getPatientPredictions {netDx} | R Documentation

## Calculates patient-level classification accuracy across train/test splits

### Description

Calculates patient-level classification accuracy across train/test splits

### Usage

    getPatientPredictions(predFiles, pheno, plotAccuracy = FALSE)

### Arguments

`predFiles` | (char) vector of paths to all test predictions (e.g. 100 files for a 100 train/test split design). Alternately, the user can also provide a single directory name, and allow the script to retrieve prediction files. Format is 'rootDir/rngX/predictionResults.txt'
`pheno` | (data.frame) ID=patient ID, STATUS=ground truth (known class label). This table is required to get the master list of all patients, as not every patient is classified in every split.
`plotAccuracy` | (logical) if TRUE, shows fraction of times patient is misclassified, using a dot plot

### Details

Takes all the predictions across the different train/test splits, and for each patient, generates a score indicating how many times they were classified by netDx as belonging to each of the classes. The result is that we get a measure of individual classification accuracy across the different train/test splits.

### Value

(list) of length 2. 1) (data.frame) rows are patients, (length(predFiles)+2) columns. Columns seq_len(length(predFiles)): Predicted labels for a given split (NA if patient was training sample for the split). Column (length(predFiles)+1): split, value is NA. Columns are : ID, REAL_STATUS, predStatus1,... predStatusN. Side effect of plotting a dot plot of and the value is '

### Examples

    inDir <- system.file("extdata","example_output",package="netDx")
    data(pheno)
    all_rngs <- list.dirs(inDir, recursive = FALSE)
    all_pred_files <- unlist(lapply(all_rngs, function(x) {
        paste(x, 'predictionResults.txt',
    	sep = getFileSep())}))
    pred_mat <- getPatientPredictions(all_pred_files, pheno)

[Package netDx version 1.0.3 Index]