Description Usage Arguments Details Examples
Binary Logistic Regression: Model Output
1 | model_output_binomial(models, formulas)
|
models |
A list of |
formulas |
A list of model formulas, generated by |
Creates output for results of hierarchical binary logisitic regression models.
1 2 3 4 5 | 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
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