R: Plot performance evaluation measures

plot {precrec} | R Documentation

## Plot performance evaluation measures

### Description

The `plot` function creates a plot of performance evaluation measures.

### Usage

    ## S3 method for class 'sscurves'
    plot(x, y = NULL, ...)

    ## S3 method for class 'mscurves'
    plot(x, y = NULL, ...)

    ## S3 method for class 'smcurves'
    plot(x, y = NULL, ...)

    ## S3 method for class 'mmcurves'
    plot(x, y = NULL, ...)

    ## S3 method for class 'sspoints'
    plot(x, y = NULL, ...)

    ## S3 method for class 'mspoints'
    plot(x, y = NULL, ...)

    ## S3 method for class 'smpoints'
    plot(x, y = NULL, ...)

    ## S3 method for class 'mmpoints'
    plot(x, y = NULL, ...)

### Arguments

1. `x` | An `S3` object generated by `evalmod`. The `plot` function accepts the following `S3` objects. ROC and Precision-Recall curves (mode = "rocprc")
   `S3` object | # of models | # of test datasets
   sscurves | single | single
   mscurves | multiple | single
   smcurves | single | multiple
   mmcurves | multiple | multiple
2. Basic evaluation measures (mode = "basic")
   `S3` object | # of models | # of test datasets
   sspoints | single | single
   mspoints | multiple | single
   smpoints | single | multiple
   mmpoints | multiple | multiple
See the Value section of `evalmod` for more details.
`y` | Equivalent with `curvetype`.
  1. `...` | All the following arguments can be specified. curvetype ROC and Precision-Recall curves (mode = "rocprc")
     curvetype | description
     ROC | ROC curve
     PRC | Precision-Recall curve
  2. Multiple `curvetype` can be combined, such as `c("ROC", "PRC")`. Basic evaluation measures (mode = "basic")
     curvetype | description
     error | Normalized ranks vs. error rate
     accuracy | Normalized ranks vs. accuracy
     specificity | Normalized ranks vs. specificity
     sensitivity | Normalized ranks vs. sensitivity
     precision | Normalized ranks vs. precision
     mcc | Normalized ranks vs. Matthews correlation coefficient
     fscore | Normalized ranks vs. F-score
Multiple `curvetype` can be combined, such as `c("precision", "sensitivity")`. type A character to specify the line type as follows. "l" lines "p" points "b" both lines and points show_cb A Boolean value to specify whether point-wise confidence bounds are drawn. It is effective only when `calc_avg` of the `evalmod` function is set to `TRUE`. raw_curves A Boolean value to specify whether raw curves are shown instead of the average curve. It is effective only when `raw_curves` of the `evalmod` function is set to `TRUE`. show_legend A Boolean value to specify whether the legend is shown.

### Value

The `plot` function shows a plot and returns NULL.

### See Also

`evalmod` for generating an `S3` object. `autoplot` for plotting the equivalent curves with ggplot2.

### Examples

    ## Not run:
    ##################################################
    ### Single model & single test dataset
    ###

    ## Load a dataset with 10 positives and 10 negatives
    data(P10N10)

    ## Generate an sscurve object that contains ROC and Precision-Recall curves
    sscurves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)

    ## Plot both ROC and Precision-Recall curves
    plot(sscurves)

    ## Plot a ROC curve
    plot(sscurves, curvetype = "ROC")

    ## Plot a Precision-Recall curve
    plot(sscurves, curvetype = "PRC")

    ## Generate an sspoints object that contains basic evaluation measures
    sspoints <- evalmod(mode = "basic", scores = P10N10$scores,
                        labels = P10N10$labels)

    ## Plot normalized ranks vs. basic evaluation measures
    plot(sspoints)

    ## Plot normalized ranks vs. precision
    plot(sspoints, curvetype = "precision")

    ##################################################
    ### Multiple models & single test dataset
    ###

    ## Create sample datasets with 100 positives and 100 negatives
    samps <- create_sim_samples(1, 100, 100, "all")
    mdat <- mmdata(samps[["scores"]], samps[["labels"]],
                   modnames = samps[["modnames"]])

    ## Generate an mscurve object that contains ROC and Precision-Recall curves
    mscurves <- evalmod(mdat)

    ## Plot both ROC and Precision-Recall curves
    plot(mscurves)

    ## Hide the legend
    plot(mscurves, show_legend = FALSE)

    ## Generate an mspoints object that contains basic evaluation measures
    mspoints <- evalmod(mdat, mode = "basic")

    ## Plot normalized ranks vs. basic evaluation measures
    plot(mspoints)

    ## Hide the legend
    plot(mspoints, show_legend = FALSE)

    ##################################################
    ### Single model & multiple test datasets
    ###

    ## Create sample datasets with 100 positives and 100 negatives
    samps <- create_sim_samples(10, 100, 100, "good_er")
    mdat <- mmdata(samps[["scores"]], samps[["labels"]],
                   modnames = samps[["modnames"]],
                   dsids = samps[["dsids"]])

    ## Generate an smcurve object that contains ROC and Precision-Recall curves
    smcurves <- evalmod(mdat, raw_curves = TRUE)

    ## Plot average ROC and Precision-Recall curves
    plot(smcurves, raw_curves = FALSE)

    ## Hide confidence bounds
    plot(smcurves, raw_curves = FALSE, show_cb = FALSE)

    ## Plot raw ROC and Precision-Recall curves
    plot(smcurves, raw_curves = TRUE, show_cb = FALSE)

    ## Generate an smpoints object that contains basic evaluation measures
    smpoints <- evalmod(mdat, mode = "basic")

    ## Plot normalized ranks vs. average basic evaluation measures
    plot(smpoints)

    ##################################################
    ### Multiple models & multiple test datasets
    ###

    ## Create sample datasets with 100 positives and 100 negatives
    samps <- create_sim_samples(10, 100, 100, "all")
    mdat <- mmdata(samps[["scores"]], samps[["labels"]],
                   modnames = samps[["modnames"]],
                   dsids = samps[["dsids"]])

    ## Generate an mscurve object that contains ROC and Precision-Recall curves
    mmcurves <- evalmod(mdat, raw_curves = TRUE)

    ## Plot average ROC and Precision-Recall curves
    plot(mmcurves, raw_curves = FALSE)

    ## Show confidence bounds
    plot(mmcurves, raw_curves = FALSE, show_cb = TRUE)

    ## Plot raw ROC and Precision-Recall curves
    plot(mmcurves, raw_curves = TRUE)

    ## Generate an mmpoints object that contains basic evaluation measures
    mmpoints <- evalmod(mdat, mode = "basic")

    ## Plot normalized ranks vs. average basic evaluation measures
    plot(mmpoints)

    ##################################################
    ### N-fold cross validation datasets
    ###

    ## Load test data
    data(M2N50F5)

    ## Speficy nessesary columns to create mdat
    cvdat <- mmdata(nfold_df = M2N50F5, score_cols = c(1, 2),
                    lab_col = 3, fold_col = 4,
                    modnames = c("m1", "m2"), dsids = 1:5)

    ## Generate an mmcurve object that contains ROC and Precision-Recall curves
    cvcurves <- evalmod(cvdat)

    ## Average ROC and Precision-Recall curves
    plot(cvcurves)

    ## Show confidence bounds
    plot(cvcurves, show_cb = TRUE)

    ## Generate an mmpoints object that contains basic evaluation measures
    cvpoints <- evalmod(cvdat, mode = "basic")

    ## Normalized ranks vs. average basic evaluation measures
    plot(cvpoints)

    ## End(Not run)

[Package precrec version 0.9.1 Index]