ols_step_forward_p: Stepwise forward regression

In olsrr: Tools for Building OLS Regression Models

Description

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

Usage

ols_step_forward_p(model, ...)

## Default S3 method:
ols_step_forward_p(model, penter = 0.3,
  progress = FALSE, details = FALSE, ...)

## S3 method for class 'ols_step_forward_p'
plot(x, model = NA, print_plot = TRUE,
  ...)

Arguments

model	An object of class lm; the model should include all candidate predictor variables.
...	Other arguments.
penter	p value; variables with p value less than penter will enter into the model
progress	Logical; if TRUE, will display variable selection progress.
details	Logical; if TRUE, will print the regression result at each step.
x	An object of class ols_step_forward_p.
print_plot	logical; if TRUE, prints the plot else returns a plot object.

Value

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

model	final model; an object of class lm
steps	number of steps
predictors	variables added to 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

Deprecated Function

ols_step_forward() has been deprecated. Instead use ols_step_forward_p().

References

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

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

See Also

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

Examples

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

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

# final model
k$model

olsrr documentation built on Feb. 10, 2020, 5:07 p.m.