selectQ | R Documentation |
This function applies DB-CSP and classification with different dimensions to see which gets the best outcomes.
selectQ( object, Q = c(1, 2, 3, 5, 10, 15), train_size = 0.75, CV = FALSE, folds = 10, seed = NULL ) ## S4 method for signature 'dbcsp' selectQ( object, Q = c(1, 2, 3, 5, 10, 15), train_size = 0.75, CV = FALSE, folds = 10, seed = NULL )
object |
object of class |
Q |
list of integers which represents the dimensions to use, by default |
train_size |
float between 0.0 and 1.0 representing the proportion of the dataset to include in the train split, by default |
CV |
logical indicating if a cross validation must be performed or not (if TRUE, train_size is not used), by default |
folds |
integer, number of folds to use if CV is performed. |
seed |
numeric value, by default |
A data.frame
including the dimensions and their corresponding accuracy values.
If CV=TRUE
, for each dimension, the standard deviation of the accuracy values of the folds is also included in the data frame.
dbcsp
, print
, summary
, train
, predict
, plot
, boxplot
# Read data from 2 classes x <- AR.data$come y <- AR.data$five mydbcsp <- new("dbcsp", X1 = x, X2 = y) result <- selectQ(mydbcsp) print(result)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.