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

Display the 95% confidence intervals for the estimated p-values.

DDiagnostics.ConfidenceIntervals

ans = 2×3

    0.3547    0.0016    0.0739
    0.4160    0.0116    0.1106

Plot the drift status for all three variables.

plotDriftStatus(DDiagnostics)

plotDriftStatus plots the confidence intervals for the estimated p-values, using error bars. The function also compares the confidence bounds against the drift and warning thresholds, and indicates the drift status of each variable using different colors. The lower confidence bound of the p-value for the first variable is higher than the warning threshold. Therefore, the drift status for the first variable is Stable, indicated by the color blue. The lower confidence bound of the p-value for the third variable is lower than the warning threshold, but higher than the drift threshold. Therefore, the drift status for the third variable is Warning, and is indicated by the color yellow. The upper confidence bound of the p-value for the second variable is lower than the drift threshold. Therefore, the drift status for the third variable is Drift and is indicated by the color orange.

Load the sample data.

load humanactivity

For details on the data set, enter Description at the command line.

Assign the first 250 observations as baseline data and the next 250 as target data for the first 15 variables.

baseline = feat(1:250,1:15);
target = feat(251:500,1:15);

Test for drift on all variables.

DDiagnostics = detectdrift(baseline,target)

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"    "x4"    "x5"    "x6"    "x7"    "x8"    "x9"    "x10"    "x11"    "x12"    "x13"    "x14"    "x15"]
       CategoricalVariables: []
                DriftStatus: ["Drift"    "Drift"    "Drift"    "Drift"    "Drift"    "Drift"    "Drift"    "Stable"    "Stable"    "Drift"    "Stable"    "Stable"    "Drift"    "Stable"    "Warning"]
                    PValues: [1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 1.0000e-03 0.8630 0.7260 1.0000e-03 0.4960 0.2490 1.0000e-03 0.5740 0.0940]
        ConfidenceIntervals: [2×15 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

Display the 95% confidence intervals of the p-values for variables 10 to 15.

DDiagnostics.ConfidenceIntervals(:,10:15)

ans = 2×6

    0.0000    0.4646    0.2225    0.0000    0.5427    0.0766
    0.0056    0.5275    0.2770    0.0056    0.6049    0.1138

Plot the drift status for variables 10 to 15.

plotDriftStatus(DDiagnostics,Variables=(10:15))

Load the sample data.

load humanactivity

For details on the data set, enter Description at the command line.

Assign the first 250 observations as baseline data and the next 250 as target data for the first 15 variables.

baseline = feat(1:250,1:15);
target = feat(251:500,1:15);

Test for drift on all variables.

DDiagnostics = detectdrift(baseline,target);

Plot the drift status for all variables and return the ErrorBar and ConstantLine objects.

[EB,CL] = plotDriftStatus(DDiagnostics)

EB = 
  3×1 ErrorBar array:

  ErrorBar    (Stable)
  ErrorBar    (Warning)
  ErrorBar    (Drift)

CL = 
  2×1 ConstantLine array:

  ConstantLine
  ConstantLine

EB is an array of ErrorBar objects and CL is an array of ConstantLine objects. You can change the appearance of the plot by accessing the properties of these objects. Change the color of the error bars and markers for status Stable to green. Change the color of the drift threshold line, error bars, and markers for the status Drift to magenta.

EB(1).Color = [0 1 0];
EB(1).MarkerFaceColor = [0 1 0];
EB(1).MarkerEdgeColor = [0 1 0];
EB(3).Color = [1 0 1];
EB(3).MarkerFaceColor = [1 0 1];
EB(3).MarkerEdgeColor = [1 0 1];
CL(2).Color = [1 0 1];

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