print.tunecpfa {cpfa} | R Documentation |
Print Method for Tuning for Classification with Parallel Factor Analysis
Description
Prints summary of a 'tunecpfa' model object generated by function
tunecpfa
.
Usage
## S3 method for class 'tunecpfa'
print(x, ...)
Arguments
x |
A fit object of class 'tunecpfa' from function |
... |
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 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 ##########
## Not run:
# set seed and simulate a three-way array connected to a binary response
set.seed(5)
cormat <- matrix(c(1, .35, .35, .35, 1, .35, .35, .35, 1), nrow = 3, ncol = 3)
data <- simcpfa(arraydim = c(10, 11, 80), model = "parafac", nfac = 3,
nclass = 2, nreps = 1e2, onreps = 10, corresp = rep(.75, 3),
meanpred = rep(2, 3), modes = 3, corrpred = cormat)
# 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$X, y = as.factor(data$y), model = model,
nfac = 3, nfolds = nfolds, method = method,
family = family, parameters = parameters,
parallel = FALSE, const = const, nstart = nstart)
# print summary of output
print(tune.object)
## End(Not run)
[Package cpfa version 1.2-1 Index]