Stepwise forward regression

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

blr_step_p_forward(model, ...)

# Default S3 method
blr_step_p_forward(model, penter = 0.3, details = FALSE, ...)

# S3 method for class 'blr_step_p_forward'
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

details

Logical; if TRUE, will print the regression result at each step.

x

An object of class blr_step_p_forward.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Value

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

model

model with the least AIC; an object of class glm

steps

number of steps

predictors

variables added to the model

aic

akaike information criteria

bic

bayesian information criteria

dev

deviance

indvar

predictors

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: blr_step_aic_backward(), blr_step_aic_both(), blr_step_aic_forward(), blr_step_p_backward()

Examples

if (FALSE) { # \dontrun{
# stepwise forward regression
model <- glm(honcomp ~ female + read + science, data = hsb2,
  family = binomial(link = 'logit'))
blr_step_p_forward(model)

# stepwise forward regression plot
model <- glm(honcomp ~ female + read + science, data = hsb2,
  family = binomial(link = 'logit'))
k <- blr_step_p_forward(model)
plot(k)

# final model
k$model

} # }