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An object of class dbcsp. 'dbcsp' stands for Distance-Based Common Spatial Patterns. The object includes the Common Spatial Patterns filter obtained with the input lists and using the distance method indicated.
The output is a list containing this information (object@out
):
vectors
The projection vectors obtained after applying CSP.
eig
The eigenvalues obtained after applying CSP.
proy
The variance values of the proyected signals obtained after applying CSP.
And if training=TRUE
the following values are also saved:
acc
The mean accuracy value obtained for training data applying cross validation.
used_folds
List of the folds used in the cross validation.
folds_acc
Accuracy values for each of the folds of the cross validation.
model
The trained LDA classifier.
selected_q
The number of vectors used when training.
X1
list of matrices for data class 1.
X2
list of matrices for data class 2.
q
integer value indicating the number of vectors used in the projection, by default q=15
.
labels
vector of two strings indicating labels names, by default names of variables X1 and X2.
type
sets the type of distance to be considered, by default type='EUCL'
.The supported distances are these ones
Included in TSdist: infnorm, ccor, sts, lb.keogh, edr, erp, lcss, fourier, tquest, dissim, acf, pacf, ar.lpc.ceps, ar.mah, ar.mah.statistic, ar.mah.pvalue, ar.pic, cdm, cid, cor, cort, int.per, per, mindist.sax, ncd, pred, spec.glk, spec.isd, spec.llr, pdc, frechet, tam.
Included in parallelDist: bhjattacharyya, bray, canberra, chord, divergence, dtw, euclidean, fJaccard, geodesic, hellinger, kullback, mahalanobis, manhattan, maximum, minkowski, podani, soergel, wave, whittaker.
w
weight for the distances mixture D_mixture = w*D_euclidean + (1-w)*D_type, by default w=0.5
.
mixture
logical value indicating whether to use distances mixture or not (EUCL + other), by default mixture=FALSE
.
training
logical value indicating whether to perform the training or not.
fold
integer value, by default fold=10
. It controls the number of partitions when training.
If fold==1
a train/test split is performed, with p=0.2 for test indices.
seed
numeric value, by default seed=NULL
. Set a seed in case the results want to be replicable.
eig.tol
numeric value, by default eig.tol=1e-06
, tolerance to convert distance matrix to be definite positive.
verbose
logical
out
list containing the output.
dbcsp
, print
, summary
, train
, selectQ
, predict
, plot
, boxplot
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