Description Usage Arguments Examples
This function makes a snv prediction model on one set of data and then applies it to another, typically one without the ground truth information required to build a cell type specific model.
1 2 | snv.cross.predict(train_data, test_data, classifiers = NULL,
threshold = NULL)
|
train_data |
Output from snv.bench. A data frame containing all derived snv classifiers with ground truths. |
test_data |
Output from snv.bench. A data frame containing all derived snv classifiers (where training_set=FALSE, though not essential). |
classifiers |
Output from snv.bench. A vector to keep low correlating classifiers only, all used if NULL.If applied to the training data, it will need to be used here. |
threshold |
Defaults to 0.5. Values 0 to 1 can be input to tune predictions for specificity or sensitivity. |
1 | snv.cross.predict(train_data,test_data,classifiers,threshold)
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