plotEmpiricalCDF

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 ecdf for the variable with the lowest p-value.

plotEmpiricalCDF(DDiagnostics)

By default, plotEmpiricalCDF plots the ecdf of the baseline and target data for the variable with the lowest p-value, which is x2 in this case. You can see the difference between the two empirical cumulative distribution functions. The plot also displays the p-value and the drift status for variable x2.

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 ecdf for the third variable.

plotEmpiricalCDF(DDiagnostics,Variable="x3")

plotEmpiricalCDF plots the ecdf for the baseline and target data. The function also displays the estimated p-value and the drift status for the specified variable.

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 columns 10 to 15.

baseline = feat(1:250,10:15);
target = feat(251:500,10:15);

Test for drift on all variables.

DDiagnostics = detectdrift(baseline,target)

DDiagnostics = 
  DriftDiagnostics

              VariableNames: ["x1"    "x2"    "x3"    "x4"    "x5"    "x6"]
       CategoricalVariables: []
                DriftStatus: ["Drift"    "Stable"    "Stable"    "Drift"    "Stable"    "Warning"]
                    PValues: [1.0000e-03 0.5080 0.2370 1.0000e-03 0.5370 0.0820]
        ConfidenceIntervals: [2×6 double]
    MultipleTestDriftStatus: "Drift"
             DriftThreshold: 0.0500
           WarningThreshold: 0.1000


  Properties, Methods

The drift status for variables x4 and x6 is Drift and Warning, respectively. Plot the ecdf values for x4 and x6 in a tiled layout.

tiledlayout(1,2);
ax1 = nexttile;
plotEmpiricalCDF(DDiagnostics,ax1,Variable="x4")
ax2= nexttile;
plotEmpiricalCDF(DDiagnostics,ax2,Variable="x6")

There is a greater difference between the ecdf of the baseline and target data for variable x4. The detectdrift function detects the shift for variable x4.