Predict Method for Tuning for Classification with Parallel Factor Analysis

Obtains predicted class labels from a 'tunecpfa' model object generated by function tunecpfa.

Usage

# S3 method for class 'tunecpfa'
predict(object, newdata = NULL, newdata.z = NULL, 
        method = NULL, type = c("response", "prob", "classify.weights"), 
        threshold = NULL, ...)

Arguments

object: A fit object of class 'tunecpfa' produced by function tunecpfa.
newdata: An optional two-way matrix, three-way array, or four-way data array used to predict Parafac, Parafac2, or PCA component weights using estimated Parafac, Parafac2, or PCA model component weights from the input object. For Parafac2, can be a list of length K where the k-th element is a matrix or three-way array associated with the k-th observation. Matrix, array, or list must contain only real numbers. Dimensions must match dimensions of original data for all modes except the classification mode. If omitted, the original data are used.
newdata.z: An optional two-way matrix containing additional features used to predict class labels. If omitted, and original z was NULL, defaults to NULL. Otherwise, if provided, this argument must have number of features equal to the number of columns of the original z. As such, the original z must not have been NULL.
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 selected, default is to use methods used in original tunecpfa run.
type: Character vector indicating type of prediction to return. Possible values include: (1) "response", returning predicted class labels; (2) "prob", returning predicted class probabilities; or (3) "classify.weights", returning predicted component weights used for classification in the specified component models. Must be specified.
threshold: For binary classification, value indicating prediction threshold over which observations are classified as the positive class. If not provided, calculates threshold using class proportions in original data. For multiclass classification, threshold is not currently implemented.
...: Additional predict arguments. Currently ignored.

Details

Predicts class labels for a binary or a multiclass outcome. Specifically, predicts component weights for one mode of a Parallel Factor Analysis-1 (Parafac) model, one mode of a Parallel Factor Analysis-2 (Parafac2) model, or scores from a PCA model, using new data and previously estimated mode weights from original data. Passes predicted component weights (or scores for PCA) to one or several classification methods as new data for predicting class labels.

Tuning parameters optimized by k-fold cross-validation are used for each classification method (see help for tunecpfa). If not supplied in argument threshold, prediction threshold for all classification methods is calculated using proportions of class labels for original data in the binary case (and the positive class proportion is set as the threshold). For multiclass case, class with highest probability is chosen.

Value

Returns one of the following, depending on the choice for argument type:

type = "response": A data frame containing predicted class labels for each component model and classification method selected (see argument type). Number of columns is equal to number of methods times number of component models. Number of rows is equal to number of predicted observations.
type = "prob": A list containing predicted probabilities for each component model and classification method selected (see argument type). The number of list elements is equal to the number of methods times the number of component models.
type = "classify.weights": List containing predicted component weights for each component model. Length is equal to number of component models that were fit.

Note

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

Author

Matthew Asisgress <mattgress@protonmail.ch>

References

See help file for function tunecpfa for a list of references.

Examples

########## Parafac example with 3-way array and binary response ##########
if (FALSE) { # \dontrun{
# set seed and simulate a three-way array related to a binary response
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 C mode weights matrix
cormat <- matrix(c(1, .6, .6, .6, 1, .6, .6, .6, 1), nrow = 3, ncol = 3)

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

# initialize
alpha <- seq(0, 1, length = 2)
gamma <- c(0, 0.01)
cost <- c(1, 2)
method <- c("PLR", "SVM")
family <- "binomial"
parameters <- list(alpha = alpha, gamma = gamma, cost = cost)
model <- "parafac"
nfolds <- 3
nstart <- 3

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

# fit Parafac models and use third mode to tune classification methods
tune.object <- tunecpfa(x = data$X[, , 1:80], y = data$y[1:80], 
                        model = model, nfac = 3, nfolds = nfolds, 
                        method = method, family = family, 
                        parameters = parameters, parallel = FALSE, 
                        const = const, nstart = nstart)
                    
# predict class labels
predict.labels <- predict(object = tune.object, newdata = data$X[, , 81:100], 
                          type = "response")

# print predicted labels
predict.labels
} # }