Description Usage Arguments Value Author(s)
****** STILL CONTAINS BUGS (inverse ROC and PR) ****** This function quickly performs a cross-validated SVM classification on a data matrix.
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FeatureMatrix |
The matrix of Features (obtained by using the xcms::groupval function). Matrix has to have columns for features and rows for samples. |
GroupLabels |
The group labels. If not a factor a conversion will be applied. |
SampleLabels |
(optional) unique sample identifier. |
nFolds |
Number of cross validation folds. |
nSims |
Number of simulations (every simulation has different folds) |
plot.out |
Whether to print the ROC curve (default is TRUE). |
plot.type |
Type of plot output. "ROC" for receiver operator characteristic (default) or "PR" for precision-recall. |
nCPU |
The number of cores to use (default is the maximum amount available minus 2) |
plotcol |
(optional) colour to use for the plot |
svm.kernel |
The kernel to be used for the svm (default is linear) |
plottitle.extra |
Optional extra character string to be added to every plot title. |
prediction_prob |
Whether to take the values for the TRUE prediction (default) or the FALSE prediction. Choosing FALSE inverts the ROC curve. |
Nplotpoints |
The amount of points used to construct the plot. |
... |
Extra paremeters to be passed along to |
A ROC plot (if plot.out = TRUE) and a list with 2 elements: 1) a data frame with the ROC plot data and 2) a matrix with the variable importance for each cross validated simulation (nFolds * nSims times).
Charlie Beirnaert, charlie.beirnaert@uantwerpen.be
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