Description Usage Arguments Value Author(s) See Also Examples
Function Validation performs a cross-validated classification using three different classifiers: KNN, PLSDA and SVM. The output comes in a table with the classification ratio and its standard error. The classification ratio is weighted to take into account the different sample number of each class.
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Iterations |
Number of iterations to be performed in the classifications. For each iteration a new training group is randomly chosen. |
MAIT.object |
A MAIT-class object where significant features have already been found. |
trainSamples |
Number of samples per class to construct the train dataset. |
PCAscale |
If method="PCA" and PCAscale is set to TRUE, then the data is scaled following the |
PCAcenter |
If method="PCA" and PCAscale is set to TRUE, then the data is centered following the |
RemoveOnePeakSpectra |
If it is set to TRUE, all the one-peak spectra are deleted from the dataSet and the resulting |
tuneSVM |
If it is set to TRUE, a tune of parameters is performed before the SVM calculus. |
scale |
If it is set to TRUE, the data is scaled through the spectral mean value. Set to TRUE by default. |
The numerical results of the classification per class and per classifier are saved in a MAIT-class object. Additionally, a table is also included in the output both in the list (field table) and printed as a csv file in the folder (working directory)/Validation. A boxplot is also printed as a png in the same folder showing the differences between classifiers. The confusion matrices of each iteration and classifier are also stored as csv files.
Francesc Fernandez, francesc.fernandez.albert@upc.edu
peakAggregation
spectralAnova
spectralTStudent
spectralSigFeatures
1 2 3 | data(MAIT_sample)
MAIT<-spectralSigFeatures(MAIT,p.adj="fdr",parametric=TRUE)
MAIT <- Validation(Iterations = 20, trainSamples= 15, MAIT.object = MAIT)
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