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

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

Perform permutation testing for any drift between the baseline and the target data.

DDiagnostics = detectdrift(baseline,target);

Display the summary of the drift diagnostics.

summary(DDiagnostics)

    Multiple Test Correction Drift Status: Drift

          DriftStatus    PValue      ConfidenceInterval  
          ___________    ______    ______________________

    x1     "Stable"      0.285       0.25719      0.31408
    x2     "Drift"       0.003     0.0006191     0.008742

summary displays the multiple test correction drift status above the summary table. detectdrift uses the default multiple test correction method, Bonferroni, which determines that the drift status for the overall data is Drift. The summary table has two rows, one for each variable, and three columns containing the drift status, estimated p-value, and 95% confidence bounds for the estimated p-values. detectdrift identifies the drift status as stable for the first variable, and detects the drift in the distribution for the second variable. The upper confidence bound for the second variable is lower than the default drift threshold of 0.05, so the drift status for this variable is Drift.

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

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

Perform permutation testing for any drift between the baseline and target data.

DDiagnostics = detectdrift(baseline,target);

Save the summary of the drift diagnostics in the table S.

S = summary(DDiagnostics)

S=3×3 table
                    DriftStatus    PValue      ConfidenceInterval  
                    ___________    ______    ______________________

    x1               "Stable"      0.285       0.25719      0.31408
    x2               "Drift"       0.003     0.0006191     0.008742
    MultipleTest     "Drift"         NaN           NaN          NaN

When you save the results in a table, summary stores the multiple test correction drift status in a row MultipleTest below the variables. The multiple test correction has no p-value or confidence interval, so the function stores NaNs.

If you set EstimatePValues to false in the call to detectdrift, the software does not perform any estimation or confidence interval computation. In this case, S stores the name and initial value of the metric you specify for each variable in the call to detectdrift.

DDiagnostics = detectdrift(baseline,target,EstimatePValues=false);
S = summary(DDiagnostics)

S=2×2 table
          MetricValue       Metric    
          ___________    _____________

    x1      0.22381      "Wasserstein"
    x2      0.36879      "Wasserstein"