MetricPairPlot {WVPlots}

Plot the relationship between two metrics.

Description

Plot the relationship between two metrics.

Usage

MetricPairPlot(
  frame,
  xvar,
  truthVar,
  title,
  ...,
  x_metric = "false_positive_rate",
  y_metric = "true_positive_rate",
  truth_target = TRUE,
  points_to_plot = NULL,
  linecolor = "black"
)

Arguments

frame

data frame to get values from

xvar

name of the independent (input or model) column in frame

truthVar

name of the column to be predicted

title

title to place on plot

...

no unnamed argument, added to force named binding of later arguments.

x_metric

metric to be plotted. See Details for the list of allowed metrics

y_metric

metric to be plotted. See Details for the list of allowed metrics

truth_target

truth value considered to be positive.

points_to_plot

how many data points to use for plotting. Defaults to NULL (all data)

linecolor

character: name of line color

Details

Plots two classifier metrics against each other, showing achievable combinations of performance metrics. For example, plotting true_positive_rate vs false_positive_rate recreates the ROC plot.

MetricPairPlot can plot a number of metrics. Some of the metrics are redundant, in keeping with the customary terminology of various analysis communities.

• sensitivity: fraction of true positives that were predicted to be true (also known as the true positive rate)

• specificity: fraction of true negatives to all negatives (or 1 - false_positive_rate)

• precision: fraction of predicted positives that are true positives

• recall: same as sensitivity or true positive rate

• accuracy: fraction of items correctly decided

• false_positive_rate: fraction of negatives predicted to be true over all negatives

• true_positive_rate: fraction of positives predicted to be true over all positives

• false_negative_rate: fraction of positives predicted to be all false over all positives

• true_negative_rate: fraction negatives predicted to be false over all negatives

points_to_plot specifies the approximate number of datums used to create the plots as an absolute count; for example setting points_to_plot = 200 uses approximately 200 points, rather than the entire data set. This can be useful when visualizing very large data sets.

See Also

ThresholdPlot, PRTPlot, ROCPlot, PRPlot

Examples

if (requireNamespace('data.table', quietly = TRUE)) {
	# don't multi-thread during CRAN checks
		data.table::setDTthreads(1)
}

# data with two different regimes of behavior
d <- rbind(
  data.frame(
    x =  rnorm(1000),
    y = sample(c(TRUE, FALSE), prob = c(0.02, 0.98), size = 1000, replace = TRUE)),
  data.frame(
    x =  rnorm(200) + 5,
    y = sample(c(TRUE, FALSE), size = 200, replace = TRUE))
)

# Sensitivity/Specificity examples
MetricPairPlot(d, 'x', 'y',
   x_metric = 'false_positive_rate',
   y_metric = 'true_positive_rate',
   truth_target = TRUE,
   title = 'ROC equivalent')
if(FALSE) {
ThresholdPlot(d, 'x', 'y',
   title = 'Sensitivity/Specificity',
   metrics = c('sensitivity', 'specificity'),
   truth_target = TRUE)
ROCPlot(d, 'x', 'y',
   truthTarget = TRUE,
   title = 'ROC example')

# Precision/Recall examples
ThresholdPlot(d, 'x', 'y',
   title = 'precision/recall',
   metrics = c('recall', 'precision'),
   truth_target = TRUE)
MetricPairPlot(d, 'x', 'y',
   x_metric = 'recall',
   y_metric = 'precision',
   title = 'recall/precision',
   truth_target = TRUE)
PRPlot(d, 'x', 'y',
   truthTarget = TRUE,
   title = 'p/r plot')
}