.
mean.opt.tune	Mean across all replications of optimal tuning parameters for classification methods for each Parafac or Parafac2 model.
X	Two-way matrix, or three-way or four-way data array or list used in argument x. If x was a three-way or four-way array, and if model was pca, returns the flattened two-way matrix.
y	Vector of class labels used in input argument y.
nfac	Number of components used to fit each Parafac, Parafac2, or PCA model.
model	Character designating the component model that was used, including: model = "parafac" for the Parafac model, model = "parafac2" for the Parafac2 model, or model = "pca" for the PCA model.
method	Classification methods used.
const	Constraints used in fitting the Parafac, Parafac2, or PCA model. When model was pca, contains the value used in input pcarot to indicate whether a rotation was applied to the solution.
cmode	Integer value used to specify the mode whose component weights were predictors for classification.
family	Character value used to specify binary classification (family = "binomial") or multiclass classification (family = "multinomial").
lxdim	Integer specifying the number of modes of the output x. Identical to the number of modes of input x, except when input x was a 3-way or 4-way array and when model was pca. In this case, lxdim is 2.
trainIDs	List where each element contains the vector of integer IDs that specify the rows/observations of the classification mode assigned to the train set for a given replication.
testIDs	List where each element contains the vector of integer IDs that specify the rows/observations of the classification mode assigned to the test set for a given replication.
flattened	Logical indicating whether input x was flattened into a two-way matrix. Only TRUE when model was pca and input x was a 3-way or 4-way array.

Note

For Parafac and Parafac2, if argument cmode is not null, input array x is reshaped with function aperm such that the cmode dimension of x is ordered last. Estimated mode A and B (and mode C for a four-way array) weights that are outputted as Aweights and Bweights (and Cweights) reflect this permutation. For example, if x is a four-way array and cmode = 2, the original input modes 1, 2, 3, and 4 will correspond to output modes 1, 3, 4, 2. Here, output A = input 1; B = 3, and C = 4 (i.e., the second mode specified by cmode has been moved to the D mode/last mode). For model = "parafac2", classification mode is assumed to be the last mode (i.e., mode C for three-way array and mode D for four-way array). For PCA, if argument cmode is not NULL, and if x is a 3-way or 4-way array, the array is reshaped with aperm such that the cmode dimension of x is ordered first. Then, the array is unfolded into a two-way matrix. If x is already input as a two-way matrix, the matrix is reshaped if cmode is 2, such that the matrix is transposed. Note that for PCA, Aweights contains the PCA model loadings.

In addition, note that the following combination of arguments will give an error: nfac = 1, family = "multinomial", method = "PLR". The issue arises from providing glmnet::cv.glmnet input x an input matrix that has a single column. The issue is resolved for family = "binomial" because a column of 0s is appended to the single column, but this solution does not appear to work for the multiclass case. As such, this combination of arguments is not currently allowed. Future updates are planned to resolve this issue.

Applications of this function to real datasets can be explored at the following repository: https://github.com/matthewasisgress/multiway-classification/.

Author(s)

Matthew A. Asisgress <[e-mail]>

References

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., Li, Y., Yuan, J. (2025). xgboost: Extreme gradient boosting. R Package Version 1.7.9.1.

Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5), 1189-1232.

Friedman, J. H. (1989). Regularized discriminant analysis. Journal of the American Statistical Association, 84(405), 165-175.

Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22.

Gaujoux, R. (2025). doRNG: Generic reproducible parallel backend for 'foreach' loops. R Package Version 1.8.6.2.

Guo, Y., Hastie, T., and Tibshirani, R. (2007). Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1), 86-100.

Guo, Y., Hastie, T., and Tibshirani, R. (2023). rda: Shrunken centroids regularized discriminant analysis. R Package Version 1.2-1.

Harshman, R. (1970). Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multimodal factor analysis. UCLA Working Papers in Phonetics, 16, 1-84.

Harshman, R. (1972). PARAFAC2: Mathematical and technical notes. UCLA Working Papers in Phonetics, 22, 30-44.

Harshman, R. and Lundy, M. (1994). PARAFAC: Parallel factor analysis. Computational Statistics and Data Analysis, 18, 39-72.

Helwig, N. (2017). Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints. Biometrical Journal, 59(4), 783-803.

Helwig, N. (2025). multiway: Component models for multi-way data. R Package Version 1.0-7.

Liaw, A. and Wiener, M. (2002). Classification and regression by randomForest. R News 2(3), 18–22.

Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2024). e1071: Misc functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R Package Version 1.7-16.

Ripley, B. (1994). Neural networks and related methods for classification. Journal of the Royal Statistical Society: Series B (Methodological), 56(3), 409-437.

Venables, W. and Ripley, B. (2002). Modern applied statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0.

Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.

Examples

########## Parafac2 example with 4-way array and multiclass response ##########
## Not run: 
# set seed
set.seed(5)

# define list of arguments specifying distributions for A and G weights
techlist <- list(distA = list(dname = "poisson", 
                              lambda = 3),                 # for A weights
                 distG = list(dname = "gamma", shape = 2, 
                              scale = 4))                  # for G weights

# define target correlation matrix for columns of D mode weights matrix
cormat <- matrix(c(1, .6, .6, .6, 1, .6, .6, .6, 1), nrow = 3, ncol = 3)

# simulate a four-way ragged array connected to a response
data <- simcpfa(arraydim = c(10, 11, 12, 100), model = "parafac2", nfac = 3, 
                nclass = 3, nreps = 1e2, onreps = 10, corresp = rep(.6, 3), 
                meanpred = rep(2, 3), modes = 4, corrpred = cormat,
                technical = techlist, smethod = "eigende")

# initialize
alpha <- seq(0, 1, length = 20)
gamma <- c(0, 1)
cost <- c(0.1, 5)
ntree <- c(200, 300)
nodesize <- c(1, 2)
size <- c(1, 2)
decay <- c(0, 1)
rda.alpha <- seq(0.1, 0.9, length = 2)
delta <- c(0.1, 2)
eta <- c(0.3, 0.7)
max.depth <- c(1, 2)
subsample <- c(0.75)
nrounds <- c(100)
method <- c("PLR", "SVM", "RF", "NN", "RDA", "GBM")
family <- "multinomial"
parameters <- list(alpha = alpha, gamma = gamma, cost = cost, ntree = ntree,
                   nodesize = nodesize, size = size, decay = decay, 
                   rda.alpha = rda.alpha, delta = delta, eta = eta,
                   max.depth = max.depth, subsample = subsample,
                   nrounds = nrounds)
model <- "parafac2"
nfolds <- 10
nstart <- 10

# constrain first mode weights to be orthogonal, fourth mode to be nonnegative
const <- c("orthog", "uncons", "uncons", "nonneg")

# fit Parafac2 model and use fourth mode weights to tune classification
# methods, to predict class labels, and to return classification 
# performance measures pooled across multiple train-test splits
output <- cpfa(x = data$X, y = data$y, model = model, nfac = 3, 
               nrep = 5, ratio = 0.9, nfolds = nfolds, method = method, 
               family = family, parameters = parameters, 
               type.out = "descriptives", seeds = NULL, plot.out = TRUE, 
               parallel = FALSE, const = const, nstart = nstart)

# print performance measure means across train-test splits
output$descriptive$mean

## End(Not run)

cpfa documentation built on March 30, 2026, 1:06 a.m.