Generate baseline and target data with three variables, where the distribution parameters of the second and third variables change for the target data.

rng('default') % For reproducibility
baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1),betarnd(1,2,100,1)];
target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1),betarnd(1.7,2.8,100,1)];

Perform permutation testing for all variables to check for any drift between the baseline and target data.

DDiagnostics = detectdrift(baseline,target)

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"    "Warning"]
                    PValues: [0.3850 0.0050 0.0910]
        ConfidenceIntervals: [2×3 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

Plot the permutation results for the default variable.

plotPermutationResults(DDiagnostics)

By default, plotPermutationResults plots a histogram of the metric values computed in permutation testing for the variable with the lowest p-value, which is x2 in this case. The function includes the metric threshold value (the initial metric value computed by detectdrift using the baseline and target data) on the histogram, so you can see the values that are greater than or equal to the threshold. plotPermutationResults also displays the p-value and the drift status for the variable, and the metric that you specify to use for permutation testing in the call to detectdrift. In this example, no metric is specified, so detectdrift uses the default metric (Wasserstein) for continuous variables.

Generate baseline and target data with three variables, where the distribution parameters of the second and third variables change for the target data.

rng('default') % For reproducibility
baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1),betarnd(1,2,100,1)];
target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1),betarnd(1.7,2.8,100,1)];

Perform permutation testing for all variables to check for any drift between the baseline and target data. Use the Energy metric for all variables.

DDiagnostics = detectdrift(baseline,target,ContinuousMetric="energy")

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"    "Warning"]
                    PValues: [0.3790 0.0110 0.0820]
        ConfidenceIntervals: [2×3 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

Display the 95% confidence bounds for the p-values.

DDiagnostics.ConfidenceIntervals

ans = 2×3

    0.3488    0.0055    0.0657
    0.4099    0.0196    0.1008

Plot the permutation results for the third variable.

plotPermutationResults(DDiagnostics,Variable=3)

Generate baseline and target data with three variables, where the distribution parameters of the second and third variables change for the target data.

rng('default') % For reproducibility
baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1),betarnd(1,2,100,1)];
target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1),betarnd(1.7,2.8,100,1)];

Perform permutation testing for all variables to check for any drift between the baseline and target data. Use the Energy metric for all variables.

DDiagnostics = detectdrift(baseline,target,ContinuousMetric="energy")

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"    "Warning"]
                    PValues: [0.3790 0.0110 0.0820]
        ConfidenceIntervals: [2x3 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000

Plot the permutation results for variables x1 and x2 in a tiled layout.

tiledlayout(2,1);
ax1 = nexttile;
plotPermutationResults(DDiagnostics,ax1,Variable="x1")
ax2 = nexttile;
plotPermutationResults(DDiagnostics,ax2,Variable="x2")

Plot the permutation results for variables x1 and x3 in a tiled layout.

tiledlayout(2,1);
ax1 = nexttile;
plotPermutationResults(DDiagnostics,ax1,Variable="x1")
ax3= nexttile;
plotPermutationResults(DDiagnostics,ax3,Variable="x3")

Generate baseline and target data with three variables, where the distribution parameters of the second and third variables change for the target data.

rng('default') % For reproducibility
baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1),betarnd(1,2,100,1)];
target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1),betarnd(1.7,2.8,100,1)];

Perform permutation testing for all variables to check for any drift between the baseline and target data. Use the Energy distance as the metric.

DDiagnostics = detectdrift(baseline,target,ContinuousMetric="energy")

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"]
       CategoricalVariables: []
                DriftStatus: ["Stable"    "Drift"    "Warning"]
                    PValues: [0.3790 0.0110 0.0820]
        ConfidenceIntervals: [2×3 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

Plot the permutation results for the third variable.

[H,CL] = plotPermutationResults(DDiagnostics,Variable=3)

H = 
  2×1 Histogram array:

  Histogram
  Histogram

CL = 
  ConstantLine with properties:

    InterceptAxis: 'x'
            Value: 0.1012
            Color: [0.1500 0.1500 0.1500]
        LineStyle: ':'
        LineWidth: 3
            Label: ''
      DisplayName: ''

  Show all properties

Change the histogram bar colors to blue and the threshold line color to red.

H(1).FaceColor = "b";
CL.Color = "r";

You can also access and modify properties by double-clicking H or CL in the Workspace to open and use the Property Inspector.