step_bw_p {BiVariAn} | R Documentation |
Automatized stepwise backward for regression models
Description
Automatized stepwise backward for regression models
Usage
step_bw_p(
reg_model,
s_lower = "~1",
s_upper = "all",
trace = TRUE,
steps = NULL,
p_threshold = 0.05,
data = NULL,
...
)
Arguments
reg_model |
Regression model. Must be a glm or lm model |
s_lower |
Lower step. Names of the variables to be included at the lower step. Default is "~1" (Intercept) |
s_upper |
Upper step. Names of the variables to be included at the upper step. Default is "all" (Includes all variables in a dataframe) |
trace |
Trace the steps in R console. Display the output of each iteration. Default is TRUE |
steps |
Maximum number of steps in the process. If NULL, steps will be the length of the regression model introduced. |
p_threshold |
Treshold of p value. Default is 0.05 |
data |
Dataframe to execute the stepwise process. If NULL, data will be assigned from the regression model data. |
... |
Arguments passed to |
Value
An oject class step_bw containing the final model an each step performed in backward regression. The final model can be accessed using $ operator
References
Efroymson MA. Multiple regression analysis. In: Ralston A, Wilf HS, editors. Mathematical methods for digital computers. New York: Wiley; 1960.
Examples
data(mtcars)
regression_model<-lm(cyl~., data=mtcars)
stepwise<-step_bw_p(regression_model, trace=FALSE)
final_stepwise_model<-stepwise$final_model
summary(final_stepwise_model)