This example uses Poisson regression to predict the number of awards won by students, based on their programming levels and math scores.
Input
awards
id |
num_awards |
prog |
math |
45 |
0 |
3 |
41 |
108 |
0 |
1 |
41 |
15 |
0 |
3 |
44 |
67 |
0 |
3 |
42 |
... |
... |
... |
... |
196 |
0 |
2 |
49 |
36 |
1 |
1 |
44 |
155 |
1 |
1 |
46 |
6 |
0 |
2 |
46 |
... |
... |
... |
... |
38 |
3 |
2 |
50 |
182 |
0 |
2 |
43 |
115 |
0 |
1 |
43 |
14 |
1 |
2 |
54 |
... |
... |
... |
... |
SQL Call
Because the response variable is a count value (number of awards), the Family is Poisson. The Alpha value, 1, specifies a LASSO model.
SELECT * FROM GLM2 (
ON awards AS InputTable
OUT TABLE ModelTable (glm2_lasso_model)
OUT TABLE RegularizationTable (glm2_regularization)
USING
InputColumns ('prog', 'math')
ResponseColumn ('num_awards')
Family ('POISSON')
Intercept ('TRUE')
NumLambdas (10)
Alpha (1)
) AS dt;
Output
Onscreen Output
dfDevRatio |
devRatio |
dfDev |
deviance |
lambda |
0 |
2.3155999606359e-15 |
199 |
287.672234452864 |
0.522060074545705 |
1 |
0.252835382560055 |
198 |
214.938515003069 |
0.187618662922922 |
1 |
0.285724516586101 |
198 |
205.477224328577 |
0.0674266514397151 |
2 |
0.291092364886125 |
197 |
203.933043413905 |
0.024231882124864 |
2 |
0.292547804908229 |
197 |
203.514353730634 |
0.00870848690800394 |
2 |
0.292738550188869 |
197 |
203.459481609541 |
0.00312966792410483 |
2 |
0.29276365104296 |
197 |
203.452260790758 |
0.0011247443348819 |
2 |
0.29276688972071 |
197 |
203.451329113093 |
0.000404212155898542 |
2 |
0.292767310601548 |
197 |
203.451208037361 |
0.000145266316894411 |
2 |
0.292767359699846 |
197 |
203.451193913144 |
5.22060074545706e-05 |
glm2_lasso_model
category |
information |
value |
A |
FAMILY : POISSON |
|
A |
RESPONSE : num_awards |
|
A |
REGULARIZER : LASSO |
|
A |
CONVERGED : true |
|
A |
NUMOBSERVATIONS |
200 |
B |
has_intercept |
1 |
B |
alpha |
1 |
B |
devnull |
287.672234452865 |
B |
maxNumLambdas |
10 |
B |
maxIterNum |
100000 |
B |
threshold |
1e-07 |
B |
minLambdaRatio |
0.0001 |
B |
iterationNum |
79 |
B |
minLambda |
5.22060074545706e-05 |
C |
intercept |
-5.5766613646 |
C |
math |
0.0861044506 |
C |
prog |
0.1230769507 |
glm2_regularization
df_dev_ratio |
deviance_ratio |
df_dev |
deviance |
lambda |
intercept |
math |
prog |
0 |
2.3155999606359e-15 |
199 |
287.672234452864 |
0.522060074545705 |
-0.4620354596 |
0 |
0 |
1 |
0.252835382560055 |
198 |
214.938515003069 |
0.187618662922922 |
-3.474053956 |
0.0546564845 |
0 |
1 |
0.285724516586101 |
198 |
205.477224328577 |
0.0674266514397151 |
-4.6383122967 |
0.0745575487 |
0 |
2 |
0.291092364886125 |
197 |
203.933043413905 |
0.024231882124864 |
-5.1481338496 |
0.0819282453 |
0.0350038202 |
2 |
0.292547804908229 |
197 |
203.514353730634 |
0.00870848690800394 |
-5.4215383741 |
0.0846018544 |
0.0911403822 |
2 |
0.292738550188869 |
197 |
203.459481609541 |
0.00312966792410483 |
-5.5210823104 |
0.0855658029 |
0.1116709262 |
2 |
0.29276365104296 |
197 |
203.452260790758 |
0.0011247443348819 |
-5.5575725933 |
0.0859216969 |
0.1190959763 |
2 |
0.29276688972071 |
197 |
203.451329113093 |
0.000404212155898542 |
-5.5706478142 |
0.0860486438 |
0.1217705047 |
2 |
0.292767310601548 |
197 |
203.451208037361 |
0.000145266316894411 |
-5.5753457856 |
0.0860942091 |
0.1227324598 |
2 |
0.292767359699846 |
197 |
203.451193913144 |
5.22060074545706e-05 |
-5.5766613646 |
0.0861044506 |
0.1230769507 |