model_output_binomial: Binary Logistic Regression: Model Output
In AutoModel: Automated Hierarchical Multiple Regression with Assumptions Checking

Description Usage Arguments Details Examples

Binary Logistic Regression: Model Output

1	model_output_binomial(models, formulas)

`models`	A list of `lm` model objects. A set of model objects created by `create_model_object`.
`formulas`	A list of model formulas, generated by `create_formula_objects`.

Creates output for results of hierarchical binary logisitic regression models.

formulas <- create_formula_objects("am", c("hp", "mpg"), c("disp"),
c("drat"))
mtcars_models <- create_model_objects(formulas, data=mtcars,
type="binomial")
model_output_binomial(mtcars_models, formulas)

Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred 

Model Summary Table: Pseudo R^2

        McFadden's Adj McFadden's Cox-Snell Nagelkerke
Model 1  0.5551082      0.3700504 0.5275918  0.7119993
Model 2  0.7652506      0.5339283 0.6443468  0.8695633
Model 3  0.8197844      0.5421977 0.6696064  0.9036518

Model Coefficient Table

  model_terms    coefs      SE   wald  p.value  coefs.1    SE.1 wald.1
1 (Intercept) -33.6052 15.0767 4.9682 0.0258 * -33.8128 24.1753 1.9562
2          hp    0.055  0.0269 4.1821 0.0409 *   0.1494  0.0787 3.6009
3         mpg   1.2596  0.5675 4.9271 0.0264 *    1.285  0.8989 2.0433
4        disp       --      --     --       --  -0.0654   0.043 2.3115
5        drat       --      --     --       --       --      --     --
  p.value.1  coefs.2    SE.2 wald.2 p.value.2
1  0.1619   -86.9867 73.1174 1.4154  0.2342  
2  0.0577 .   0.1501  0.1424 1.1108  0.2919  
3  0.1529     1.2298  1.0823 1.2911  0.2558  
4  0.1284    -0.0593  0.0759 0.6105  0.4346  
5        --  13.8066 14.9591 0.8519   0.356  

Classification Table

       Actual
Predict  0  1
      0 18  0
      1  1 13
Specificity:  0.9285714 
Sensitivity:  1 
Total Accuracy:  0.96875 
Model 1 : am ~ hp + mpg 
Model 2 : am ~ hp + mpg + disp 
Model 3 : am ~ hp + mpg + disp + drat