cpfa {cpfa} | R Documentation |
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. Allows for multiple constraint options on different tensor modes. Uses Parafac component weights from a single mode of this 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 over some number of replications with different train-test splits. Provides descriptive statistics to pool output across replications.
Usage
cpfa(x, y, model = c("parafac", "parafac2"), nfac = 1,
nrep = 5, ratio = 0.8, nfolds = 10,
method = c("PLR", "SVM", "RF", "NN", "RDA"),
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, ...)
Arguments
x |
Three-way or four-way data array. Must contain real numbers. See note below. |
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. |
model |
Character designating the Parafac model to use, either |
nfac |
Number of components for each Parafac or Parafac2 model to fit. Default is |
nrep |
Number of replications to repeat the procedure. Default is |
ratio |
Split ratio for dividing data into train and test sets. Default is |
nfolds |
Numeric setting number of folds for k-fold cross-validation. Must be 2 or greater. Default is |
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); and regularized discriminant analysis (RDA). If none selected, default is to use all methods with |
family |
Character value specifying binary classification ( |
parameters |
List containing arguments related to classification methods. When specified, must contain one or more of the following:
|
type.out |
Type of output desired: |
foldid |
Integer vector containing fold IDs for k-fold cross-validation. If not provided, fold IDs are generated randomly for number of folds |
prior |
Prior probabilities of class membership. If unspecified, the class proportions for input |
cmode |
Integer value of 1, 2, or 3 (or 4 if |
seeds |
Random seeds to be associated with each replication. Default is |
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 |
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 |
verbose |
If TRUE, progress is printed. |
... |
Additional arguments to be passed to function |
Details
After fitting a Parafac or Parafac2 model with package multiway (see parafac
or parafac2
in multiway for details), the estimated classification mode weight matrix is passed to one or several of five classification methods–including penalized logistic regression (PLR); support vector machine (SVM); random forest (RF); feed-forward neural network (NN); and regularized discriminant analysis (RDA).
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 all five methods, k-fold cross-validation is implemented to tune classification parameters where the number of folds is set by argument nfolds
. After tuning, class labels are predicted using optimal parameters; and 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 either a three-way array with classification performance measures for each model and for each replication, or a list containing matrices with different 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 both options, the following objects are also provided:
predweights |
List of predicted classification weights for each Parafac or Parafac2 model and for each replication. |
train.weights |
List of lists of training weights for each Parafac or Parafac2 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. |
Note
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).
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
a matrix with 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. This issue will be resolved in a future update.
Author(s)
Matthew A. Snodgress <snodg031@umn.edu>
References
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
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.
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. (2019). multiway: Component models for multi-way data. R Package Version 1.0-6.
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. (2023). e1071: Misc functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R Package Version 1.7-13.
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
########## Parafac example with 3-way array and binary response ##########
# set seed and specify dimensions of a three-way tensor
set.seed(3)
mydim <- c(10, 11, 100)
nf <- 3
# create correlation matrix between response and third mode's weights
rho.cc <- .35
rho.cy <- .75
cormat.values <- c(1, rho.cc, rho.cc, rho.cy, rho.cc, 1, rho.cc, rho.cy,
rho.cc, rho.cc, 1, rho.cy, rho.cy, rho.cy, rho.cy, 1)
cormat <- matrix(cormat.values, nrow = (nf + 1), ncol = (nf + 1))
# sample from a multivariate normal with specified correlation structure
ymean <- Cmean <- 2
mu <- as.matrix(c(Cmean, Cmean, Cmean, ymean))
eidecomp <- eigen(cormat, symmetric = TRUE)
L.sqrt <- diag(eidecomp$values^0.5)
