print.tunecpfa {cpfa}

Print Method for Tuning for Classification with Parallel Factor Analysis

Description

Prints summary of results from a 'tunecpfa' model object obtained using function tunecpfa.

Usage

## S3 method for class 'tunecpfa'
print(x, ...)

Arguments

x

A fit object of class 'tunecpfa' from function tunecpfa.

...

Additional print arguments.

Details

Prints names of the models and methods used to create the input 'tunecpfa' model object. Prints misclassification error rates and estimation times in seconds.

Value

Returns a summary of the 'tunecpfa' model object.

Author(s)

Matthew Snodgress <[e-mail]>

References

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

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, 80)
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)
method <- c("PLR", "SVM")
family <- "multinomial"
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 = X, y = y, model = model, nfac = nf, 
                        nfolds = nfolds, method = method, family = family, 
                        parameters = parameters, parallel = FALSE, 
                        const = const, nstart = nstart)
                         
# print summary of output
print(tune.object)