xPredictROCR {Pi} | R Documentation

## Function to assess the prediction performance via ROC and Precision-Recall (PR) analysis

### Description

`xPredictROCR` is supposed to assess the prediction performance via Receiver Operating Characteristic (ROC) and Precision-Recall (PR) analysis. It requires three inputs: 1) Gold Standard Positive (GSP) targets; 2) Gold Standard Negative (GSN) targets; 3) prediction containing predicted targets and predictive scores.

### Usage

    xPredictROCR(prediction, GSP, GSN, rescale = TRUE, plot = c("none",
    "ROC",
    "PR"), verbose = TRUE, signature = TRUE)

### Arguments

`prediction` | a data frame containing predictions along with predictive scores. It has two columns: 1st column for target, 2nd column for predictive scores (the higher the better). Alternatively, it can be an object of class "pNode" (or "sTarget" or "dTarget") from which a data frame is extracted
`GSP` | a vector containing Gold Standard Positives (GSP)
`GSN` | a vector containing Gold Standard Negatives (GSN)
`rescale` | logical to indicate whether to linearly rescale predictive scores for GSP/GSN targets to the range [0,1]. By default, it sets to TRUE
`plot` | the way to plot performance curve. It can be 'none' for no curve returned, 'ROC' for ROC curve, and 'PR' for PR curve.
`verbose` | logical to indicate whether the messages will be displayed in the screen. By default, it sets to TRUE for display
`signature` | a logical to indicate whether the signature is assigned to the plot caption. By default, it sets TRUE showing which function is used to draw this graph

### Value

If plot is 'none' (by default), an object of class "pPerf", a list with following components:

- `PRS`: a data frame with 3 columns ('Precision', 'Recall' and 'Specificity')

- `AUROC`: a scalar value for ROC AUC

- `Fmax`: a scalar value for maximum F-measure

- `ROC_perf`: a ROCR performance-class object for ROC curve

- `PR_perf`: a ROCR performance-class object for PR curve

- `Pred_obj`: a ROCR prediction-class object (potentially used for calculating other performance measures)

If plot is 'ROC' or 'PR', it will return a ggplot object after being appended with the same components as mentioned above. If no GSP and/or GSN is predicted, it will return NULL

### Note

AUC: the area under ROC F-measure: the maximum of a harmonic mean between precision and recall along PR curve

### Examples

    ## Not run:
    # Load the library
    library(Pi)

    ## End(Not run)
    RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev"
    ## Not run:
    pPerf <- xPredictROCR(prediction, GSP, GSN)

    ## End(Not run)

[Package Pi version 1.6.2 Index]