Input
- Input: admissions_test, which has admissions information for 20 students
- Model: glm_admissions_model, output by GLM Example: Logistic Regression Analysis with Intercept
id | masters | gpa | stats | programming | admitted |
---|---|---|---|---|---|
50 | yes | 3.95 | Beginner | Beginner | 0 |
51 | yes | 3.76 | Beginner | Beginner | 0 |
52 | no | 3.7 | Novice | Beginner | 1 |
53 | yes | 3.5 | Beginner | Novice | 1 |
54 | yes | 3.5 | Beginner | Advanced | 1 |
55 | no | 3.6 | Beginner | Advanced | 1 |
56 | no | 3.82 | Advanced | Advanced | 1 |
57 | no | 3.71 | Advanced | Advanced | 1 |
58 | no | 3.13 | Advanced | Advanced | 1 |
59 | no | 3.65 | Novice | Novice | 1 |
60 | no | 4 | Advanced | Novice | 1 |
61 | yes | 4 | Advanced | Advanced | 1 |
62 | no | 3.7 | Advanced | Advanced | 1 |
63 | no | 3.83 | Advanced | Advanced | 1 |
64 | yes | 3.81 | Advanced | Advanced | 1 |
65 | yes | 3.9 | Advanced | Advanced | 1 |
66 | no | 3.87 | Novice | Beginner | 1 |
67 | yes | 3.46 | Novice | Beginner | 0 |
68 | no | 1.87 | Advanced | Novice | 1 |
69 | no | 3.96 | Advanced | Advanced | 1 |
SQL Call
CREATE MULTISET TABLE glmpredict_admissions AS ( SELECT * FROM GLMPredict_MLE ( ON admissions_test PARTITION BY ANY ON glm_admissions_model AS Model DIMENSION USING Accumulate ('id', 'masters', 'gpa', 'stats', 'programming', 'admitted') Family ('LOGISTIC') LinkFunction ('LOGIT') OutputProb ('t') ) AS dt ) WITH DATA;
Output
SELECT * FROM glmpredict_admissions ORDER BY 1;
id masters gpa stats programming admitted fitted_value prediction prob -- ------- ---- -------- ----------- -------- ------------------ ---------- ------------------ 50 yes 3.95 beginner beginner 0 0.3507656829365149 0 0.6492343170634851 51 yes 3.76 beginner beginner 0 0.3557112671780229 0 0.6442887328219771 52 no 3.7 novice beginner 1 0.7583079903427496 1 0.7583079903427496 53 yes 3.5 beginner novice 1 0.5560152436940663 1 0.5560152436940663 54 yes 3.5 beginner advanced 1 0.7694761266019933 1 0.7694761266019933 55 no 3.6 beginner advanced 1 0.9680314543695169 1 0.9680314543695169 56 no 3.82 advanced advanced 1 0.9457732442937538 1 0.9457732442937538 57 no 3.71 advanced advanced 1 0.9464124273756033 1 0.9464124273756033 58 no 3.13 advanced advanced 1 0.949666669516694 1 0.949666669516694 59 no 3.65 novice novice 1 0.8741907950304955 1 0.8741907950304955 60 no 4.0 advanced novice 1 0.8650601698708184 1 0.8650601698708184 61 yes 4.0 advanced advanced 1 0.6506209990774642 1 0.6506209990774642 62 no 3.7 advanced advanced 1 0.9464701812067841 1 0.9464701812067841 63 no 3.83 advanced advanced 1 0.9457147816580886 1 0.9457147816580886 64 yes 3.81 advanced advanced 1 0.6555256147282206 1 0.6555256147282206 65 yes 3.9 advanced advanced 1 0.6532064285302687 1 0.6532064285302687 66 no 3.87 novice beginner 1 0.7547403697792542 1 0.7547403697792542 67 yes 3.46 novice beginner 0 0.2600362186838019 0 0.7399637813161981 68 no 1.87 advanced novice 1 0.8909664987530864 1 0.8909664987530864 69 no 3.96 advanced advanced 1 0.9449493421287761 1 0.9449493421287761
Download a zip file of all examples and a SQL script file that creates their input tables from the attachment in the left sidebar.