Groups, prediction and segmentation · Tutorial 24 of 41

FIMIX-PLS

Does finite-mixture PLS reveal latent customer classes with distinct renewal behavior?

Supported in 2.62.8

Purpose and applicability

Does finite-mixture PLS reveal latent customer classes with distinct renewal behavior?

No completed-result prerequisite is required for the first run.

Do not use it merely because it is available

Use FIMIX-PLS only when its estimand, data roles and assumptions answer the stated research question. Choose a related estimator or diagnostic when the intended outcome is not among the documented outputs below.

Study preparation

Teaching data
subscription-segmentation.csv
Observations
600
Study type
Deterministic synthetic teaching study
Calculate route
Calculate → FIMIX-PLS in the shared MultiMod workspace

Open the data dictionary and variable-role reference

Model

Ease, Quality, Value, Satisfaction, and Renewal, each measured by its three matching indicators

Ease, Quality, and Value → Satisfaction; Ease → Renewal; Satisfaction → Renewal

Exact workflow

  1. Download subscription-segmentation.csv and the prepared subscription-segmentation.qpls project. Keep both files in a writable study folder.

  2. Start QuickPLS, choose Open Project, and open the prepared project. Its saved model is already arranged and fitted.

  3. Open Data and confirm that the study contains 600 observations. Return to the model or calculation workspace.

  4. Confirm the saved model specification: Ease, Quality, Value, Satisfaction, and Renewal, each measured by its three matching indicators. Structural specification: Ease, Quality, and Value → Satisfaction; Ease → Renewal; Satisfaction → Renewal.

  5. Confirm that construct labels, indicators, and arrows are readable. The supplied project is saved after Arrange and Fit; do not rearrange it before the tutorial run.

  6. Choose Calculate, then use Calculate → FIMIX-PLS in the shared MultiMod workspace.

  7. Review the essential settings listed below. Open Advanced settings only when the design requires a non-default option.

  8. Resolve actionable blockers, review applicability warnings, and leave Open Results when finished selected.

  9. Choose Start calculation. Wait for Completed, then confirm that Results opens for this method.

  10. Inspect the named outputs below in order. Use Save Report, table Copy, or Export as required, then save and reopen the project to confirm the result remains available.

Essential and advanced settings

  • Exclude reference_segment from estimation
  • Evaluate the bounded K = 2–3 candidate plan with multiple starts

Keep other advanced controls at their documented defaults unless the study design requires a justified change. Record every non-default value in the research log.

Results to inspect

  1. Candidate information criteria
  2. Likelihood history
  3. Posterior memberships
  4. Assignments and segment shares

Interpretation guidance

For FIMIX-PLS, interpret the listed outputs together with the prerequisite result, the selected settings, model assumptions and data quality. The supplied values are instructional; they are not validation against an external paper or another software package.

Reporting guidance

Report the QuickPLS version, FIMIX-PLS route, sample size, model or variable roles, preprocessing, essential settings, non-default advanced settings, and the named primary outputs. Retain the project, data checksum and exported table used in the manuscript.

Common mistakes and recovery

  • Running the method before completing its prerequisite calculation.
  • Changing the data, model or variable roles after the prerequisite result was saved.
  • Treating an unavailable or not-applicable value as zero.
  • Reporting an estimate without its method-appropriate uncertainty or diagnostic context.

Open calculation and Results troubleshooting

Screen-by-screen evidence

Images shown here are mapped to this tutorial’s required installed-application evidence. Missing captures are labelled explicitly and are not replaced with generic screenshots.

Evidence capture pending
Required screen 1: Imported teaching dataset is open
Step 1: Imported teaching dataset is open
Evidence capture pending
Required screen 2: The prepared project opens with its saved, neatly arranged model diagram
Step 3: The prepared project opens with its saved, neatly arranged model diagram
Evidence capture pending
Required screen 3: Correct method and required settings are visible
Step 6: Correct method and required settings are visible
Evidence capture pending
Required screen 4: Primary FIMIX-PLS result is visible
Step 9: Primary FIMIX-PLS result is visible
Evidence capture pending
Required screen 5: Second important FIMIX-PLS result is visible
Step 9: Second important FIMIX-PLS result is visible

Limitations and related methods

This tutorial verifies the documented workflow and outputs for its supplied teaching fixture. It does not establish numerical identity with another package, replace method literature, or guarantee that the method is suitable for a different study.

Related methods in this family