classification_table: Binary Logistic Regression: Classification Table
In alishinski/AutoModel: Automated Hierarchical Multiple Regression with Assumptions Checking
Description
Usage
Arguments
Details
Examples
Description
Binary Logistic Regression: Classification Table
Usage
1
classification_table(model, response)
Arguments
model
A binary logistic regression model object.
response
The dependent variable in model.
Details
Creates classification table for binary logistic regresison model
using optimal cut point for accuracy.
Examples
1
2
3
4
5
6
formulas <- create_formula_objects("am", c("hp", "mpg"), c("disp"),
c("drat"))
mtcars_models <- create_model_objects(formulas, data=mtcars,
type="binomial")
last_model <- mtcars_models[[length(mtcars_models)]]
classification_table(last_model, last_model$model[,1])
alishinski/AutoModel documentation built on May 12, 2019, 5:37 a.m.
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Description Usage Arguments Details Examples
Description
Binary Logistic Regression: Classification Table
Usage
1 | classification_table(model, response)
|
Arguments
model |
A binary logistic regression model object. |
response |
The dependent variable in |
Details
Creates classification table for binary logistic regresison model using optimal cut point for accuracy.
Examples
1 2 3 4 5 6 | formulas <- create_formula_objects("am", c("hp", "mpg"), c("disp"),
c("drat"))
mtcars_models <- create_model_objects(formulas, data=mtcars,
type="binomial")
last_model <- mtcars_models[[length(mtcars_models)]]
classification_table(last_model, last_model$model[,1])
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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