cormat.sqrt <- eidecomp$vectors %*% L.sqrt %*% t(eidecomp$vectors)
Z <- matrix(rnorm(mydim[3] * (nf + 1)), nrow = mydim[3], ncol = (nf + 1))
Xw <- rep(1, mydim[3]) %*% t(mu) + Z %*% cormat.sqrt
Cmat <- Xw[, 1:nf]
# create a random three-way data tensor with C weights related to a response
Amat <- matrix(rnorm(mydim[1] * nf), nrow = mydim[1], ncol = nf)
Bmat <- matrix(runif(mydim[2] * nf), nrow = mydim[2], ncol = nf)
Xmat <- tcrossprod(Amat, krprod(Cmat, Bmat))
Xmat <- array(Xmat, dim = mydim)
Emat <- array(rnorm(prod(mydim)), dim = mydim)
Emat <- nscale(Emat, 0, ssnew = sumsq(Xmat))
X <- Xmat + Emat
# create a binary response by dichotomizing at the specified response mean
y <- factor(as.numeric(Xw[ , (nf + 1)] > ymean))
# initialize
alpha <- seq(0, 1, length = 2)
gamma <- c(0, 0.01)
cost <- c(1, 2)
ntree <- c(100, 200)
nodesize <- c(1, 2)
size <- c(1, 2)
decay <- c(0, 1)
rda.alpha <- c(0.1, 0.6)
delta <- c(0.1, 2)
method <- c("PLR", "SVM", "RF", "NN", "RDA")
family <- "binomial"
parameters <- list(alpha = alpha, gamma = gamma, cost = cost, ntree = ntree,
nodesize = nodesize, size = size, decay = decay,
rda.alpha = rda.alpha, delta = delta)
model <- "parafac"
nfolds <- 3
# constrain first mode weights to be orthogonal
const <- c("orthog", "uncons", "uncons")
# fit Parafac models and use third mode weights to tune classification
# methods, to predict class labels, and to return classificaiton
# performance measures pooled across multiple train-test splits
output <- cpfa(x = X, y = y, model = model, nfac = 3, nrep = 2, ratio = 0.8,
nfolds = nfolds, method = method, family = family,
parameters = parameters, type.out = "descriptives",
seeds = NULL, plot.out = TRUE, parallel = FALSE, const = const)
# print performance measure means across train-test splits
output$descriptive$mean
########## Parafac2 example with 4-way array and multiclass response ##########
# set seed and specify dimensions of a four-way tensor
set.seed(5)
mydim <- c(10, 11, 12, 100)
nf <- 3
# create correlation matrix between response and fourth mode's weights
rho.dd <- .35
rho.dy <- .75
cormat.values <- c(1, rho.dd, rho.dd, rho.dy, rho.dd, 1, rho.dd, rho.dy,
rho.dd, rho.dd, 1, rho.dy, rho.dy, rho.dy, rho.dy, 1)
cormat <- matrix(cormat.values, nrow = (nf + 1), ncol = (nf + 1))
# sample from a multivariate normal with specified correlation structure
ymean <- Dmean <- 2
mu <- as.matrix(c(Dmean, Dmean, Dmean, ymean))
eidecomp <- eigen(cormat, symmetric = TRUE)
L.sqrt <- diag(eidecomp$values^0.5)
cormat.sqrt <- eidecomp$vectors %*% L.sqrt %*% t(eidecomp$vectors)
Z <- matrix(rnorm(mydim[4] * (nf + 1)), nrow = mydim[4], ncol = (nf + 1))
Xw <- rep(1, mydim[4]) %*% t(mu) + Z %*% cormat.sqrt
Dmat <- Xw[, 1:nf]
# create a random four-way data tensor with D weights related to a response
Bmat <- matrix(runif(mydim[2] * nf), nrow = mydim[2], ncol = nf)
Cmat <- matrix(runif(mydim[3] * nf), nrow = mydim[3], ncol = nf)
nDd <- rep(c(10, 12, 14), length.out = mydim[4])
Gmat <- matrix(rnorm(nf * nf), nrow = nf)
Amat <- vector("list", mydim[4])
X <- Xmat <- Emat <- Amat
for (Dd in 1:mydim[4]) {
Amat[[Dd]] <- matrix(nf * rnorm(nDd[Dd]), nrow = nDd[Dd], ncol = nf)
Amat[[Dd]] <- svd(Amat[[Dd]], nv = 0)$u %*% Gmat
leftMat <- Amat[[Dd]] %*% diag(Dmat[Dd,])
Xmat[[Dd]] <- array(tcrossprod(leftMat, krprod(Cmat, Bmat)),
dim = c(nDd[Dd], mydim[2], mydim[3]))
Emat[[Dd]] <- array(rnorm(nDd[Dd] * mydim[2] * mydim[3]),
dim = c(nDd[Dd], mydim[2], mydim[3]))
X[[Dd]] <- Xmat[[Dd]] + Emat[[Dd]]
}
# create a multiclass response
stor <- matrix(rep(1, nrow(Xw)), nrow = nrow(Xw))
stor[which(Xw[, (nf + 1)] < (ymean - 0.4 * sd(Xw[, (nf + 1)])))] <- 2
stor[which(Xw[, (nf + 1)] > (ymean + 0.4 * sd(Xw[, (nf + 1)])))] <- 0
y <- factor(stor)
# initialize
alpha <- seq(0, 1, length = 2)
gamma <- c(0, 1)
cost <- c(0.1, 5)
ntree <- c(200, 300)
nodesize <- c(1, 2)
size <- c(1, 2)
decay <- c(0, 1)
rda.alpha <- seq(0.1, 0.9, length = 2)
delta <- c(0.1, 1)
method <- c("PLR", "SVM", "RF", "NN", "RDA")
family <- "multinomial"
parameters <- list(alpha = alpha, gamma = gamma, cost = cost, ntree = ntree,
nodesize = nodesize, size = size, decay = decay,
rda.alpha = rda.alpha, delta = delta)
model <- "parafac2"
nfolds <- 3
# constrain first mode weights to be orthogonal, fourth mode to be nonnegative
const <- c("orthog", "uncons", "uncons", "nonneg")
# fit Parafac2 model and use fourth mode weights to tune classification
# methods, to predict class labels, and to return classificaiton
# performance measures pooled across multiple train-test splits
output <- cpfa(x = X, y = y, model = model, nfac = nf, nrep = 2, ratio = 0.8,
nfolds = nfolds, method = method, family = family,
parameters = parameters, type.out = "descriptives",
seeds = NULL, plot.out = TRUE, parallel = FALSE, const = const)
# print performance measure means across train-test splits
output$descriptive$mean