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)

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