ols_step_backward_p: Stepwise backward regression
In cmlopera/olsrr: Tools for Building OLS Regression Models

Description Usage Arguments Value Deprecated Function References See Also Examples

Build regression model from a set of candidate predictor variables by removing predictors based on p values, in a stepwise manner until there is no variable left to remove any more.

ols_step_backward_p(model, ...)

## Default S3 method:
ols_step_backward_p(model, prem = 0.3,
  details = FALSE, ...)

## S3 method for class 'ols_step_backward_p'
plot(x, model = NA, ...)

`model`	An object of class `lm`; the model should include all candidate predictor variables.
`...`	Other inputs.
`prem`	p value; variables with p more than `prem` will be removed from the model.
`details`	Logical; if `TRUE`, will print the regression result at each step.
`x`	An object of class `ols_step_backward_p`.

ols_step_backward_p returns an object of class "ols_step_backward_p". An object of class "ols_step_backward_p" is a list containing the following components:

`model`	final model; an object of class `lm`
`steps`	total number of steps
`removed`	variables removed from the model
`rsquare`	coefficient of determination
`aic`	akaike information criteria
`sbc`	bayesian information criteria
`sbic`	sawa's bayesian information criteria
`adjr`	adjusted r-square
`rmse`	root mean square error
`mallows_cp`	mallow's Cp
`indvar`	predictors

ols_step_backward() has been deprecated. Instead use ols_step_backward_p().

Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

Other variable selection procedures: ols_step_all_possible, ols_step_backward_aic, ols_step_best_subset, ols_step_both_aic, ols_step_forward_aic, ols_step_forward_p

# stepwise backward regression
model <- lm(y ~ ., data = surgical)
ols_step_backward_p(model)

# stepwise backward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_p(model)
plot(k)

# final model
k$model